1e:I[23489,["/_next/static/immutable/chunks/0cmb0ng968nlp.js","/_next/static/immutable/chunks/1c7dg80qt12uw.js","/_next/static/immutable/chunks/2bmdwv49ba1qx.js","/_next/static/immutable/chunks/3ld61_1ghpsxn.js"],"ShareMenu"] 1f:I[55078,["/_next/static/immutable/chunks/0cmb0ng968nlp.js","/_next/static/immutable/chunks/1c7dg80qt12uw.js","/_next/static/immutable/chunks/2bmdwv49ba1qx.js","/_next/static/immutable/chunks/3ld61_1ghpsxn.js"],"AuthorMetricsPanel"] 20:I[39060,["/_next/static/immutable/chunks/0cmb0ng968nlp.js","/_next/static/immutable/chunks/1c7dg80qt12uw.js","/_next/static/immutable/chunks/2bmdwv49ba1qx.js","/_next/static/immutable/chunks/3ld61_1ghpsxn.js"],"AuthorPapers"] 48:I[49310,["/_next/static/immutable/chunks/0cmb0ng968nlp.js","/_next/static/immutable/chunks/1c7dg80qt12uw.js"],"IconMark"] 21:T51e,With the severe spectrum shortage in conventional cellular bands, millimeter wave (mmW) frequencies between 30 and 300 GHz have been attracting growing attention as a possible candidate for next-generation micro- and picocellular wireless networks. The mmW bands offer orders of magnitude greater spectrum than current cellular allocations and enable very high-dimensional antenna arrays for further gains via beamforming and spatial multiplexing. This paper uses recent real-world measurements at 28 and 73 GHz in New York, NY, USA, to derive detailed spatial statistical models of the channels and uses these models to provide a realistic assessment of mmW micro- and picocellular networks in a dense urban deployment. Statistical models are derived for key channel parameters, including the path loss, number of spatial clusters, angular dispersion, and outage. It is found that, even in highly non-line-of-sight environments, strong signals can be detected 100-200 m from potential cell sites, potentially with multiple clusters to support spatial multiplexing. Moreover, a system simulation based on the models predicts that mmW systems can offer an order of magnitude increase in capacity over current state-of-the-art 4G cellular networks with no increase in cell density from current urban deployments.22:T726,The relatively unused millimeter-wave (mmWave) spectrum offers excellent opportunities to increase mobile capacity due to the enormous amount of available raw bandwidth. This paper presents experimental measurements and empirically-based propagation channel models for the 28, 38, 60, and 73 GHz mmWave bands, using a wideband sliding correlator channel sounder with steerable directional horn antennas at both the transmitter and receiver from 2011 to 2013. More than 15,000 power delay profiles were measured across the mmWave bands to yield directional and omnidirectional path loss models, temporal and spatial channel models, and outage probabilities. Models presented here offer side-by-side comparisons of propagation characteristics over a wide range of mmWave bands, and the results and models are useful for the research and standardization process of future mmWave systems. Directional and omnidirectional path loss models with respect to a 1 m close-in free space reference distance over a wide range of mmWave frequencies and scenarios using directional antennas in real-world environments are provided herein, and are shown to simplify mmWave path loss models, while allowing researchers to globally compare and standardize path loss parameters for emerging mmWave wireless networks. A new channel impulse response modeling framework, shown to agree with extensive mmWave measurements over several bands, is presented for use in link-layer simulations, using the observed fact that spatial lobes contain multipath energy that arrives at many different propagation time intervals. The results presented here may assist researchers in analyzing and simulating the performance of next-generation mmWave wireless networks that will rely on adaptive antennas and multiple-input and multiple-output (MIMO) antenna systems.23:T667,Wireless data traffic is projected to skyrocket 10 000 fold within the next 20 years. To tackle this incredible increase in wireless data traffic, a first approach is to further improve spectrally efficient systems such as 4G LTE in bands below 6 GHz by using more advanced spectral efficiency techniques. However, the required substantial increase in system complexity along with fundamental limits on hardware implementation and channel conditions may limit the viability of this approach. Furthermore, the end result would be an extremely spectrally efficient system with little room for future improvement to meet the ever-growing wireless data usage. The second approach is to move up in frequency, into an unused nontraditional spectrum where enormous bandwidths are available, such as at millimeter wave (mmWave). The mmWave option enables the use of simple air interfaces since large bandwidths can be exploited (e.g., 2 GHz) to achieve high data rates rather than relying on highly complex techniques originally aimed at achieving a high spectral efficiency with smaller bandwidths. In addition, mmWave systems will easily evolve to even higher system capacities, because there will be plenty of margin to improve the spectral efficiency as data demands further increase. In this paper, a case is made for using mmWave for a fifth generation (5G) wireless system for ultradense networks by presenting an overview of enhanced local area (eLA) technology at mmWave with emphasis on 5G requirements, spectrum considerations, propagation and channel modeling, air-interface and multiantenna design, and network architecture solutions.24:T836,Ultra-wideband millimeter-wave (mmWave) propagation measurements were conducted in the 28- and 73-GHz frequency bands in a typical indoor office environment in downtown Brooklyn, New York, on the campus of New York University. The measurements provide large-scale path loss and temporal statistics that will be useful for ultra-dense indoor wireless networks for future mmWave bands. This paper presents the details of measurements that employed a 400 Megachips-per-second broadband sliding correlator channel sounder, using rotatable highly directional horn antennas for both co-polarized and cross-polarized antenna configurations. The measurement environment was a closed-plan in-building scenario that included a line-of-sight and non-line-of-sight corridor, a hallway, a cubicle farm, and adjacent-room communication links. Well-known and new single-frequency and multi-frequency directional and omnidirectional large-scale path loss models are presented and evaluated based on more than 14 000 directional power delay profiles acquired from unique transmitter and receiver antenna pointing angle combinations. Omnidirectional path loss models, synthesized from the directional measurements, are provided for the case of arbitrary polarization coupling, as well as for the specific cases of co-polarized and cross-polarized antenna orientations. The results show that novel large-scale path loss models provided here are simpler and more physically based compared to previous 3GPP and ITU indoor propagation models that require more model parameters and offer very little additional accuracy and lack a physical basis. Multipath time dispersion statistics for mmWave systems using directional antennas are presented for co-polarization, crosspolarization, and combined-polarization scenarios, and show that the multipath root mean square delay spread can be reduced when using transmitter and receiver antenna pointing angles that result in the strongest received power. Raw omnidirectional path loss data and closed-form optimization formulas for all path loss models are given in the Appendices.25:T66b,This paper compares three candidate large-scale propagation path loss models for use over the entire microwave and millimeter-wave (mmWave) radio spectrum: the alpha-beta-gamma (ABG) model, the close-in (CI) free space reference distance model, and the CI model with a frequency-weighted path loss exponent (CIF). Each of these models have been recently studied for use in standards bodies such as 3GPP, and for use in the design of fifth generation (5G) wireless systems in urban macrocell, urban microcell, and indoor office and shopping mall scenarios. Here we compare the accuracy and sensitivity of these models using measured data from 30 propagation measurement datasets from 2 GHz to 73 GHz over distances ranging from 4 m to 1238 m. A series of sensitivity analyses of the three models show that the physically-based two-parameter CI model and three-parameter CIF model offer computational simplicity, have very similar goodness of fit (i.e., the shadow fading standard deviation), exhibit more stable model parameter behavior across frequencies and distances, and yield smaller prediction error in sensitivity testing across distances and frequencies, when compared to the four-parameter ABG model. Results show the CI model with a 1 m close-in reference distance is suitable for outdoor environments, while the CIF model is more appropriate for indoor modeling. The CI and CIF models are easily implemented in existing 3GPP models by making a very subtle modification -- by replacing a floating non-physically based constant with a frequency-dependent constant that represents free space path loss in the first meter of propagation.26:T4c9,In this paper, we present reflection coefficients and penetration losses for common building materials at 28 GHz for the design and deployment of future millimeter wave mobile communication networks. Reflections from walls and buildings and penetration losses were measured for indoor and outdoor materials, such as tinted glass, clear glass, brick, concrete, and drywall at 28 GHz in New York City. A 400 Mega-chip-per-second sliding correlator channel sounder and 24.5 dBi steerable horn antennas were used to emulate future mobile devices with adaptive antennas that will likely be used in future millimeter wave cellular systems [1]. Measurements in and around buildings show that outdoor building materials are excellent reflectors with the largest measured reflection coefficient of 0.896 for tinted glass as compared to indoor building materials that are less reflective. We also found that penetration loss is dependent not only on the number of obstructions and distance between transmitter and receiver, but also on the surrounding environment. The greatest penetration loss containing three interior walls of an office building was found to be 45.1 dB, with 11.39 m separation between the transmitter and receiver.27:T490,This paper presents and compares two candidate large-scale propagation path loss models, the alpha-beta-gamma (ABG) model and the close-in (CI) free space reference distance model, for the design of fifth generation (5G) wireless communication systems in urban micro- and macro-cellular scenarios. Comparisons are made using the data obtained from 20 propagation measurement campaigns or ray- tracing studies from 2 GHz to 73.5 GHz over distances ranging from 5 m to 1429 m. The results show that the one-parameter CI model has a very similar goodness of fit (i.e., the shadow fading standard deviation) in both line-of-sight and non-line-of-sight environments, while offering substantial simplicity and more stable behavior across frequencies and distances, as compared to the three-parameter ABG model. Additionally, the CI model needs only one very