Hybrid Spherical- and Planar-Wave Channel Modeling and Estimation for Terahertz Integrated UM-MIMO and IRS Systems
Yuhang Chena, Renwang Lib, Chong Zhao Hanc, Shu Dong Sunb d, Meixia Taob
- aTerahertz Wireless Communications (TWC) Laboratory, Shanghai Jiao Tong University, Shanghai, China
- bDepartment of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China
- cDepartment of Electronic Engineering, and the Cooperative Medianet Innovation Center (CMIC), Terahertz Wireless Communications (TWC) Laboratory, Shanghai Jiao Tong University, Shanghai, China
- dShanghai Key Laboratory of Digital Media Processing and Transmission, Shanghai, China
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Abstract
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.
TL;DRA hybrid spherical- and planar-wave channel model (HSPM) is developed for the cascaded channel of the integrated system and 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. (Semantic Scholar)