A Two-stage Multiband WiFi Sensing Scheme via Stochastic Particle-Based Variational Bayesian Inference

TL;DR

This paper introduces a two-stage multiband WiFi sensing scheme that combines coarse estimation with a novel stochastic particle-based variational Bayesian inference algorithm to improve parameter estimation accuracy in complex signal models.

Contribution

It proposes a new two-stage approach with a stochastic particle-based VBI algorithm that enhances sampling efficiency and avoids local optima in multiband WiFi sensing.

Findings

The proposed SPVBI algorithm outperforms baseline methods in simulations.

The two-stage scheme improves parameter estimation accuracy.

The method effectively handles high-dimensional signal models.

Abstract

Multiband fusion enhances WiFi sensing by jointly utilizing signals from multiple non-contiguous frequency bands. However, in the multi-band WiFi sensing signal model, there are many local optimums in the associated likelihood function due to the existence of high frequency component and phase distortion factors, posing challenges for high-accuracy parameter estimation. To address this, we propose a two-stage scheme equipped with different signal models derived from the original model, where the first-stage coarse estimation is performed using a weighted root MUSIC algorithm to narrow down the search range for the subsequent stage, and the second-stage refined estimation utilizes a Bayesian approach to avoid convergence to bad suboptimal solutions. 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 conventional particle-based VBI (PVBI) that optimizes only particle probability and incurs exponential per-iteration complexity with particle count, our more flexible SPVBI algorithm optimizes both the position and probability of each particle. Additionally, it utilizes block SSCA to significantly improve sampling efficiency by averaging over iterations, making it suitable for high-dimensional problems. Extensive simulations demonstrate the superiority of our proposed algorithm over various baseline methods.