subtle and simple modification to the existing 3GPP floating-intercept path loss model (replacing a constant with a close-in free space reference value) in order to provide greater simulation accuracy, more simplicity, better repeatability across experiments, and higher stability across a vast range of frequencies.28:T4a6,This paper presents details and applications of a novel channel simulation software named NYUSIM, which can be used to generate realistic temporal and spatial channel responses to support realistic physical-and link-layer simulations and design for fifth-generation (5G) cellular communications. NYUSIM is built upon the statistical spatial channel model for broadband millimeter-wave (mmWave) wireless communication systems developed by researchers at New York University (NYU). The simulator is applicable for a wide range of carrier frequencies (500 MHz to 100 GHz), radio frequency (RF) bandwidths (0 to 800 MHz), antenna beamwidths (7° to 360° for azimuth and 7° to 45° for elevation), and operating scenarios (urban microcell, urban macrocell, and rural macrocell), and also incorporates multiple-input multiple-output (MIMO) antenna arrays at the transmitter and receiver. This paper also provides examples to demonstrate how to use NYUSIM for analyzing MIMO channel conditions and spectral efficiencies, which show that NYUSIM is an alternative and more realistic channel model compared to the 3rd Generation Partnership Project (3GPP) and other channel models for mmWave bands.29:T659,Fifth-generation (5G) wireless networks are expected to operate at both microwave and millimeter-wave (mmWave) frequency bands, including frequencies in the range of 24 to 86 GHz. Radio propagation models are used to help engineers design, deploy, and compare candidate wireless technologies, and have a profound impact on the decisions of almost every aspect of wireless communications. This paper provides a comprehensive overview of the channel models that will likely be used in the design of 5G radio systems. We start with a discussion on the framework of channel models, which consists of classical models of path loss versus distance, large-scale, and small-scale fading models, and multiple-input multiple-output channel models. Then, key differences between mmWave and microwave channel models are presented, and two popular mmWave channel models are discussed: the 3rd Generation Partnership Project model, which is adopted by the International Telecommunication Union, and the NYUSIM model, which was developed from several years of field measurements in New York City. Examples on how to apply the channel models are then given for several diverse applications demonstrating the wide impact of the models and their parameter values, where the performance comparisons of the channel models are done with promising hybrid beamforming approaches, including leveraging coordinated multipoint transmission. These results show that the answers to channel performance metrics, such as spectrum efficiency, coverage, hardware/signal processing requirements, etc., are extremely sensitive to the choice of channel models.2a:T4cc,This paper presents small-scale fading measurements for 28 GHz outdoor millimeter-wave ultrawideband channels using directional horn antennas at the transmitter and receiver. Power delay profiles were measured at half-wavelength spatial increments over a local area (33 wavelengths) on a linear track in two orthogonal receiver directions in a typical base-to-mobile scenario with fixed transmitter and receiver antenna beam pointing directions. The voltage path amplitudes are shown to follow a Rician distribution, with K-factor ranging from 9 - 15 dB and 5 - 8 dB in line of sight (LOS) and non-line of sight (NLOS) for a vertical-to-vertical co- polarized antenna scenario, respectively, and from 3 - 7 dB in both LOS and NLOS vertical-to- horizontal cross-polarized antenna scenario. The average spatial autocorrelation functions of individual multipath components reveal that signal amplitudes reach a correlation of 0 after 2 and 5 wavelengths in LOS and NLOS co-polarized V-V antenna scenarios. The models provided are useful for recreating path gain statistics of millimeter- wave wideband channel impulse responses over local areas, for the study of multi-element antenna simulations and channel estimation algorithms.2b:T5c0,Propagation measurements at 28 GHz were conducted in outdoor urban environments in New York City using four different transmitter locations and 83 receiver locations with distances of up to 500 m. A 400 mega- chip per second channel sounder with steerable 24.5 dBi horn antennas at the transmitter and receiver was used to measure the angular distributions of received multipath power over a wide range of propagation distances and urban settings. Measurements were also made to study the small-scale fading of closely-spaced power delay profiles recorded at half-wavelength (5.35 mm) increments along a small-scale linear track (10 wavelengths, or 107 mm) at two different receiver locations. Our measurements indicate that power levels for small- scale fading do not significantly fluctuate from the mean power level at a fixed angle of arrival. We propose here a new lobe modeling technique that can be used to create a statistical channel model for lobe path loss and shadow fading, and we provide many model statistics as a function of transmitter- receiver separation distance. Our work shows that New York City is a multipath-rich environment when using highly directional steerable horn antennas, and that an average of 2.5 signal lobes exists at any receiver location, where each lobe has an average total angle spread of 40.3° and an RMS angle spread of 7.8°. This work aims to create a 28 GHz statistical spatial channel model for future 5G cellular networks.2c:T4ad,This paper presents 73 GHz human blockage measurements for a point-to-point link with a 5 m transmitter-receiver separation distance in an indoor environment, with a human that walked at a speed of approximately 1 m/s at a perpendicular orientation to the line between the transmitter and receiver, at various distances between them. The experiment measures the shadowing effect of a moving human body when using directional antennas at the transmitter and receiver for millimeter- wave radio communications. The measurements were conducted using a 500 Megachips-per-second wideband correlator channel sounder with a 1 GHz first null-to-null RF bandwidth. Results indicate high shadowing attenuation is not just due to the human blocker but also is due to the static directional nature of the antennas used, leading to the need for phased-array antennas to switch beam directions in the presence of obstructions and blockages at millimeter-waves. A simple model for human blockage is provided based on the double knife-edge diffraction (DKED) model where humans are approximated by a rectangular screen with infinite vertical height, similar to the human blockage model given by the METIS project.2d:T5da,This paper studies radio propagation mechanisms that impact handoffs, air interface design, beam steering, and multiple-input multiple-output for 5G mobile communication systems. Knife-edge diffraction (KED) and a creeping wave linear model are shown to predict diffraction loss around typical building objects from 10 to 26 GHz, and human blockage measurements at 73 GHz are shown to fit a double KED model, which incorporates antenna gains. Small-scale spatial fading of millimeter wave (mmWave)-received signal voltage amplitude is generally Ricean-distributed for both omnidirectional and directional receive antenna patterns under both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions in most cases, although the log-normal distribution fits measured data better for the omnidirectional receive antenna pattern in the NLOS environment. Small-scale spatial autocorrelations of received voltage amplitudes are shown to fit sinusoidal exponential and exponential functions for LOS and NLOS environments, respectively, with small decorrelation distances of 0.27-13.6 cm (smaller than the size of a handset) that are favorable for spatial multiplexing. Local area measurements using cluster and route scenarios show how the received signal changes as the mobile moves and transitions from LOS to NLOS locations, with reasonably stationary signal levels within clusters. Wideband mmWave power levels are shown to fade from 0.4 dB/ms to 40 dB/s, depending on travel speed and surroundings.2e:T58b,This paper presents millimeter-wave propagation measurements for urban micro-cellular and indoor office scenarios at 28 GHz and 73 GHz, and investigates the corresponding path loss using five types of path loss models, the single-frequency floating-intercept (FI) model, single-frequency close-in (CI) free space reference distance model, multi-frequency alpha-beta-gamma (ABG) model, multi-frequency CI model, and multi-frequency CI model with a frequency-weighted path loss exponent (CIF), in both line-of-sight and non-line-of-sight environments. Results show that the CI and CIF models provide good estimation and exhibit stable behavior over frequencies and distances, with a solid physical basis and less computational complexity when compared with the FI and ABG models. Furthermore, path loss in outdoor scenarios shows little dependence on frequency beyond the first meter of free space propagation, whereas path loss tends to increase with frequency in addition to the increased free space path loss in indoor environments. Therefore, the CI model is suitable for outdoor environments over multiple frequencies, while the CIF model is more appropriate for indoor modeling. This work shows that both the CI and CIF models use fewer parameters and offer more convenient closed-form expressions suitable for analysis, without compromising model accuracy when compared to current 3GPP and WINNER path loss models.2f:T5da,Massive MIMO, especially in the millimeter- wave frequency bands, has been recognized as a promising technique to enhance spectrum and energy efficiency, as well as network coverage for wireless communications. Most research in massive MIMO just uses the extended conventional MIMO channel model by directly assuming that the channel dimensionality becomes large. With massive numbers of antennas, however, there exists a non-negligible propagation delay across the large array aperture, which then causes a transmitted symbol to reach different antennas with different delays, thereby rendering conventional MIMO channel models inapplicable. Such a phenomenon is known as the spatial-wideband effect in the areas of array signal processing and radar signal processing, and introduces the beam squint effect in beamforming. However, the spatial-wideband effect and the related beam squint issue are seldom studied in massive MIMO communications. To design a practical massive MIMO system, it is important to understand when the spatial-wideband effect appears and how it affects signal transmission, how the spatial-wideband effect interacts with the frequency-wideband effect (frequency selectivity), especially for multi-carrier modulations such as orthogonal frequency- division multiplexing (OFDM), and how we should re-design the transceiver. In this article we suggest a new massive MIMO channel model that embraces both the spatial- and frequency- wideband