Bayesian Probability Fusion for Multi-AP Collaborative Sensing in Mobile Networks

TL;DR

This paper introduces a Bayesian probability fusion framework for multi-AP collaborative sensing in mobile networks, significantly improving estimation accuracy and reducing transmission overhead, validated through simulations and real-world experiments.

Contribution

It proposes a novel Bayesian fusion method with a PCGA algorithm for efficient, accurate target parameter estimation in multi-AP networks, incorporating prior information and addressing high-dimensional optimization.

Findings

Reduces transmission overhead by 90% compared to signal fusion

Lowers estimation error by 41% relative to parameter fusion

Achieves submeter accuracy with 50% probability in field tests

Abstract

Integrated sensing and communication is widely acknowledged as a foundational technology for next-generation mobile networks. Compared with monostatic sensing, multi-access point (AP) collaborative sensing endows mobile networks with broader, more accurate, and resilient sensing capabilities, which are critical for diverse location-based sectors. This paper focuses on collaborative sensing in multi-AP networks and proposes a Bayesian probability fusion framework for target parameter estimation using orthogonal frequency-division multiplexing waveform. The framework models multi-AP received signals as probability distributions to capture stochastic observations from channel noise and scattering coefficients. Prior information is then incorporated into the joint probability density function to cast the problem as a constrained maximum a posteriori estimation. To address the high-dimensional optimization, we develop a prior-constrained gradient ascent (PCGA) algorithm that decouples correlated parameters and performs efficient gradient updates guided by the target prior. Theoretical analysis covers optimal fusion weights for global signal-to-noise ratio maximization, PCGA convergence, and the Cramer-Rao lower bound of the estimator, with insights applicable to broader fusion schemes. Extensive numerical simulations and real-world experiments with commercial devices show the framework reduces transmission overhead by 90% versus signal fusion and lowers estimation error by 41% relative to parameter fusion. Notably, field tests achieve submeter accuracy with 50% probability in typical coverage of mmWave APs. These improvements highlight a favorable balance between communication efficiency and estimation accuracy for practical multi-AP sensing deployment. The dataset is released for research purposes and is publicly available at: http://pmldatanet.com.cn/dataapp/multimodal