Multi-Layer Perceptron-based Beamformer Design for Next-Generation Full-Duplex Cellular Systems

An in-band full-duplex (IBFD) multiple-input multiple-output (MIMO) radio’s self-interference (SI) and co-channel interference (CCI) cancellation strengths usually determine its performance gains over conventional half-duplex ones. Accordingly, this paper explores an alternative to traditional optimization driven design (ODD) techniques available in the literature for beamformer design in IBFD radios. In particular, to mitigate the residual SI and CCI, we propose a run-time data-driven prediction approach to predict the beamforming matrices at the uplink users and the base station. First, we formulate an ODD-based beamforming design problem, which we structurally optimize through sum-rate maximization, and cast it as a second-order cone programming problem. Then, we repeatedly solve this problem to generate a dataset forming a multiple multivariate regression problem. We use the dataset to train a multi-layer perceptron (MLP) employing a supervised learning scheme to solve the associated regression problem. Experimental results demonstrate that the MLP based beamformer design achieves a near-optimal performance at a remarkably high speed for reasonable residual SI and CCI cancellation without the need for explicit channel estimation.