{\alpha}-Fair Multistatic ISAC Beamforming for Multi-User MIMO-OFDM Systems via Riemannian Optimization

TL;DR

This paper introduces an $ ext{ extalpha}$-fair multistatic ISAC framework for MIMO-OFDM systems, optimizing sensing fairness and communication performance using Riemannian optimization techniques.

Contribution

It formulates a novel $ ext{ extalpha}$-fair utility optimization over CRLBs and solves it with Riemannian conjugate gradient, addressing fairness in sensing and communication.

Findings

The proposed scheme effectively balances sensing fairness and communication performance.

Simulation results demonstrate improved fairness and system trade-offs.

The RCG method efficiently solves the non-convex optimization problem.

Abstract

This paper proposes an $\alpha$-fair multistatic integrated sensing and communication (ISAC) framework for multi-user multi-input multi-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems, where communication users act as passive bistatic receivers to enable multistatic sensing. Unlike existing works that optimize aggregate sensing metrics and thus favor geometrically advantageous targets, we minimize the $\alpha$-fairness utility over per-target Cram\'{e}r--Rao lower bounds (CRLBs) subject to per-user minimum data rate and transmit power constraints. The resulting non-convex problem is solved via the Riemannian conjugate gradient (RCG) method with a smooth penalty reformulation. Simulation results validate the effectiveness of the proposed scheme in achieving a favorable sensing fairness--communication trade-off.