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In this paper, we investigate an intelligent reflecting surface (IRS)assisted multi-user multiple-input multiple-output (MIMO) full-duplex (FD)system. We jointly optimize the active beamforming matrices at the access point(AP) and uplink users, and the passive beamforming matrix at the IRS tomaximize the weighted sum-rate of the system. Since it is practically difficultto acquire the channel state information (CSI) for IRS-related links due to itspassive operation and large number of elements, we conceive a mixed-timescalebeamforming scheme. Specifically, the high-dimensional passive beamformingmatrix at the IRS is updated based on the channel statistics while the activebeamforming matrices are optimized relied on the low-dimensional real-timeeffective CSI at each time slot. We propose an efficient stochastic successiveconvex approximation (SSCA)-based algorithm for jointly designing the activeand passive beamforming matrices. Moreover, due to the high computationalcomplexity caused by the matrix inversion computation in the SSCA-basedoptimization algorithm, we further develop a deep-unfolding neural network (NN)to address this issue. The proposed deep-unfolding NN maintains the structureof the SSCA-based algorithm but introduces a novel non-linear activationfunction and some learnable parameters induced by the first-order Taylorexpansion to approximate the matrix inversion. In addition, we develop ablack-box NN as a benchmark. Simulation results show that the proposedmixed-timescale algorithm outperforms the existing single-timescale algorithmand the proposed deep-unfolding NN approaches the performance of the SSCA-basedalgorithm with much reduced computational complexity when deployed online.

Deep-Unfolding Beamforming for Intelligent Reflecting Surface assisted Full-Duplex Systems