Fairness Optimization in Next-Generation Dense WLANs with ISAC

Dense wireless local area networks (WLANs) have been developed to enable high-capacity indoor wireless communications. Nevertheless, due to the inherent interference among densely deployed access points (APs) and the competitive channel access scheme, the balance of network load among APs may degrade significantly. This challenge motivates the development of a novel association policy exploiting the integrated sensing and communication (ISAC) technique to enhance network fairness. In this paper, we propose a novel optimization framework for ISAC resource allocation in the downlink of dense WLANs to maximize the total network fairness utility function while guaranteeing users' quality-of-service (QoS) requirements. Unlike conventional association policies, our proposed approach effectively determines the associated APs based on signal-to-noise ratio (SNR) and measured angle of sensing signals to ensure network fairness. By leveraging coalition game and binary relaxation techniques, we further transform the non-convex resource allocation design problem and address them via the alternating optimization (AO) technique. Simulation results demonstrate that the proposed ISAC-based resource allocation framework can effectively improve the data rate over the network and, simultaneously ensure fairness among stations.