effects, and discuss these issues.30:T786,Future mobile communications systems are likely to be very different to those of today with new service innovations driven by increasing data traffic demand, increasing processing power of smart devices and new innovative applications. To meet these service demands the telecommunications industry is converging on a common set of 5G requirements which includes network speeds as high as 10 Gbps, cell edge rate greater than 100 Mbps, and latency of less than 1 msec. To reach these 5G requirements the industry is looking at new spectrum bands in the range up to 100 GHz where there is spectrum availability for wide bandwidth channels. For the development of new 5G systems to operate in bands up to 100 GHz there is a need for accurate radio propagation models which are not addressed by existing channel models developed for bands below 6 GHz. This paper presents a preliminary overview of the 5G channel models for bands up to 100 GHz in indoor offices and shopping malls, derived from extensive measurements across a multitude of bands. These studies have found some extensibility of the existing 3GPP models (e.g. 3GPP TR36.873) to the higher frequency bands up to 100 GHz. The measurements indicate that the smaller wavelengths introduce an increased sensitivity of the propagation models to the scale of the environment and show some frequency dependence of the path loss as well as increased occurrence of blockage. Further, the penetration loss is highly dependent on the material and tends to increase with frequency. The small-scale characteristics of the channel such as delay spread and angular spread and the multipath richness is somewhat similar over the frequency range, which is encouraging for extending the existing 3GPP models to the wider frequency range. Further work will be carried out to complete these models, but this paper presents the first steps for an initial basis for the model development.31:T531,Channel models describe how wireless channel parameters behave in a given scenario, and help evaluate link- and system-level performance. A proper channel model should be able to faithfully reproduce the channel parameters obtained in field measurements and accurately predict the spatial and temporal channel impulse response along with large-scale fading. This paper compares two popular channel models for next generation wireless communications: the 3rd Generation Partnership Project (3GPP) TR 38.900 Release 14 channel model and the statistical spatial channel model NYUSIM developed by New York University (NYU). The two channel models employ different modeling approaches in many aspects, such as the line-of-sight probability, path loss, and clustering methodology. Simulations are performed using the two channel models to analyze the channel eigenvalue distribution and spectral efficiency leveraging the analog/digital hybrid beamforming methods found in the literature. Simulation results show that the 3GPP model produces different eigenvalue and spectral efficiency distributions for mmWave bands, as compared to the outcome from NYUSIM that is based on massive amounts of real-world measured data in New York City. This work shows NYUSIM is more accurate for realistic simulations than 3GPP in urban environments.32:T5bd,Omnidirectional path loss models are vital for radiosystem design in wireless communication systems, as they allow engineers to perform network simulations for systems with arbitrary antenna patterns. At millimeter-wave frequencies, channel measurements are frequently conducted using steerable highgain directional antennas at both the transmitter and receiver to make up for the significant increase in free space path loss at these frequencies compared to traditional cellular systems that operate at lower frequencies. The omnidirectional antenna pattern, and resulting omnidirectional received power must therefore be synthesized from many unique pointing angles, where the transmit and receive antennas are rotated over many different azimuth and elevation planes. In this paper, the equivalent omnidirectional antenna pattern and omnidirectional received power are synthesized by summing the received powers from all measured unique pointing angles obtained at antenna halfpower beamwidth step increments in the azimuth and elevation planes, and this method is validated by demonstrating that the synthesized omnidirectional received power and path loss are independent of antenna beamwidth, through theoretical analyses and millimeter-wave propagation measurements using antennas with different beamwidths. The method in this paper is shown to provide accurate results while enhancing the measurement range substantially through the use of directional antennas.33:T435,As the mobile cellular carriers are currently facing a spectrum crunch, researchers are concentrating on higher carrier frequency bands, such as E-band (71-76 GHz and 81-86 GHz) for the next generation wireless communication systems. The E-band is promising due to its large available, continuous bandwidth and robust weather resilience. In this paper, we demonstrate a spread spectrum sliding correlator channel sounder operating at a center frequency of 73.5 GHz with an 800 MHz null-to-null bandwidth. The channel sounder provides a multipath time resolution of 2.33 ns. 72 GHz millimeter wave propagation and penetration characteristics in an indoor office environment are investigated using the sliding correlator channel sounding system. Data collected and processed from the measurements shows that strong received power can be achieved from the multipath-rich indoor environment, in the presence of multiple obstructions. The data obtained from this measurement campaign may be utilized for the design of future fifth generation millimeter wave indoor cellular systems.34:T60b,In this paper, we consider a cooperative communication network where multiple low-Earth-orbit (LEO) satellites provide services to multiple ground users (GUs) cooperatively at the same time and on the same frequency. The multi-satellite cooperation has great potential in extending communication coverage and increasing spectral efficiency. Considering that the on-board radio-frequency circuit resources and computation resources on each satellite are restricted, we aim to propose a low-complexity yet efficient multi-satellite cooperative transmission framework. Specifically, we first propose a hybrid beamforming method consisting of analog beamforming for beam alignment and digital beamforming for interference mitigation. Then, to establish appropriate connections between the satellites and GUs, we propose a heuristic user scheduling algorithm which determines the connections according to the total spectral efficiency increment of the multi-satellite cooperative network. Next, considering the intrinsic connection between beamforming and user scheduling, a joint hybrid beamforming and user scheduling (JHU) scheme is proposed to dramatically improve the performance of the multi-satellite cooperative network. In addition to the single-connection scenario, we also consider the multi-connection case using the JHU scheme. Extensive simulations conducted over different LEO satellite constellations and across various GU locations demonstrate the superiority of the proposed schemes in both overall and per-user spectral efficiencies.35:T59a,Multiple-input multiple-output (MIMO) systems are well suited for millimeter-wave (mmWave) wireless communications where large antenna arrays can be integrated in small form factors due to tiny wavelengths, thereby providing high array gains while supporting spatial multiplexing, beamforming, or antenna diversity. It has been shown that mmWave channels exhibit sparsity due to the limited number of dominant propagation paths, thus compressed sensing techniques can be leveraged to conduct channel estimation at mmWave frequencies. This paper presents a novel approach of constructing beamforming dictionary matrices for sparse channel estimation using the continuous basis pursuit (CBP) concept, and proposes two novel low-complexity algorithms to exploit channel sparsity for adaptively estimating multipath channel parameters in mmWave channels. We verify the performance of the proposed CBP-based beamforming dictionary and the two algorithms using a simulator built upon a three-dimensional mmWave statistical spatial channel model, NYUSIM, that is based on real-world propagation measurements. Simulation results show that the CBPbased dictionary offers substantially higher estimation accuracy and greater spectral efficiency than the grid-based counterpart introduced by previous researchers, and the algorithms proposed here render better performance but require less computational effort compared with existing algorithms.36:T4d6,In wireless systems aided by reconfigurable intelligent surfaces (RISs), channel state information plays a pivotal role in achieving the performance gain of RISs. Mobility renders accurate channel estimation (CE) more challenging due to the Doppler effect. In this letter, we propose two practical wideband CE schemes incorporating Doppler shift adjustment (DSA) for multi-path and single-path propagation environments, respectively, for RIS-assisted communication with passive reflecting elements. For the multi-path scenario, ordinary CE is first executed assuming quasi-static channels, followed by DSA realized via joint RIS reflection pattern selection and transformations between frequency and time domains. The proposed CE necessitates only one more symbol incurring negligible extra overhead compared with the number of symbols required for the original CE. For the single-path case which is especially applicable to millimeter-wave and terahertz systems, a novel low-complexity CE method is devised capitalizing on the form of the array factors at the RIS. Simulation results demonstrate that the proposed algorithms yield high CE accuracy and achievable rate with low complexity, and outperform representative benchmark schemes.37:T4c8,Multi-cell wireless systems usually suffer both intra-cell and inter-cell interference, which can be mitigated via coordinated multipoint (CoMP) techniques. Previous works on multi-cell analysis for the microwave band generally consider fully digital beamforming that requires a complete radio-frequency chain behind each antenna, which is less practical for millimeter-wave (mmWave) systems where large amounts of antennas are necessary to provide sufficient beamforming gain and to enable transmission and reception of multiple data streams per user. This paper proposes four analog and digital hybrid beamforming schemes for multi-cell multi-user multi-stream mmWave communication, leveraging CoMP. Spectral efficiency performances of the proposed hybrid beamforming approaches are investigated and compared using both the 3rd Generation Partnership Project and NYUSIM channel models. Simulation results show that CoMP based on maximizing signal-to-leakage-plus-noise ratio can improve spectral efficiency as compared to the no-coordination case, and spectral efficiency gaps between different beamforming approaches depend on the interference level that is influenced by the cell radius and the number of users per cell.38:T5c5,Integrated ultra-massive multiple-input multiple-output (UM-MIMO) and intelligent reflecting surface (IRS) systems are promising