Precoding Optimization for MIMO-OFDM Integrated Sensing and Communication Systems

Abstract

To meet the increasing demands of high sensing accuracy and high data rate in the intelligent applications of forthcoming 6th generation (6G) mobile communication systems, a precoding optimization scheme is presented for multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) integrated sensing and communication (ISAC) systems. We employ the precoding matrix as the decision variable, the sensing mutual information (MI) as the objective function, and pre-defined signal-to-interference-plus-noise ratio (SINR) levels for each communication user equipment (UE) and transmit power budget as constraints to formulate the MIMO-OFDM ISAC precoding optimization model. We further propose a rank-1 optimization algorithm, which converts the non-convex optimization problem to a semidefinite program problem using the semidefinite relaxation method and obtains the globally optimal rank-1 solution. Compared to existing benchmark method, our proposed algorithm achieves a superior performance tradeoff between sensing and communication, resulting in higher sensing MI and more focused transmit beam energy in the target direction of the radar beampattern while effectively suppressing interference and noise. We conduct extensive simulations to demonstrate the feasibility and effectiveness of our presented precoding optimization scheme for MIMO-OFDM ISAC systems, which leads to an improvement in sensing MI by about 40% compared to the benchmark method.