α-Fair Multistatic ISAC Beamforming for Multi-User MIMO-OFDM Systems via Riemannian Optimization

2026-03-31

AI Summary

This work addresses the issue of perceptual unfairness in existing integrated sensing and communication (ISAC) systems, where multi-target sensing performance is biased toward geometrically favorable targets due to holistic optimization. To tackle this, the study introduces α-fairness into multistatic ISAC beamforming design for the first time, leveraging communication users as passive bistatic receivers within a multi-user MIMO-OFDM framework. The proposed approach minimizes an α-fair utility function of the Cramér-Rao lower bounds (CRLBs) for target estimation, subject to minimum user rate and transmit power constraints. A Riemannian conjugate gradient method combined with smooth penalty-based reformulation is employed to solve the resulting non-convex problem. Simulation results demonstrate that the proposed scheme effectively balances estimation accuracy across diverse targets, significantly enhancing sensing fairness while maintaining communication performance.

Abstract

This paper proposes an $α$-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 $α$-fairness utility over per-target Cramér--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.

Problem

• ISAC
• sensing fairness
• multi-user MIMO-OFDM
• Cramér-Rao lower bound
• α-fairness

Innovation

• α-fairness
• multistatic ISAC
• Riemannian optimization
• Cramér–Rao lower bound
• MIMO-OFDM