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Resource Allocation for IRS-assisted Full-Duplex Cognitive Radio Systems
In this paper, 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 sum rate 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 sum rate 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.Comment: 30 pages, 8 figures, submitted for potential publicatio