Joint Near Field Uplink Communication and Localization Using Message Passing-Based Sparse Bayesian Learning

Fei Liu, Zhengdao Yuan, Qinghua Guo, Yuanyuan Zhang, Zhongyong Wang, J. Andrew Zhang

Abstract

This work deals with the problem of uplink communication and localization in an integrated sensing and communication system, where users are in the near field (NF) of antenna aperture due to the use of high carrier frequency and large antenna arrays at base stations. We formulate joint NF signal detection and localization as a problem of recovering signals with a sparse pattern, and we adopt differentiation modulations to avoid the frequent use of pilot signals. To solve the problem, we develop a message passing based sparse Bayesian learning (SBL) algorithm, where multiple unitary approximate message passing (UAMP)-based sparse signal estimators work jointly to recover the sparse signals with low complexity. Simulation results demonstrate the effectiveness of the proposed method.

Locations

• IEEE Transactions on Vehicular Technology - View
• arXiv (Cornell University) - View - PDF