for 6G and beyond Terahertz (0.1-10 THz) communications, to effectively bypass the barriers of limited coverage and line-of-sight blockage. However, excessive dimensions of UM-MIMO and IRS enlarge the near-field region, while strong THz channel sparsity in the far-field is detrimental to spatial multiplexing. Moreover, channel estimation (CE) requires recovering the large-scale channel from severely compressed observations due to limited RF-chains. To tackle these challenges, a hybrid spherical- and planar-wave channel model (HSPM) is introduced for the cascaded channel of the integrated system. The spatial multiplexing gains under near-field and far-field regions are analyzed, which are found to be limited by the segmented channel with a lower rank. Furthermore, a compressive sensing-based CE framework is developed, including a sparse channel representation method, a separate-side estimation (SSE) and a dictionary-shrinkage estimation (DSE) algorithms. Numerical results verify the effectiveness of the HSPM, the capacity of which is only$5\times 10^{-4}$bits/s/Hz deviated from that obtained by the ground-truth spherical-wave-model, with 256 elements. While the SSE achieves improved accuracy for CE than benchmark algorithms, the DSE is more attractive in noisy environments, with 1 dB lower normalized-mean-square error than SSE.39:T686,Multiple-input multiple-output (MIMO) spatial multiplexing and beamforming are regarded as key technology enablers for the fifth-generation (5G) millimeter wave (mmWave) mobile radio services. Spatial multiplexing requires sufficiently separated and incoherent antenna array elements, while in the case of beamforming, the antenna array elements need to be coherent and closely spaced. Extensive 28-, 60-, and 73-GHz ultrawideband propagation measurements in cities of New York City and Austin have indicated formation of two or more spatial lobes for the angles-of-departure and angles-of-arrival even for line-of-sight (LOS) transmission, which is an advantageous feature of mmWave channels, indicating that the transmitting and receiving array antenna elements can be co-located, thus enabling a single architecture for both spatial multiplexing and beamforming. In this paper, a two-level beamforming architecture for uniform linear arrays is proposed that leverages the formation of these spatial lobes. The antenna array is composed of sub-arrays, and the impact of sub-array spacing on the spectral efficiency is investigated through simulations using a channel simulator named NYUSIM developed based on extensive measured data at mmWave frequencies. Simulation results indicate spectral efficiencies of 18.5-28.1 bits/s/Hz with a sub-array spacing of 16 wavelengths for an outdoor mmWave urban LOS channel. The spectral efficiencies obtained are for single-user (SU) MIMO transmission at the recently allocated 5G carrier frequencies in July 2016. The method and results in this paper are useful for designing antenna array architectures for 5G wireless systems.3a:T674,Multi-cell wireless systems usually encounter both intra-cell and inter-cell interference, which can be mitigated via coordinated multipoint (CoMP) transmission. Previous works on multi-cell analysis in the microwave band generally consider fully digital beamforming, requiring a complete radio-frequency chain behind each antenna. This is practically infeasible for millimeter-wave (mmWave) systems where large amounts of antennas are necessary to provide sufficient gain and to enable transmission/reception of multiple streams to/from a user. This paper provides a general methodology to analytically compute the expected per-cell spectral efficiency of an mmWave multi-cell single-stream system using phase-shifter-based analog beamforming and regularized zero-forcing digital beamforming. Four analog-digital hybrid beamforming techniques for multi-cell multi-stream mmWave communication are proposed, assuming that base stations in different cells can share channel state information to cooperatively transmit signals to their home-cell users. Spectral efficiency of the proposed hybrid beamforming approaches is investigated and compared using two channel models suitable for fifth-generation cellular systems, namely the 3rd Generation Partnership Project model and the NYUSIM model. Numerical results show that the benefits of base station coordination (as compared with the non-CoMP case) are governed by the underlying propagation model, and the aggregate interference levels proportional to the cell radius and number of users per cell. We show that in sparse channels, non-CoMP approaches exceed CoMP (coordinated beamforming) performance.3b:T4a2,It is becoming clear that 5G wireless systems will encompass frequencies from around 500 MHz all the way to around 100 GHz. To adequately assess the performance of 5G systems in these different bands, path loss (PL) models will need to be developed across this wide frequency range. The PL models can roughly be broken into two categories, ones that have some anchor in physics, and ones that curve- match only over the data set without any physical anchor. In this paper we use both real-world measurements from 2 to 28 GHz and ray-tracing studies from 2 to 73.5 GHz, both in an urban-macro environment, to assess the prediction performance of the two PL modeling techniques. In other words, we look at how the two different PL modeling techniques perform when the PL model is applied to a prediction set which is different in distance, frequency, or environment from a measurement set where the parameters of the respective models are determined. We show that a PL model with a physical anchor point can be a better predictor of PL performance in the prediction sets while also providing a parameterization which is more stable over a substantial number of different measurement sets.3c:T710,This paper studies an integrated sensing and communications (ISAC) system for low-altitude economy (LAE), where a ground base station (GBS) provides communication and navigation services for authorized unmanned aerial vehicles (UAVs), while sensing the low-altitude airspace to monitor the unauthorized mobile target. The expected communication sum-rate over a given flight period is maximized by jointly optimizing the beamforming at the GBS and UAVs’ trajectories, subject to the constraints on the average signal-to-noise ratio requirement for sensing, the flight mission and collision avoidance of UAVs, as well as the maximum transmit power at the GBS. Typically, this is a sequential decision-making problem with the given flight mission. Thus, we transform it to a specific Markov decision process (MDP) model called episode task. Based on this modeling, we propose a novel LAE-oriented ISAC scheme, referred to as Deep LAE-ISAC (DeepLSC), by leveraging the deep reinforcement learning (DRL) technique. In DeepLSC, a reward function and a new action selection policy termed constrained noise-exploration policy are judiciously designed to fulfill various constraints. To enable efficient learning in episode tasks, we develop a hierarchical experience replay mechanism, where the gist is to employ all experiences generated within each episode to jointly train the neural network. Besides, to enhance the convergence speed of DeepLSC, a symmetric experience augmentation mechanism, which simultaneously permutes the indexes of all variables to enrich available experience sets, is proposed. Simulation results demonstrate that compared with benchmarks, DeepLSC yields a higher sum-rate while meeting the preset constraints, achieves faster convergence, and is more robust against different settings.3d:T479,This paper presents millimeter wave propagation measurements in New York City and an analysis of signal outage at 28 and 73 GHz using similar spread spectrum sliding correlator channel sounders that employed high gain, directional steerable antennas (24.5 dBi gain antennas at 28 GHz and 27 dBi gain antennas at 73 GHz) at both the transmitter and receiver. Three identical transmitter locations were used for both the 28 and 73 GHz campaigns, while the 73 GHz campaign included two new TX locations. The 28 GHz campaign tested 25 receiver locations for each of the three transmitter locations, and the 73 GHz campaign tested 27 receiver locations in various combinations with the five transmitter sites. Overall, 75 TX-RX location combinations were tested at 28 GHz and 74 TX-RX combinations were tested at 73 GHz, with T-R (transmitter-receiver) separation distances up to 425 m. The maximum transmit power was 30 dBm at 28 GHz and 14.6 dBm at 73 GHz. Our analysis shows that the estimated outage probabilities at 28 and 73 GHz for the cellular communication scenario are 14% and 17%, respectively, and is 16% for the 73 GHz backhaul scenario.3e:T588,This paper presents outdoor wideband small-scale spatial fading and autocorrelation measurements and results in the 73 GHz millimeter-wave (mmWave) band conducted in downtown Brooklyn, New York. Both directional and omnidirectional receiver (RX) antennas are studied. Two pairs of transmitter (TX) and RX locations were tested with one line-of-sight (LOS) and one non-line-of-sight (NLOS) environment, where a linear track was employed at each RX to move the antenna in half-wavelength increments. Measured data reveal that the small-scale spatial fading of the received signal voltage amplitude are generally Ricean-distributed for both omnidirectional and directional RX antenna patterns under both LOS and NLOS conditions in most cases, except for the log-normal distribution for the omnidirectional RX antenna pattern in the NLOS environment. Sinusoidal exponential and typical exponential functions are found to model small-scale spatial autocorrelation of the received signal voltage amplitude in LOS and NLOS environments in most cases, respectively. Furthermore, different decorrelation distances were observed for different RX track orientations, i.e., for different directions of motion relative to the TX. Results herein are valuable for characterizing small-scale spatial fading and autocorrelation properties in multiple-input multiple-output (MIMO) systems for fifth-generation (5G) mmWave frequencies.3f:T428,Future wireless systems are likely to adopt extremely large aperture arrays to achieve higher throughput, wider coverage, and higher spatial resolution. Conventional wireless systems pre-dominantly operate in the far field (FF) of the radiation source. However, as the array size increases and the carrier wavelength decreases, the near field (NF) becomes non-negligible. Since the NF and FF differ in many aspects, it is critical to identify their corresponding regions. In this article, we first provide a comprehensive overview of the existing NF-FF boundaries, then introduce a novel NF-FF demarcation method based on effective degrees of freedom (EDoF) of the channel. Since EDoF is intimately related to channel capacity, the EDoF-based border is able to characterize key channel performance more accurately than the classic Rayleigh distance and other representative benchmarks. Furthermore, we analyze the main features of the EDoF-based NF-FF boundary, provide insights into system design, and outline the associated challenges and research opportunities.40:T5bd,Omnidirectional path loss models are vital for radiosystem design in wireless communication systems, as they allow