Two-stage Multiband Wi-Fi Sensing for ISAC via Stochastic Particle-Based Variational Bayesian Inference

In integrated sensing and communication (ISAC) systems, communication signals are exploited to achieve high-accuracy sensing. Multiband Wi-Fi sensing, which jointly utilizes Wi-Fi signals from multiple non-contiguous frequency bands to improve the sensing performance, has recently emerged as a promising technology for ISAC. However, the multi-dimensional non-convex likelihood function associated with the multiband WiFi sensing contains many local optimums due to the existence of high frequency components and phase distortion factors in the signal model, making it difficult to exploit the multiband gain for high-accuracy parameter estimation. To address this, we divide the target parameter estimation into two stages equipped with different signal models derived from the original model, where the first-stage coarse estimation is used to narrow down the search range for the next stage, and the second-stage refined estimation is based on the Bayesian approach to avoid the convergence to a bad local optimum of the likelihood function. Specifically, we apply the block stochastic successive convex approximation (SSCA) approach to derive a novel stochastic particle-based variational Bayesian inference (SPVBI) algorithm in the refined stage. Unlike the conventional particle-based VBI (PVBI) in which only particle probability is optimized and the per-iteration computational complexity increases exponentially with particle count, the proposed SPVBI optimizes both the position and probability of each particle, and it adopts the block SSCA to significantly improve the sampling efficiency by averaging over iterations. As such, the proposed SPVBI can achieve a better performance than the conventional PVBI with a much lower complexity. Finally, simulations verify the advantage of the proposed algorithm over various baseline algorithms.