A Two-Layer Iterative Algorithm for Max-Min Rate Optimization in IRS Assisted Multiuser Systems With Improper Gaussian Signaling

In this paper, we consider an intelligent reflecting surface (IRS) assisted downlink multiuser communication system with improper Gaussian signaling (IGS) that serves as generalized Gaussian signaling and can effectively combat multiuser interference. We focus on the max-min achievable rate optimiza...

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Veröffentlicht in:IEEE transactions on communications Jg. 72; H. 12; S. 7596 - 7610
Hauptverfasser: Fang, Junjie, Zhang, Chao, Wu, Qingqing, Zeng, Yong, Shi, Qingjiang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.12.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
alternating direction method of multipliers (ADMM)
Array signal processing
Beamforming
Closed form solutions
Communications systems
Complexity
Convexity
Downlink
Gaussian beams (optics)
improper Gaussian signaling
intelligent reflecting surface
Interference
Iterative algorithms
Iterative methods
low-complexity
Lower bounds
Max-min rate optimization
Minimax techniques
MISO communication
Multiuser systems
Optimization
Reconfigurable intelligent surfaces
Software
Wireless communication
ISSN:0090-6778, 1558-0857
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Zusammenfassung:In this paper, we consider an intelligent reflecting surface (IRS) assisted downlink multiuser communication system with improper Gaussian signaling (IGS) that serves as generalized Gaussian signaling and can effectively combat multiuser interference. We focus on the max-min achievable rate optimization problem by jointly optimizing the transmit beamforming vectors and reflecting phase shifts, subject to the transmit power budget constraint at the access point (AP). We propose a low-complexity iterative algorithm based on a two-layer iterative procedure, which differs from these existing algorithms that rely on inefficient alternating optimization framework and high computational complexity convex optimization tools. Specifically, in the outer layer procedure, we employ a tractable lower bound of user communication rate to reformulate the original problem and repeatedly update the lower bound in each iteration. In the inner layer procedure, based on the alternating direction method of multipliers (ADMM), we decompose the reformulated problem into several convex sub-problems, which can be alternately solved by closed-form solutions. Furthermore, we study the initialization, convergence, and computational complexity of the proposed algorithm. Additionally, we simplify the algorithm to make it applicable for the cases of conventional proper Gaussian signaling (PGS) and without IRS. Finally, numerical results validate the advantages of the proposed algorithm over benchmarking algorithm in terms of rate performance and average execution time.
Bibliographie:ObjectType-Article-1
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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2024.3422170