Full Duplex Transmit and Receive Beamforming with Block-Sparse Antenna Selection for Multi-User Massive MIMO

Through simultaneous downlink and uplink transmission on the same frequency slot, in-band full duplex has the potential to double the spectral efficiency of communication systems, however the potential is difficult to realize due to the strong self interference (SI). The great number of antenna elements in massive MIMO has made spatial SI suppression an promising solution to SI suppression but this approach is challenged by the coupling of the transmit and receive beamforming problems. This paper applies a combined beamforming and reduced connectivity antenna selection approach to suppress SI while maintaining user directivity and reduce switching complexity. To solve the non-convex beamforming problem, Regularized Joint Linearly Constrained Minimum Variance (RJLCMV) is proposed which leverages disappearing regularization to provide deep SI nulling while avoiding the self-nulling problem. To solve the nonconvex joint group antenna selection, we pose the problem as a block-sparse recovery problem and propose Hard-Thresholding Pursuit-based Joint Group Antenna Selection (HTP-JGAS), an iterative method based on compressed sensing. Using measured SI channel data, RJLCMV decreases the probability of deep self-nulling by 49% compared to a standard alternating approach. By leveraging HTP-JGAS with RJLCMV, the probability of deep nulling is nearly eliminated compared to a sub-connected approach while the run-time is over two orders of magnitude faster than existing nature inspired approaches. Furthermore, it is demonstrated that the proposed partial switching connectivity does not substantially reduce performance while providing a great reduction in hardware complexity.