engineers to perform network simulations for systems with arbitrary antenna patterns. At millimeter-wave frequencies, channel measurements are frequently conducted using steerable highgain directional antennas at both the transmitter and receiver to make up for the significant increase in free space path loss at these frequencies compared to traditional cellular systems that operate at lower frequencies. The omnidirectional antenna pattern, and resulting omnidirectional received power must therefore be synthesized from many unique pointing angles, where the transmit and receive antennas are rotated over many different azimuth and elevation planes. In this paper, the equivalent omnidirectional antenna pattern and omnidirectional received power are synthesized by summing the received powers from all measured unique pointing angles obtained at antenna halfpower beamwidth step increments in the azimuth and elevation planes, and this method is validated by demonstrating that the synthesized omnidirectional received power and path loss are independent of antenna beamwidth, through theoretical analyses and millimeter-wave propagation measurements using antennas with different beamwidths. The method in this paper is shown to provide accurate results while enhancing the measurement range substantially through the use of directional antennas.41:T5ed,This paper presents a millimeter-wave (mmWave) wideband sliding correlator channel sounder with flexibility to operate at various transmission rates. The channel sounder can transmit and receive up to 1 GHz of RF null-to-null bandwidth while measuring a 2 nanosecond multipath time resolution. The system architecture takes advantage of field-programmable gate arrays (FPGAs), high-speed digital-to-analog converters (DACs), and low phase noise Rubidium (Rb) references for synchronization. Using steerable narrowbeam antennas, the system can measure up to 185 dB of path loss. The channel sounder is used to measure the directional and omnidirectional received power as a receiver transitions from line-of-sight to non-line-of-sight conditions down an urban canyon. A 25 dB drop in omnidirectional received power was observed as the receiver transitioned from line-of-sight (LOS) conditions to deeply shadowed non-LOS (NLOS) conditions. The channel sounder was also used to study signal variation and spatial consistency for a local set of receiver locations arranged in a cluster spanning a 5 m x 10 m local area, where the omnidirectional received power in LOS and NLOS environments is found to be relatively stable with standard deviations of received power of 2.2 dB and 4.3 dB, respectively. This work shows that when implementing beamforming at the transmitter at mmWave, the omnidirectional received power over a local area has little fluctuation among receiver locations separated by a few to several meters.42:T7d3,Triggered by the introduction of higher frequencies (above 24 GHz), there has been a long-standing debate in the radio propagation community on whether higher frequency radio channels are sparser relative to channels below 6 GHz. Here, sparsity implies a few dominant multipath components containing the vast majority of the electromagnetic energy. This discussion has recently been revisited with the study and interest in bands above 100 GHz for future wireless access. In this paper, the level of sparsity is examined at 6, 26, and 105 GHz carrier frequencies by conducting channel measurements in an indoor office environment. By using the Gini index (value between 0 and 1) as a metric for characterizing sparsity, we show that increasing carrier frequency leads to increased levels of sparsity. The measured channel impulse responses are used to derive a Third-Generation Partnership Project (3GPP)-style propagation model, used to calculate the Gini index for the comparison of the channel sparsity between the measurement and simulation based on the 3GPP model. Our results show that the mean value of the Gini index in measurement is nearly twice the value in simulation, implying that the 3GPP channel model does not capture the effects of sparsity in the delay domain as frequency increases. In addition, a new intra-cluster power allocation model based on measurements is proposed to characterize the effects of sparsity in the delay domain of the 3GPP channel model. The accuracy of the proposed model is analyzed using theoretical derivations and simulations. Using the derived intra-cluster power allocation model, the mean value of the Gini index is 0.91 in the sub-THz band, while the spread of variability is restricted to 0.01, demonstrating that the proposed model is suitable for 3GPP-type channels. To our best knowledge, this paper is the first to perform measurements and analysis across a wide range of frequencies for the evaluation of channel sparsity in the same environment.43:T587,Reconfigurable intelligent surfaces (RISs) have emerged as a prospective technology for next-generation wireless networks due to their potential in coverage and capacity enhancement. Previous works on achievable rate analysis of RIS-assisted communication systems have mainly focused on the rich-scattering environment where Rayleigh and Rician channel models can be applied. This work studies the ergodic achievable rate of RIS-assisted multiple-input multiple-output communication systems in the millimeter-wave band with limited scattering under the Saleh-Valenzuela channel model. Firstly, we derive an upper bound of the ergodic achievable rate by means of majorization theory and Jensen’s inequality. The upper bound shows that the ergodic achievable rate increases logarithmically with the number of antennas at the base station (BS) and user, the number of the reflection units at the RIS, and the eigenvalues of the steering matrices associated with the BS, user and RIS. Then, we aim to maximize the ergodic achievable rate by jointly optimizing the transmit covariance matrix at the BS and the reflection coefficients at the RIS. Specifically, the transmit covariance matrix is optimized by the water-filling algorithm and the reflection coefficients are optimized using the Riemannian conjugate gradient algorithm. Simulation results validate the effectiveness of the proposed optimization algorithms.44:T5bc,This paper studies an integrated sensing and communication (ISAC) system, where a multi-antenna base station transmits beamformed signals for joint downlink multi-user communication and radar sensing of an extended target (ET). By considering echo signals as reflections from valid elements on the ET contour, a set of novel Cramér-Rao bounds (CRBs) is derived for parameter estimation of the ET, including central range, direction, and orientation. The ISAC transmit beamforming design is then formulated as an optimization problem, aiming to minimize the CRB associated with radar sensing, while satisfying a minimum signal-to-interference-pulse-noise ratio requirement for each communication user, along with a 3-dB beam coverage constraint tailored for the ET. To solve this non-convex problem, we utilize semidefinite relaxation (SDR) and propose a rank-one solution extraction scheme for non-tight relaxation circumstances. To reduce the computation complexity, we further employ an efficient zero-forcing (ZF) based beamforming design, where the sensing task is performed in the null space of communication channels. Numerical results validate the effectiveness of the obtained CRB, revealing the diverse features of CRB for differently shaped ETs. The proposed SDR beamforming design outperforms benchmark designs with lower estimation error and CRB, while the ZF beamforming design greatly improves computation efficiency with minor sensing performance loss.45:T55e,Fast and precise beam alignment is crucial for high-quality data transmission in millimeter-wave (mmWave) communication systems, where large-scale antenna arrays are utilized to overcome the severe propagation loss. To tackle the challenging problem, we propose a novel deep learning-based hierarchical beam alignment method for both multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) systems, which learns two tiers of probing codebooks (PCs) and uses their measurements to predict the optimal beam in a coarse-to-fine search manner. Specifically, a hierarchical beam alignment network (HBAN) is developed for MISO systems, which first performs coarse channel measurement using a tier-1 PC, then selects a tier-2 PC for fine channel measurement, and finally predicts the optimal beam based on both coarse and fine measurements. The propounded HBAN is trained in two steps: the tier-1 PC and the tier-2 PC selector are first trained jointly, followed by the joint training of all the tier-2 PCs and beam predictors. Furthermore, an HBAN for MIMO systems is proposed to directly predict the optimal beam pair without performing beam alignment individually at the transmitter and receiver. Numerical results demonstrate that the proposed HBANs are superior to the state-of-the-art methods in both alignment accuracy and signaling overhead reduction.46:T5c9,Integrated sensing and communication (ISAC) has attracted growing interests for enabling the future 6G wireless networks, due to its capability of sharing spectrum and hardware resources between communication and sensing systems. However, existing works on ISAC usually need to modify the communication protocol to cater for the new sensing performance requirement, which may be difficult to implement in practice. In this paper, we study a semi-passive intelligent reflecting surface (IRS) aided millimeter-wave (mmWave) ISAC system by exploiting the established beam scanning operation for simultaneous mmWave communications and sensing. First, we propose a two-phase ISAC protocol, consisting of beam scanning and data transmission. Specifically, in the beam scanning phase, the semi-passive IRS finds the optimal beam for reflecting signals from the base station to a communication user via its passive elements and, meanwhile, directly estimates the angle of a nearby target based on echo signals from the target using its active sensing elements. In the data transmission phase, the sensing accuracy is further improved by leveraging the data signals via possible IRS beam splitting. Next, we derive the achievable rate of the communication user as well as the Cramér-Rao bound and the approximate mean square error of the target angle estimation. Finally, extensive simulation results are provided to verify our analysis as well as the effectiveness of the proposed scheme.16:["$","div",null,{"className":"mx-auto max-w-6xl px-4 py-6 sm:px-6 sm:py-10","children":[["$","header",null,{"className":"grid gap-6 lg:grid-cols-[1fr_22rem] lg:items-end","children":[["$","div",null,{"children":[["$","p",null,{"className":"label mb-2 text-accent","children":["Researcher profile · ","OpenAlex"]}],["$","h1",null,{"className":"text-3xl font-bold sm:text-4xl","children":"Shu Dong Sun"}],["$","ul",null,{"className":"mt-3 flex flex-col gap-1 text-[15px]","children":[["$","li","Shanghai Jiao Tong University",{"className":"flex items-center gap-2","children":[["$","span",null,{"className":"font-medium","children":"Shanghai Jiao Tong University"}],["$","p",null,{"className":"mt-1.5 max-w-2xl text-sm text-muted","children":"School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China"}],["$","span",null,{"className":"tag","children":"CN"}]]}]]}],["$","div",null,{"className":"mt-4 