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1. Hybrid Ris-Assisted Interference Mitigation for Spectrum Sharing

   https://doi.org/10.1109/ICASSP49357.2023.10096639  

   Wang, Fangzhou; Swindlehurst, A. Lee (June 2023, ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   This paper explores reconfigurable intelligent surfaces (RIS) for mitigating cross-system interference in spectrum sharing applications. Unlike conventional reflect-only RIS that can only adjust the phase of the incoming signal, a hybrid RIS is considered that can configure the phase and modulus of the impinging signal by absorbing part of the signal energy. We investigate two spectrum sharing scenarios: (1) Spectral coexistence of radar and communication systems, where a convex optimization problem is formulated to minimize the Frobenius norm of the channel matrix from the communication base station to the radar receiver, and (2) Spectrum sharing in device-to-device (D2D) communications, where a max-min scheme that optimizes the worst-case signal-to-interference-plus-noise ratio (SINR) among the D2D links is formulated, and then solved through fractional programming. Numerical results show that with a sufficient number of elements, the hybrid RIS can in many cases completely eliminate the interference, unlike a conventional non-absorptive RIS. 

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2. Applications of Absorptive Reconfigurable Intelligent Surfaces in Interference Mitigation and Physical Layer Security

   https://doi.org/10.1109/TWC.2023.3312693  

   Wang, Fangzhou; Swindlehurst, A Lee (May 2024, IEEE Transactions on Wireless Communications)

   This paper explores the use of reconfigurable intelligent surfaces (RIS) in mitigating cross-system interference in spectrum sharing and secure wireless applications. Unlike conventional RIS that can only adjust the phase of the incoming signal and essentially reflect all impinging energy, or active RIS, which also amplify the reflected signal at the cost of significantly higher complexity, noise, and power consumption, an absorptive RIS (ARIS) is considered. An ARIS can in principle modify both the phase and modulus of the impinging signal by absorbing a portion of the signal energy, providing a compromise between its conventional and active counterparts in terms of complexity, power consumption, and degrees of freedom (DoFs). We first use a toy example to illustrate the benefit of ARIS, and then we consider three applications: 1) spectral coexistence of radar and communication systems, where a convex optimization problem is formulated to minimize the Frobenius norm of the channel matrix from the communication base station to the radar receiver; 2) spectrum sharing in device-to-device (D2D) communications, where a max-min scheme that maximizes the worst-case signal-to-interference-plus-noise ratio (SINR) among the D2D links is developed and then solved via fractional programming; 3) physical layer security of a downlink communication system, where the secrecy rate is maximized and the resulting nonconvex problem is solved by a fractional programming algorithm together with a sequential convex relaxation procedure. Numerical results are then presented to show the significant benefit of ARIS in these applications. 

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3. Distributed Data-Driven Learning-Based Optimal Dynamic Resource Allocation for Multi-RIS-Assisted Multi-User Ad-Hoc Network

   https://doi.org/10.3390/a17010045  

   Zhang, Yuzhu; Xu, Hao (January 2024, Algorithms)

   This study investigates the problem of decentralized dynamic resource allocation optimization for ad-hoc network communication with the support of reconfigurable intelligent surfaces (RIS), leveraging a reinforcement learning framework. In the present context of cellular networks, device-to-device (D2D) communication stands out as a promising technique to enhance the spectrum efficiency. Simultaneously, RIS have gained considerable attention due to their ability to enhance the quality of dynamic wireless networks by maximizing the spectrum efficiency without increasing the power consumption. However, prevalent centralized D2D transmission schemes require global information, leading to a significant signaling overhead. Conversely, existing distributed schemes, while avoiding the need for global information, often demand frequent information exchange among D2D users, falling short of achieving global optimization. This paper introduces a framework comprising an outer loop and inner loop. In the outer loop, decentralized dynamic resource allocation optimization has been developed for self-organizing network communication aided by RIS. This is accomplished through the application of a multi-player multi-armed bandit approach, completing strategies for RIS and resource block selection. Notably, these strategies operate without requiring signal interaction during execution. Meanwhile, in the inner loop, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm has been adopted for cooperative learning with neural networks (NNs) to obtain optimal transmit power control and RIS phase shift control for multiple users, with a specified RIS and resource block selection policy from the outer loop. Through the utilization of optimization theory, distributed optimal resource allocation can be attained as the outer and inner reinforcement learning algorithms converge over time. Finally, a series of numerical simulations are presented to validate and illustrate the effectiveness of the proposed scheme. 

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4. WMMSE-Based Rate Maximization for RIS-Assisted MU-MIMO Systems

   https://doi.org/10.1109/TCOMM.2024.3381707  

   Choi, Hyuckjin; Swindlehurst, A Lee; Choi, Junil (August 2024, IEEE Transactions on Communications)

   Reconfigurable intelligent surface (RIS) technology, given its ability to favorably modify wireless communication environments, will play a pivotal role in the evolution of future communication systems. This paper proposes rate maximization techniques for both single-user and multiuser MIMO systems, based on the well-known weighted minimum mean square error (WMMSE) criterion. Using a suitable weight matrix, the WMMSE algorithm tackles an equivalent weighted mean square error (WMSE) minimization problem to achieve the sum-rate maximization. By considering a more practical RIS system model that employs a tensor-based representation enforced by the electromagnetic behavior exhibited by the RIS panel, we detail both the sum-rate maximizing and WMSE minimizing strategies for RIS phase shift optimization by deriving the closed-form gradient of the sum-rate and the WMSE with respect to the RIS phase shift vector. Our simulations reveal that the proposed rate maximization technique, rooted in the WMMSE algorithm, exhibits superior performance when compared to other benchmarks. 

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5. Joint Downlink and Uplink Optimization for RIS-Aided FDD MIMO Communication Systems

   https://doi.org/10.1109/TWC.2024.3358291  

   Lee, Gyoseung; Lee, Hyeongtaek; Kim, Donghwan; Chung, Jaehoon; Swindlehurst, A Lee; Choi, Junil (August 2024, IEEE Transactions on Wireless Communications)

   This paper investigates reconfigurable intelligent surface (RIS)-aided frequency division duplexing (FDD) communication systems. Since the downlink and uplink signals are simultaneously transmitted in FDD, the phase shifts at the RIS should be designed to support both transmissions. Considering a single-user multiple-input multiple-output system, we formulate a weighted sum-rate maximization problem to jointly maximize the downlink and uplink system performance. To tackle the non-convex optimization problem, we adopt an alternating optimization (AO) algorithm, in which two phase shift optimization techniques are developed to handle the unit-modulus constraints induced by the reflection coefficients at the RIS. The first technique exploits the manifold optimization-based algorithm, while the second uses a lower-complexity AO approach. Numerical results verify that the proposed techniques rapidly converge to local optima and significantly improve the overall system performance compared to existing benchmark schemes. 

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