Sparsity Exploitation via Joint Receive Processing and Transmit Beamforming Design for MIMO-OFDM ISAC Systems

TL;DR

This work addresses efficient ISAC design by exploiting echo sparsity in a MIMO-OFDM system to reduce sensing resource overhead while maintaining sensing fidelity.It introduces a CS-assisted target parameter estimation approach to shrink the number of sensing subcarriers and a joint transmit beamforming design that maximizes the downlink sum-rate under sensing SNR guarantees and a power limit.An MM–FP–neADMM framework is developed to solve the resulting high-dimensional non-convex optimization with closed-form subproblem solutions and convergence guarantees.Numerical results show substantial gains in communication throughput and accurate sensing performance, validating the effectiveness of the sparsity-exploitation strategy for practical ISAC deployments.

Abstract

Integrated sensing and communication (ISAC) is widely recognized as a pivotal enabling technique for the advancement of future wireless networks. This paper aims to efficiently exploit the inherent sparsity of echo signals for the multi-input-multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) based ISAC system. A novel joint receive echo processing and transmit beamforming design is presented to achieve this goal. Specifically, we first propose a compressive sensing (CS)-assisted estimation approach to facilitate ISAC receive echo processing, which can not only enable accurate recovery of target information, but also allow substantial reduction in the number of sensing subcarriers to be sampled and processed. Then, based on the proposed CS-assisted processing method, the associated transmit beamforming design is formulated with the objective of maximizing the sum-rate of multiuser communications while satisfying the transmit power budget and ensuring the received signal-to-noise ratio (SNR) for the designated sensing subcarriers. In order to address the formulated non-convex problem involving high-dimensional variables, an effective iterative algorithm employing majorization minimization (MM), fractional programming (FP), and the nonlinear equality alternative direction method of multipliers (neADMM) with closed-form solutions has been developed. Finally, extensive numerical simulations are conducted to verify the effectiveness of the proposed algorithm and the superior performance of the introduced sparsity exploitation strategy.

Figures (6)

• Figure 1: Average sum-rate, the norm of the primal/dual residuals versus the number of iterations ($\Gamma_0=-5\mathrm{dB}$, $P_0=10\mathrm{W}$).
• Figure 2: Average sum-rate versus sensing received SNR requirement $\Gamma_0$ ($P_0=10\mathrm{W}$).
• Figure 3: Estimation performance versus sensing received SNR requirement $\Gamma_0$ ($P_0=10\mathrm{W}$).
• Figure 4: Average sum-rate versus the transmit power $P_0$ (sensing received SNR requirement $\Gamma_0=-5 \mathrm{dB}$).
• Figure 5: Average sum-rate versus the number of transmit antennas $N_\mathrm{t}$ ($\Gamma_0=-5 \mathrm{dB}$).