4.7 Article

Resource Allocation for IRS-Assisted Full-Duplex Cognitive Radio Systems

Journal

IEEE TRANSACTIONS ON COMMUNICATIONS
Volume 68, Issue 12, Pages 7376-7394

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCOMM.2020.3020838

Keywords

Interference; Resource management; Optimization; Array signal processing; Cognitive radio; Quality of service; Block coordinate descent; imperfect channel state information; intelligent reflecting surface; cognitive radio; full-duplex

Funding

  1. Chinese Scholarship Council (CSC)
  2. Alexander von Humboldt Foundation
  3. UNSW Digital Grid Futures Institute, UNSW, Sydney, under a cross-disciplinary fund scheme
  4. Australian Research Council's [DP190101363]

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In this article, we investigate the resource allocation design for intelligent reflecting surface (IRS)-assisted full-duplex (FD) cognitive radio systems. In particular, a secondary network employs an FD base station (BS) for serving multiple half-duplex downlink (DL) and uplink (UL) users simultaneously. An IRS is deployed to enhance the performance of the secondary network while helping to mitigate the interference caused to the primary users (PUs). The DL transmit beamforming vectors and the UL receive beamforming vectors at the FD BS, the transmit power of the UL users, and the phase shift matrix at the IRS are jointly optimized for maximization of the total spectral efficiency of the secondary system. The design task is formulated as a non-convex optimization problem taking into account the imperfect knowledge of the PUs' channel state information (CSI) and their maximum interference tolerance. Since the maximum interference tolerance constraint is intractable, we apply a safe approximation to transform it into a convex constraint. To efficiently handle the resulting approximated optimization problem, which is still non-convex, we develop an iterative block coordinate descent (BCD)-based algorithm. This algorithm exploits semidefinite relaxation, a penalty method, and successive convex approximation and is guaranteed to converge to a stationary point of the approximated optimization problem. Our simulation results do not only reveal that the proposed scheme yields a substantially higher system spectral efficiency for the secondary system than several baseline schemes, but also confirm its robustness against CSI uncertainty. Besides, our results illustrate the tremendous potential of IRS for managing the various types of interference arising in FD cognitive radio networks.

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