flex flex-wrap gap-2","children":[["$","$L1e",null,{"path":"/author/openalex%3AA5014646698","title":"Shu Dong Sun — researcher profile on SciRah","buttonLabel":"Share profile","className":"btn-ghost py-1.5"}],["$","span",null,{"className":"tag self-center","title":"ORCID iD: this researcher's unique international researcher number","children":["ORCID iD ","0000-0001-8655-3746"]}],"$undefined"]}],"$undefined","$undefined"]}],["$","$L1f",null,{"authorId":"openalex:A5014646698","initial":{"works":116,"citations":20970,"hIndex":42,"i10Index":63}}]]}],["$","div",null,{"className":"mt-6 flex flex-wrap gap-1.5","children":[["$","span","Millimeter-Wave Propagation and Modeling",{"className":"tag","children":"Millimeter-Wave Propagation and Modeling"}],["$","span","Advanced Wireless Communication Technologies",{"className":"tag","children":"Advanced Wireless Communication Technologies"}],["$","span","Advanced Wireless Communication Techniques",{"className":"tag","children":"Advanced Wireless Communication Techniques"}]]}],["$","div",null,{"className":"relative h-8 w-full my-8","aria-hidden":"true","children":["$","svg",null,{"viewBox":"0 0 1200 40","preserveAspectRatio":"none","className":"absolute inset-0 h-full w-full","children":[["$","path",null,{"d":"M0 20 C 120 6, 240 34, 360 20 S 600 6, 720 20 S 960 34, 1080 20 S 1200 12, 1200 12","fill":"none","stroke":"var(--line)","strokeWidth":"6","strokeLinecap":"round","vectorEffect":"non-scaling-stroke"}],["$","path",null,{"d":"M0 20 C 120 6, 240 34, 360 20 S 600 6, 720 20 S 960 34, 1080 20 S 1200 12, 1200 12","fill":"none","stroke":"var(--primary)","strokeOpacity":"0.55","strokeWidth":"1.25","strokeDasharray":"6 9","vectorEffect":"non-scaling-stroke"}]]}]}],["$","div",null,{"className":"grid gap-8 lg:grid-cols-[1fr_20rem]","children":[["$","section",null,{"aria-labelledby":"works","className":"min-w-0","children":[["$","h2",null,{"id":"works","className":"mb-4 text-xl font-semibold","children":"Most-cited papers"}],["$","$L20",null,{"authorId":"openalex:A5014646698","papers":[{"ids":{"openalex":"W2116334496","doi":"10.1109/access.2013.2260813"},"title":"Millimeter Wave Mobile Communications for 5G Cellular: It Will Work!","abstract":"The global bandwidth shortage facing wireless carriers has motivated the exploration of the underutilized millimeter wave (mm-wave) frequency spectrum for future broadband cellular communication networks. There is, however, little knowledge about cellular mm-wave propagation in densely populated indoor and outdoor environments. Obtaining this information is vital for the design and operation of future fifth generation cellular networks that use the mm-wave spectrum. In this paper, we present the motivation for new mm-wave cellular systems, methodology, and hardware for measurements and offer a variety of measurement results that show 28 and 38 GHz frequencies can be used when employing steerable directional antennas at base stations and mobile devices.","authors":[{"name":"Theodore S. Rappaport","id":"openalex:A5058193542","orcid":"https://orcid.org/0000-0001-7449-9957","affiliation":"New York University","institutionId":"I57206974","institutionUrl":"https://ror.org/0190ak572","affiliations":["NYU WIRELESS, Polytechnic Institute of New York University, New York, NY, USA","NYU WIRELESS, Polytech. 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Sch. of Eng., New York, NY, USA"]}],"venue":"IEEE Transactions on Communications","venueId":"S196647941","publisher":"IEEE Communications Society","publisherId":"P4310316002","year":2015,"publishedDate":"2015-05-18","type":"article","citationCount":1588,"referenceCount":90,"isOpenAccess":true,"sourceUrl":"https://doi.org/10.1109/tcomm.2015.2434384","oaUrl":"https://ieeexplore.ieee.org/ielx7/26/7240139/07109864.pdf","pdfUrl":"https://ieeexplore.ieee.org/ielx7/26/7240139/07109864.pdf","topics":[{"id":"T10936","name":"Millimeter-Wave Propagation and Modeling","score":1,"subfieldId":"2208","subfield":"Electrical and Electronic Engineering","fieldId":"22","field":"Engineering"},{"id":"T10125","name":"Advanced Wireless Communication Techniques","score":0,"subfieldId":"2208","subfield":"Electrical and Electronic Engineering","fieldId":"22","field":"Engineering"},{"id":"T10069","name":"Antenna Design and Analysis","score":0,"subfieldId":"2202","subfield":"Aerospace Engineering","fieldId":"22","field":"Engineering"}],"sources":["openalex"],"id":"openalex:W1584244501"},{"ids":{"openalex":"W905396345","doi":"10.1145/2980055.2987353"},"title":"Millimeter wave wireless communications","abstract":"This paper shows the remarkable distances that can be achieved using millimeter wave communications, and presents a new rural macrocell (RMa) path loss model for millimeter wave frequencies, based on measurements at 73 GHz in rural Virginia. Path loss models are needed to estimate signal coverage and interference for wireless network design, yet little is known about rural propagation at millimeter waves. This work identifies problems with the RMa model used by the 3rd Generation Partnership Project (3GPP) TR 38.900 Release 14, and offers a close-in (CI) reference distance model that has improved accuracy, fewer parameters, and better stability as compared with the existing 3GPP RMa path loss model. The measurements and models presented here are the first to validate rural millimeter wave path loss models.","authors":[{"name":"George R. MacCartney","id":"openalex:A5068361918","orcid":"https://orcid.org/0000-0002-8665-8318","affiliation":"New York University","institutionId":"I57206974","institutionUrl":"https://ror.org/0190ak572","affiliations":["New York University"]},{"name":"Shu Dong Sun","id":"openalex:A5014646698","orcid":"https://orcid.org/0000-0001-8655-3746","affiliation":"New York University","institutionId":"I57206974","institutionUrl":"https://ror.org/0190ak572","affiliations":["New York University"]},{"name":"Theodore S. Rappaport","id":"openalex:A5104364108","affiliation":"New York University","institutionId":"I57206974","institutionUrl":"https://ror.org/0190ak572","affiliations":["New York University"]},{"name":"Yunchou Xing","id":"openalex:A5090349133","orcid":"https://orcid.org/0000-0003-0378-2696","affiliation":"New York University","institutionId":"I57206974","institutionUrl":"https://ror.org/0190ak572","affiliations":["New York University"]},{"name":"Hangsong Yan","id":"openalex:A5039169473","orcid":"https://orcid.org/0000-0002-0463-8058","affiliation":"New York University","institutionId":"I57206974","institutionUrl":"https://ror.org/0190ak572","affiliations":["New York University"]},{"name":"Jeton Koka","id":"openalex:A5022462842","affiliation":"New York University","institutionId":"I57206974","institutionUrl":"https://ror.org/0190ak572","affiliations":["New York University"]},{"name":"Ruichen Wang","id":"openalex:A5022110710","orcid":"https://orcid.org/0000-0003-0873-0422","affiliation":"New York University","institutionId":"I57206974","institutionUrl":"https://ror.org/0190ak572","affiliations":["New York University"]},{"name":"Dian Yu","id":"openalex:A5070669227","affiliation":"New York University","institutionId":"I57206974","institutionUrl":"https://ror.org/0190ak572","affiliations":["New York University"]}],"year":2016,"publishedDate":"2016-09-22","type":"conference-paper","citationCount":1227,"referenceCount":14,"isOpenAccess":true,"sourceUrl":"https://doi.org/10.1145/2980055.2987353","oaUrl":"https://doi.org/10.1145/2980055.2987353","topics":[{"id":"T10936","name":"Millimeter-Wave Propagation and Modeling","score":1,"subfieldId":"2208","subfield":"Electrical and Electronic Engineering","fieldId":"22","field":"Engineering"},{"id":"T10891","name":"Radar Systems and Signal Processing","score":0,"subfieldId":"2202","subfield":"Aerospace Engineering","fieldId":"22","field":"Engineering"},{"id":"T10069","name":"Antenna Design and Analysis","score":0,"subfieldId":"2202","subfield":"Aerospace Engineering","fieldId":"22","field":"Engineering"}],"sources":["openalex"],"id":"openalex:W905396345"},{"ids":{"openalex":"W2009447832","doi":"10.1109/jsac.2014.2328111"},"title":"Millimeter-Wave Enhanced Local Area Systems: A High-Data-Rate Approach for Future Wireless Networks","abstract":"$23","authors":[{"name":"Amitava Ghosh","id":"openalex:A5055239311","orcid":"https://orcid.org/0000-0002-1623-0236","affiliation":"Nokia, Arlington Heights, IL, USA","affiliations":["Nokia, Arlington Heights, IL, USA"]},{"name":"Timothy A. 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Rappaport","id":"openalex:A5058193542","orcid":"https://orcid.org/0000-0001-7449-9957","affiliation":"New York University","institutionId":"I57206974","institutionUrl":"https://ror.org/0190ak572","affiliations":["Dept. of Electr. &","Comput. Eng., NYU Polytech. Sch. of Eng., Brooklyn, NY, USA","Dept. of Electr. & Comput. Eng., NYU Polytech. Sch. of Eng., Brooklyn, NY, USA"]},{"name":"George R. MacCartney","id":"openalex:A5068361918","orcid":"https://orcid.org/0000-0002-8665-8318","affiliation":"New York University","institutionId":"I57206974","institutionUrl":"https://ror.org/0190ak572","affiliations":["Dept. of Electr. &","Comput. Eng., NYU Polytech. Sch. of Eng., Brooklyn, NY, USA","Dept. of Electr. & Comput. Eng., NYU Polytech. Sch. of Eng., Brooklyn, NY, USA"]},{"name":"Shu Dong Sun","id":"openalex:A5014646698","orcid":"https://orcid.org/0000-0001-8655-3746","affiliation":"New York University","institutionId":"I57206974","institutionUrl":"https://ror.org/0190ak572","affiliations":["Dept. of Electr. &","Comput. Eng., NYU Polytech. Sch. of Eng., Brooklyn, NY, USA","Dept. of Electr. & Comput. Eng., NYU Polytech. Sch. of Eng., Brooklyn, NY, USA"]},{"name":"Shuai Nie","id":"openalex:A5074393476","orcid":"https://orcid.org/0000-0002-1038-4146","affiliation":"New York University","institutionId":"I57206974","institutionUrl":"https://ror.org/0190ak572","affiliations":["Dept. of Electr. &","Comput. Eng., NYU Polytech. Sch. of Eng., Brooklyn, NY, USA","Dept. of Electr. & Comput. Eng., NYU Polytech. Sch. of Eng., Brooklyn, NY, USA"]}],"venue":"IEEE Journal on Selected Areas in Communications","venueId":"S90422530","publisher":"Institute of Electrical and Electronics Engineers","publisherId":"P4310319808","year":2014,"publishedDate":"2014-06-01","type":"article","citationCount":796,"referenceCount":19,"isOpenAccess":false,"sourceUrl":"https://doi.org/10.1109/jsac.2014.2328111","topics":[{"id":"T10936","name":"Millimeter-Wave Propagation and Modeling","score":0.9994999766349792,"subfieldId":"2208","subfield":"Electrical and Electronic Engineering","fieldId":"22","field":"Engineering"},{"id":"T10069","name":"Antenna Design and Analysis","score":0.00009999999747378752,"subfieldId":"2202","subfield":"Aerospace Engineering","fieldId":"22","field":"Engineering"},{"id":"T10148","name":"Advanced MIMO Systems Optimization","score":0,"subfieldId":"2208","subfield":"Electrical and Electronic Engineering","fieldId":"22","field":"Engineering"}],"sources":["openalex"],"id":"openalex:W2009447832"},{"ids":{"openalex":"W2103851250","doi":"10.1109/mcom.2014.6979962"},"title":"Mimo for millimeter-wave wireless communications: beamforming, spatial multiplexing, or both?","abstract":"The use of mmWave frequencies for wireless communications offers channel bandwidths far greater than previously available, while enabling dozens or even