Computer Science > Information Theory
[Submitted on 28 Sep 2021 (v1), last revised 25 Apr 2022 (this version, v2)]
Title:Intelligent Reflecting Surface Aided Wireless Networks: From Single-Reflection to Multi-Reflection Design and Optimization
View PDFAbstract:Intelligent reflecting surface (IRS) has emerged as a promising technique for wireless communication networks. By dynamically tuning the reflection amplitudes/phase shifts of a large number of passive elements, IRS enables flexible wireless channel control and configuration, and thereby enhances the wireless signal transmission rate and reliability significantly. Despite the vast literature on designing and optimizing assorted IRS-aided wireless systems, prior works have mainly focused on enhancing wireless links with single signal reflection only by one or multiple IRSs, which may be insufficient to boost the wireless link capacity under some harsh propagation conditions (e.g., indoor environment with dense blockages/obstructions). This issue can be tackled by employing two or more IRSs to assist each wireless link and jointly exploiting their single as well as multiple signal reflections over them. However, the resultant double-/multi-IRS aided wireless systems face more complex design issues as well as new practical challenges for implementation as compared to the conventional single-IRS counterpart, in terms of IRS reflection optimization, channel acquisition, as well as IRS deployment and association/selection. As such, a new paradigm for designing multi-IRS cooperative passive beamforming and joint active/passive beam routing arises which calls for innovative design approaches and optimization methods. In this paper, we give a tutorial overview of multi-IRS aided wireless networks, with an emphasis on addressing the new challenges due to multi-IRS signal reflection and routing. Moreover, we point out important directions worthy of research and investigation in the future.
Submission history
From: Weidong Mei [view email][v1] Tue, 28 Sep 2021 11:58:59 UTC (930 KB)
[v2] Mon, 25 Apr 2022 12:54:06 UTC (923 KB)
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