Weighted Sum-Rate Maximization for Multi-IRS-Assisted Full-Duplex Systems With Hardware Impairments

Smart and reconfigurable wireless communication environments can be established by exploiting well-designed intelligent reflecting surfaces (IRSs) to shape the communication channels. In this paper, we investigate how multiple IRSs affect the performance of multi-user full-duplex communication syste...

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Bibliographic Details
Published in:IEEE transactions on cognitive communications and networking 2021-06, Vol.7 (2), p.466-481
Main Authors: Saeidi, Mohammad Amin, Emadi, Mohammad Javad, Masoumi, Hamed, Mili, Mohammad Robat, Ng, Derrick Wing Kwan, Krikidis, Ioannis
Format: Article
Language:English
Subjects:
Algorithms
Beamforming
Business metrics
Communication
Communications systems
Downlinking
full-duplex
Hardware
hardware impairment
Impairment
Iterative methods
Measurement
Multiple intelligent reflecting surface
Optimization
Reconfigurable hardware
Resource allocation
Shape
System performance
system weighted sum-rate maximization
Transforms
Transmitters
Wireless communication
Wireless communications
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Summary:Smart and reconfigurable wireless communication environments can be established by exploiting well-designed intelligent reflecting surfaces (IRSs) to shape the communication channels. In this paper, we investigate how multiple IRSs affect the performance of multi-user full-duplex communication systems under hardware impairment at each node, wherein the base station (BS) and the uplink users are subject to maximum transmission power constraints. Firstly, the uplink-downlink system weighted sum-rate (SWSR) is derived as a system performance metric. Then, we formulate the resource allocation design to maximize the SWSR as an optimization problem which jointly optimizes the beamforming and the combining vectors at the BS, the transmit powers of the uplink users, and the phase shifts of multiple IRSs. Since the SWSR optimization problem is non-convex, an efficient iterative alternating approach is proposed to obtain a suboptimal solution for the design problem. In particular, we first reformulate the main problem into an equivalent weighted minimum mean-square-error form and then transform it into several convex sub-problems which can be analytically solved for given phase shifts. Then, the IRSs phases are optimized via a gradient ascent-based algorithm. Finally, numerical results are presented to clarify how multiple IRSs enhance the performance metric under hardware impairment.
ISSN:2332-7731
2332-7731
DOI:10.1109/TCCN.2021.3070587