hundreds of antenna elements to be used at the user equipment, base stations, and access points. To date, MIMO techniques, such as spatial multiplexing, beamforming, and diversity, have been widely deployed in lower-frequency systems such as IEEE 802.11n/ac (wireless local area networks) and 3GPP LTE 4G cellphone standards. Given the tiny wavelengths associated with mmWave, coupled with differences in the propagation and antennas used, it is unclear how well spatial multiplexing with multiple streams will be suited to future mmWave mobile communications. This tutorial explores the fundamental issues involved in selecting the best communications approaches for mmWave frequencies, and provides insights, challenges, and appropriate uses of each MIMO technique based on early knowledge of the mmWave propagation environment.","authors":[{"name":"Shu Dong Sun","id":"openalex:A5014646698","orcid":"https://orcid.org/0000-0001-8655-3746","affiliation":"New York University","institutionId":"I57206974","institutionUrl":"https://ror.org/0190ak572","affiliations":["NYU WIRELESS and New York University","New York University"]},{"name":"Theodore S. Rappaport","id":"openalex:A5058193542","orcid":"https://orcid.org/0000-0001-7449-9957","affiliation":"New York University","institutionId":"I57206974","institutionUrl":"https://ror.org/0190ak572","affiliations":["NYU WIRELESS and New York University","New York University"]},{"name":"Robert W. Heath","id":"openalex:A5029812222","orcid":"https://orcid.org/0000-0002-4666-5628","affiliation":"The University of Texas at Austin","institutionId":"I86519309","institutionUrl":"https://ror.org/00hj54h04","affiliations":["University of Texas at Austin","University of Texas at Austin#TAB#"]},{"name":"Andrew Nix","id":"openalex:A5021417938","orcid":"https://orcid.org/0000-0002-6824-3581","affiliation":"University of Bristol","institutionId":"I36234482","institutionUrl":"https://ror.org/0524sp257","affiliations":["University of Bristol"]},{"name":"Sundeep Rangan","id":"openalex:A5000099903","orcid":"https://orcid.org/0000-0002-0925-8169","affiliation":"New York University","institutionId":"I57206974","institutionUrl":"https://ror.org/0190ak572","affiliations":["NYU WIRELESS and New York University","New York University"]}],"venue":"IEEE Communications Magazine","venueId":"S158797327","publisher":"Institute of Electrical and Electronics Engineers","publisherId":"P4310319808","year":2014,"publishedDate":"2014-12-01","type":"article","citationCount":638,"referenceCount":20,"isOpenAccess":false,"sourceUrl":"https://doi.org/10.1109/mcom.2014.6979962","topics":[{"id":"T10936","name":"Millimeter-Wave Propagation and Modeling","score":0.9965999722480774,"subfieldId":"2208","subfield":"Electrical and Electronic Engineering","fieldId":"22","field":"Engineering"},{"id":"T10125","name":"Advanced Wireless Communication Techniques","score":0.00139999995008111,"subfieldId":"2208","subfield":"Electrical and Electronic Engineering","fieldId":"22","field":"Engineering"},{"id":"T10069","name":"Antenna Design and Analysis","score":0.0005000000237487257,"subfieldId":"2202","subfield":"Aerospace Engineering","fieldId":"22","field":"Engineering"}],"sources":["openalex"],"id":"openalex:W2103851250"},{"ids":{"openalex":"W2219770862","doi":"10.1109/access.2015.2486778"},"title":"Indoor Office Wideband Millimeter-Wave Propagation Measurements and Channel Models at 28 and 73 GHz for Ultra-Dense 5G Wireless Networks","abstract":"$24","authors":[{"name":"George R. 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This paper provides a comprehensive overview of the 3GPP 5G-Advanced development, introducing the prominent state-of-the-art technologies investigated in 3GPP and identifying key evolution directions for future research and standardization.","authors":[{"name":"Wanshi Chen","id":"openalex:A5037404272","orcid":"https://orcid.org/0000-0001-5061-2177","affiliation":"Qualcomm (United States)","institutionId":"I4210087596","institutionUrl":"https://ror.org/002zrf773","affiliations":["Qualcomm, San Diego, CA, USA"]},{"name":"Xingqin Lin","id":"openalex:A5101562030","orcid":"https://orcid.org/0000-0003-2496-1597","affiliation":"Nvidia (United States)","institutionId":"I4210127875","institutionUrl":"https://ror.org/03jdj4y14","affiliations":["Nvidia, Santa Clara, CA, USA"]},{"name":"Juho Lee","id":"openalex:A5100680420","orcid":"https://orcid.org/0000-0003-0347-6998","affiliation":"Samsung (South Korea)","institutionId":"I2250650973","institutionUrl":"https://ror.org/04w3jy968","affiliations":["Samsung, Seocho-gu, Seoul, South Korea"]},{"name":"Antti Toskala","id":"openalex:A5052931505","affiliation":"Nokia (Finland)","institutionId":"I2738502077","institutionUrl":"https://ror.org/04pkc8m17","affiliations":["Nokia, Espoo, Finland"]},{"name":"Shu Dong Sun","id":"openalex:A5014646698","orcid":"https://orcid.org/0000-0001-8655-3746","affiliation":"Shanghai Jiao Tong University","institutionId":"I183067930","institutionUrl":"https://ror.org/0220qvk04","affiliations":["Department of Electronic Engineering and the Shanghai Key Laboratory of Digital Media Processing and Transmission, Shanghai Jiao Tong University, Shanghai, China"]},{"name":"Carla Fabiana Chiasserini","id":"openalex:A5021780258","orcid":"https://orcid.org/0000-0003-1410-660X","affiliation":"Politecnico di Torino","institutionId":"I177477856","institutionUrl":"https://ror.org/00bgk9508","affiliations":["Politecnico di Torino, Torino, Italy"]},{"name":"Lingjia Liu","id":"openalex:A5027237940","orcid":"https://orcid.org/0000-0003-1915-1784","affiliation":"Virginia Tech","institutionId":"I859038795","institutionUrl":"https://ror.org/02smfhw86","affiliations":["Virginia Tech, Blacksburg, VA, USA"]}],"venue":"IEEE Journal on Selected Areas in Communications","venueId":"S90422530","publisher":"Institute of Electrical and Electronics Engineers","publisherId":"P4310319808","year":2023,"publishedDate":"2023-06-01","type":"article","citationCount":410,"referenceCount":176,"isOpenAccess":true,"sourceUrl":"https://doi.org/10.1109/jsac.2023.3274037","oaUrl":"https://iris.polito.it/bitstream/11583/2977710/1/JSAC_5G_Advanced-6_IRIS.pdf","pdfUrl":"https://iris.polito.it/bitstream/11583/2977710/1/JSAC_5G_Advanced-6_IRIS.pdf","topics":[{"id":"T11458","name":"Advanced Wireless Communication Technologies","score":0.7064999938011169,"subfieldId":"2208","subfield":"Electrical and Electronic Engineering","fieldId":"22","field":"Engineering"},{"id":"T13905","name":"Telecommunications and Broadcasting Technologies","score":0.18209999799728394,"subfieldId":"2214","subfield":"Media Technology","fieldId":"22","field":"Engineering"},{"id":"T10714","name":"Software-Defined Networks and 5G","score":0.042100001126527786,"subfieldId":"1705","subfield":"Computer Networks and Communications","fieldId":"17","field":"Computer Science"}],"sources":["openalex"],"id":"openalex:W4381331133"},{"ids":{"openalex":"W2249053460","doi":"10.1109/vtcspring.2016.7504435"},"title":"Propagation Path Loss Models for 5G Urban Micro- and Macro-Cellular Scenarios","abstract":"$27","authors":[{"name":"Shu Dong Sun","id":"openalex:A5014646698","orcid":"https://orcid.org/0000-0001-8655-3746","affiliation":"New York University","institutionId":"I57206974","institutionUrl":"https://ror.org/0190ak572","affiliations":["NYU WIRELESS and Tandon School of Engineering, New York University, Brooklyn, NY, USA"]},{"name":"Theodore S. 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This document presents a preliminary overview of 5G channel models for bands up to 100 GHz. These have been derived based on extensive measurement and ray tracing results across a multitude of frequencies from 6 GHz to 100 GHz, and this document describes an initial 3D channel model which includes: 1) typical deployment scenarios for urban microcells (UMi) and urban macrocells (UMa), and 2) a baseline model for incorporating path loss, shadow fading, line of sight probability, penetration and blockage models for the typical scenarios. Various processing methodologies such as clustering and antenna decoupling algorithms are also presented.","authors":[{"name":"Katsuyuki Haneda","id":"openalex:A5020777570","orcid":"https://orcid.org/0000-0002-4778-6405","affiliation":"Aalto University","institutionId":"I9927081","institutionUrl":"https://ror.org/020hwjq30","affiliations":["Aalto University"]},{"name":"Jianhua Zhang","id":"openalex:A5100434769","orcid":"https://orcid.org/0000-0002-6492-3846","affiliation":"Beijing University of Posts and Telecommunications","institutionId":"I139759216","institutionUrl":"https://ror.org/04w9fbh59","affiliations":["BUPT","Samsung"]},{"name":"Lei Tan","affiliation":"Beijing University of Posts and Telecommunications","institutionId":"I139759216","institutionUrl":"https://ror.org/04w9fbh59","affiliations":["BUPT"]},{"name":"Guangyi Liu","id":"openalex:A5100737335","orcid":"https://orcid.org/0000-0002-8656-1946","affiliation":"CMCC Foundation - Euro-Mediterranean Center on Climate Change","institutionId":"I4210111827","institutionUrl":"https://ror.org/01tf11a61","affiliations":["CMCC"]},{"name":"Yi Feng Zheng","id":"openalex:A5100624069","orcid":"https://orcid.org/0009-0005-9043-7103","affiliation":"CMCC Foundation - Euro-Mediterranean Center on Climate Change","institutionId":"I4210111827","institutionUrl":"https://ror.org/01tf11a61","affiliations":["CMCC"]},{"name":"Henrik Asplund","id":"openalex:A5032085580","affiliation":"Ericsson (Sweden)","institutionId":"I1306339040","institutionUrl":"https://ror.org/05a7rhx54","affiliations":["Ericsson"]},{"name":"Jian Li","id":"openalex:A5100402609","orcid":"https://orcid.org/0000-0003-3439-7539","affiliation":"Huawei Technologies (China)","institutionId":"I2250955327","institutionUrl":"https://ror.org/00cmhce21","affiliations":["Huawei"]},{"name":"Yi Wang","id":"openalex:A5101682898","orcid":"https://orcid.org/0000-0003-3833-4287","affiliation":"Huawei Technologies (China)","institutionId":"I2250955327","institutionUrl":"https://ror.org/00cmhce21","affiliations":["Huawei"]},{"name":"David Steer","id":"openalex:A5103943313","affiliation":"Huawei Technologies (China)","institutionId":"I2250955327","institutionUrl":"https://ror.org/00cmhce21","affiliations":["Huawei"]},{"name":"Clara Li","id":"openalex:A5014207615","affiliation":"Intel (United States)","institutionId":"I1343180700","institutionUrl":"https://ror.org/01ek73717","affiliations":["Intel"]},{"name":"Tommaso Balercia","id":"openalex:A5027113405","orcid":"https://orcid.org/0000-0003-3562-7711","affiliation":"Intel (United States)","institutionId":"I1343180700","institutionUrl":"https://ror.org/01ek73717","affiliations":["Intel"]},{"name":"Sunguk Lee","id":"openalex:A5101974996","orcid":"https://orcid.org/0009-0009-2987-192X","affiliation":"Korea Telecom (South Korea)","institutionId":"I101155339","institutionUrl":"https://ror.org/043n4tt17","affiliations":["KT Corporation"]},{"name":"Young-Suk Kim","id":"openalex:A5100773901","orcid":"https://orcid.org/0000-0001-7525-5428","affiliation":"Korea Telecom (South Korea)","institutionId":"I101155339","institutionUrl":"https://ror.org/043n4tt17","affiliations":["KT Corporation"]},{"name":"Amitava Ghosh","id":"openalex:A5055239311","orcid":"https://orcid.org/0000-0002-1623-0236","affiliation":"AT&T (United States)","institutionId":"I1283103587","institutionUrl":"https://ror.org/02bbd5539","affiliations":["AT&T","Nokia"]},{"name":"Timothy A. 