α-Fair Multistatic ISAC Beamforming for Multi-User MIMO-OFDM Systems via Riemannian Optimization
This addresses fairness in sensing for multi-user MIMO-OFDM systems, but it is incremental as it builds on existing ISAC frameworks with a new optimization approach.
The paper tackled the problem of unfair sensing in multistatic integrated sensing and communication systems by minimizing an α-fairness utility over per-target Cramér–Rao lower bounds, achieving a favorable trade-off between sensing fairness and communication performance.
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.