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Beyond Diagonal RIS for Multi-Band Multi-Cell MIMO Networks: A Practical Frequency-Dependent Model and Performance Analysis

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arxiv 2401.06475 v2 pith:WNYYLI66 submitted 2024-01-12 eess.SP

classification eess.SP
keywords bd-rissfrequency-dependentpracticalmodelproposedreflectionrissacross
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This paper delves into the unexplored frequency-dependent characteristics of beyond diagonal reconfigurable intelligent surfaces (BD-RISs). A generalized practical frequency-dependent reflection model is proposed as a fundamental framework for configuring fully-connected and group-connected RISs in a multi-band multi-base station (BS) multiple-input multiple-output (MIMO) network. Leveraging this practical model, multi-objective optimization strategies are formulated to maximize the received power at multiple users connected to different BSs, each operating under a distinct carrier frequency. By relying on matrix theory and exploiting the symmetric structure of the reflection matrices inherent to BD-RISs, relaxed tractable versions of the challenging problems are achieved for scenarios with obstructed and unobstructed direct channel links. The relaxed solutions are then combined with codebook-based approaches to configure the practical capacitance values for the BD-RISs. Simulation results reveal the frequency-dependent behaviors of different RIS architectures and demonstrate the effectiveness of the proposed schemes. Notably, BD-RISs exhibit high reflection performance across the intended frequency range, remarkably outperforming conventional single-connected RISs. Moreover, the proposed optimization approaches prove effective in enabling the targeted operation of BD-RISs across one or more carrier frequencies. The results also shed light on the potential for harmful interference in the absence of synchronization between RISs and adjacent BSs.

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Cited by 1 Pith paper

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  1. Efficient Channel Prediction for Beyond Diagonal RIS-Assisted MIMO Systems with Channel Aging

    eess.SP 2024-11 conditional novelty 6.0 of 10

    A Tucker2-BALS estimator plus a CNN-AR predictor achieves low-overhead, high-accuracy CSI acquisition and forecasting for fully- and group-connected BD-RIS MIMO systems.

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