Channel Modeling for Holographic MIMO-Enabled IOT Devices in 6G Networks
Bhoomi Guptaa, Shakir Khanb, Qusay Bsoulc, Fadl Dahand e f, Shakeel Ahmedg, Surya Kiran Chebroluh, Mohamed Abbas Ibrahimb, Nilufar Aminovai
- aDepartment of ITE, Maharaja Agrasen Institute of Technology, Delhi, India
- bInformation Technology Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
- cDepartment of Cybersecurity, College of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan
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Abstract
Holographic Multiple-Input Multiple-Output (HMIMO) is becoming a major enabling technology for next-generation IoT-enabled consumer devices and future 6G communications. For accurate electromagnetic (EM) wave manipulation, HMIMO uses densely packed antenna elements across a fixed physical aperture, creating an electromagnetically confined surface. This is in contrast to typical massive MIMO. For small, power-constrained consumer IoT devices, this architecture’s disruptive potential includes higher spatial resolution, extremely directional beamforming, and increased energy economy. With inter-element spacing less than half the wavelength, HMIMO arrays’ ultra-dense nature introduces substantial electromagnetic coupling that defies accepted channel assumptions based on independently dispersed and identically distributed models. The design and implementation of HMIMO-based IoT systems thus face a major challenge: realistic and computationally efficient channel modeling. We investigate four sophisticated channel modeling approaches based on electromagnetic field theory in order to address this. The first offers a precise but computationally demanding approach to modeling point-to-surface communication and is based on planar Green’s function. Particularly for far-field and near-field scenarios, the second and third methods offer trade-offs between complexity and modeling quality by using plane wave and spherical wave expansions, respectively. The last technique makes use of stochastic Green’s function to represent the unpredictability present in Rayleigh or rich-scattering situations. These models open the door for the incorporation of holographic communication into consumer devices for the Internet of Things, allowing for intelligent, context-aware, and high-capacity connectivity. Future studies must investigate how to integrate these models with AI-driven control and adaptive beamforming methods, as well as better improve them for real-world implementation.