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Propagation characteristics at these bands have a fundamental impact on each aspect of the cellular architecture, ranging from equipment design to real-time performance in the field. While we have a reasonable understanding of the propagation characteristics at microwave (<; 6 GHz) frequencies, the same cannot be said for mmWave. This article explains key differences in the propagation characteristics between the microwave and mmWave bands, and further gives examples of how these differences impact 5G system design.","authors":[{"name":"Mansoor Shafi","id":"openalex:A5047954881","orcid":"https://orcid.org/0000-0002-5492-8969","affiliation":"Spark, New Zealand","affiliations":["Spark, New Zealand"]},{"name":"Jianhua Zhang","id":"openalex:A5100434769","orcid":"https://orcid.org/0000-0002-6492-3846","affiliation":"Beijing University of Posts and Telecommunications","institutionId":"I139759216","institutionUrl":"https://ror.org/04w9fbh59","affiliations":["Beijing University of Posts and Telecommunications"]},{"name":"Harsh Tataria","id":"openalex:A5014951406","orcid":"https://orcid.org/0000-0001-6085-4102","affiliation":"Queen's University Belfast","institutionId":"I126231945","institutionUrl":"https://ror.org/00hswnk62","affiliations":["Queen's University, Belfast"]},{"name":"Andreas F. Molisch","id":"openalex:A5071725365","orcid":"https://orcid.org/0000-0002-4779-4763","affiliation":"University of Southern California","institutionId":"I1174212","institutionUrl":"https://ror.org/03taz7m60","affiliations":["University of Southern California"]},{"name":"Shu Dong Sun","id":"openalex:A5014646698","orcid":"https://orcid.org/0000-0001-8655-3746","affiliation":"Intel (United States)","institutionId":"I1343180700","institutionUrl":"https://ror.org/01ek73717","affiliations":["Intel Corporation"]},{"name":"Theodore S. 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The data provided herein may be used by antenna, propagation, and communications researchers for emerging mobile and/or backhaul millimeter-wave (mmWave) system analyses. This paper also presents measurement layout maps with transmitter and receiver locations and GPS coordinates, so that anyone may create similar or new measurements and models, or may perform further processing, such as with ray-tracers and modeling tools, in addition to studying mmWave system performance. Using the data provided herein, large-scale path loss models using a standard close-in 1 meter free-space reference distance are provided for each of the three frequency bands.","authors":[{"name":"George R. 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Employing experimental data obtained from 28 GHz and 73 GHz propagation measurements in the dense urban environment of New York City, we present the impact of coherent bi-beam, tri-beam and quad-beam combining on path loss and shadow factors. The results reveal that a maximum of 24.9 dB improvement in path loss at 28 GHz and 34.8 dB at 73 GHz for 100 m T-R (transmitter-receiver) separation distances can be achieved via combining the strongest four received signals from distinct beams, when compared to the case of signals at the receiver with randomly pointed beams. Comparable path loss values are achieved at both 28 and 73 GHz bands. This paper demonstrates the potential of utilizing spatial filtering and beam combining to significantly improve received signal levels and link margins at millimeter-wave frequencies.","authors":[{"name":"Shu Dong Sun","id":"openalex:A5014646698","orcid":"https://orcid.org/0000-0001-8655-3746","affiliation":"New York University","institutionId":"I57206974","institutionUrl":"https://ror.org/0190ak572","affiliations":["NYU WIRELESS, Polytechnic School of Engineering, New York University, Brooklyn, NY","Polytech. Sch. of Eng., NYU Wireless, New York Univ., New York, NY, USA"]},{"name":"George R. MacCartney","id":"openalex:A5068361918","orcid":"https://orcid.org/0000-0002-8665-8318","affiliation":"New York University","institutionId":"I57206974","institutionUrl":"https://ror.org/0190ak572","affiliations":["NYU WIRELESS, Polytechnic School of Engineering, New York University, Brooklyn, NY","Polytech. Sch. of Eng., NYU Wireless, New York Univ., New York, NY, USA"]},{"name":"Mathew K. 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This article presents channel measurement results obtained using a spread spectrum sliding correlator channel sounder at 28 GHz with an ultra-wide RF first null-to-null bandwidth of 800 MHz, collected in the dense urban environment of New York City. Statistics of multipath components, RMS delay spread, path loss, and angle distributions are investigated for antennas with different beamwidths. Theoretical beam combining analysis is performed, showing that the path loss can be reduced by 18.7 dB/decade in distance by coherently combining the four strongest beams at different pointing angles when compared to randomly pointed single beam antennas. Preliminary designs of beamforming and beam combining schemes using frequency domain equalization are proposed.","authors":[{"name":"Shu Dong Sun","id":"openalex:A5014646698","orcid":"https://orcid.org/0000-0001-8655-3746","affiliation":"New York University","institutionId":"I57206974","institutionUrl":"https://ror.org/0190ak572","affiliations":["NYU WIRELESS, NYU Polytechnic School of Engineering, Brooklyn, NY","Polytech. Sch. of Eng., NYU Wireless, New York Univ., New York, NY, USA"]},{"name":"Theodore S. Rappaport","id":"openalex:A5058193542","orcid":"https://orcid.org/0000-0001-7449-9957","affiliation":"New York University","institutionId":"I57206974","institutionUrl":"https://ror.org/0190ak572","affiliations":["NYU WIRELESS, NYU Polytechnic School of Engineering, Brooklyn, NY","Polytech. Sch. of Eng., NYU Wireless, New York Univ., New York, NY, USA"]}],"venue":"IEEE MTT-S International Microwave Symposium digest","venueId":"S4210185519","publisher":"Institute of Electrical and Electronics Engineers","publisherId":"P4310319808","year":2014,"publishedDate":"2014-06-01","type":"conference-paper","citationCount":34,"referenceCount":17,"isOpenAccess":false,"sourceUrl":"https://doi.org/10.1109/mwsym.2014.6848596","topics":[{"id":"T10936","name":"Millimeter-Wave Propagation and Modeling","score":1,"subfieldId":"2208","subfield":"Electrical and Electronic Engineering","fieldId":"22","field":"Engineering"},{"id":"T10069","name":"Antenna Design and Analysis","score":0,"subfieldId":"2202","subfield":"Aerospace Engineering","fieldId":"22","field":"Engineering"},{"id":"T10125","name":"Advanced Wireless Communication Techniques","score":0,"subfieldId":"2208","subfield":"Electrical and Electronic 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Innovation Center (CMIC), Shanghai Jiao Tong University, Shanghai, China"]},{"name":"Shu Dong Sun","id":"openalex:A5014646698","orcid":"https://orcid.org/0000-0001-8655-3746","affiliation":"Shanghai Jiao Tong University","institutionId":"I183067930","institutionUrl":"https://ror.org/0220qvk04","affiliations":["Department of Electronic Engineering and the Cooperative Medianet Innovation Center (CMIC), Shanghai Jiao Tong University, Shanghai, China"]}],"venue":"IEEE Transactions on Wireless Communications","venueId":"S63459445","publisher":"Institute of Electrical and Electronics Engineers","publisherId":"P4310319808","year":2024,"publishedDate":"2024-08-06","type":"article","citationCount":31,"referenceCount":37,"isOpenAccess":true,"sourceUrl":"https://doi.org/10.1109/twc.2024.3435864","oaUrl":"https://arxiv.org/pdf/2406.01937","pdfUrl":"https://arxiv.org/pdf/2406.01937","topics":[{"id":"T10891","name":"Radar Systems and Signal 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Engineering and the Cooperative Medianet Innovation Center (CMIC), Shanghai Jiao Tong University, Shanghai, China"]},{"name":"Weifeng Zhu","id":"openalex:A5041309751","orcid":"https://orcid.org/0000-0002-8070-3616","affiliation":"Shanghai Jiao Tong University","institutionId":"I183067930","institutionUrl":"https://ror.org/0220qvk04","affiliations":["Department of Electronic Engineering and the Cooperative Medianet Innovation Center (CMIC), Shanghai Jiao Tong University, Shanghai, China"]},{"name":"Meixia Tao","id":"openalex:A5016127527","orcid":"https://orcid.org/0000-0002-0799-0954","affiliation":"Shanghai Jiao Tong University","institutionId":"I183067930","institutionUrl":"https://ror.org/0220qvk04","affiliations":["Department of Electronic Engineering and the Cooperative Medianet Innovation Center (CMIC), Shanghai Jiao Tong University, Shanghai, China"]},{"name":"Shu Dong Sun","id":"openalex:A5014646698","orcid":"https://orcid.org/0000-0001-8655-3746","affiliation":"Shanghai Jiao Tong University","institutionId":"I183067930","institutionUrl":"https://ror.org/0220qvk04","affiliations":["Department of Electronic Engineering and the Cooperative Medianet Innovation Center (CMIC), Shanghai Jiao Tong University, Shanghai, China"]}],"venue":"IEEE Transactions on Wireless Communications","venueId":"S63459445","publisher":"Institute of Electrical and Electronics Engineers","publisherId":"P4310319808","year":2023,"publishedDate":"2023-09-05","type":"article","citationCount":29,"referenceCount":33,"isOpenAccess":true,"sourceUrl":"https://doi.org/10.1109/twc.2023.3309327","oaUrl":"https://arxiv.org/pdf/2308.12198","pdfUrl":"https://arxiv.org/pdf/2308.12198","topics":[{"id":"T10936","name":"Millimeter-Wave Propagation and Modeling","score":0.9994999766349792,"subfieldId":"2208","subfield":"Electrical and Electronic Engineering","fieldId":"22","field":"Engineering"},{"id":"T11458","name":"Advanced Wireless Communication 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Shao","id":"openalex:A5007320479","orcid":"https://orcid.org/0000-0003-3249-8045","affiliation":"University of Waterloo","institutionId":"I151746483","institutionUrl":"https://ror.org/01aff2v68","affiliations":["Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada"]},{"name":"Shu Dong Sun","id":"openalex:A5014646698","orcid":"https://orcid.org/0000-0001-8655-3746","affiliation":"Shanghai Jiao Tong University","institutionId":"I183067930","institutionUrl":"https://ror.org/0220qvk04","affiliations":["Department of Electronic Engineering and the Cooperative Medianet Innovation Center (CMIC), Shanghai Jiao Tong University, Shanghai, China"]},{"name":"Meixia Tao","id":"openalex:A5016127527","orcid":"https://orcid.org/0000-0002-0799-0954","affiliation":"Shanghai Jiao Tong University","institutionId":"I183067930","institutionUrl":"https://ror.org/0220qvk04","affiliations":["Department of Electronic Engineering and the Cooperative Medianet Innovation Center (CMIC), Shanghai Jiao Tong University, Shanghai, China"]},{"name":"Rui Zhang","id":"openalex:A5100422102","orcid":"https://orcid.org/0000-0002-8729-8393","affiliation":"Shenzhen Research Institute of Big Data","institutionId":"I4210099586","institutionUrl":"https://ror.org/00z1gwf89","affiliations":["School of Science and Engineering, Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, Guangdong, China","Shenzhen Research Institute of Big Data, School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong, China"]}],"venue":"IEEE Transactions on Wireless Communications","venueId":"S63459445","publisher":"Institute of Electrical and Electronics 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