Uplink Sensing in Perceptive Mobile Networks With Asynchronous Transceivers

• Published in: IEEE transactions on signal processing Vol. 69; pp. 1287 - 1300
• Main Authors: Ni, Zhitong, Zhang, J. Andrew, Huang, Xiaojing, Yang, Kai, Yuan, Jinhong
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Ambiguity; Carrier frequencies; Cross correlation; Doppler effect; dual-functional radar-communications; Estimation; Joint communication and radar sensing; mirrored-MUSIC; OFDM; Offsets; Parameter estimation; perceptive mobile network; Radar; Redundancy; Sensors; Transceivers; Uplink; uplink sensing
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2021.3057499

Perceptive mobile network (PMN) is a recently proposed next-generation network that integrates radar sensing into communications. One major challenge for realizing sensing in PMNs is how to deal with spatially-separated asynchronous transceivers. The asynchrony between sensing receiver and transmitter will cause both timing offsets (TOs) and carrier frequency offsets (CFOs) and lead to degraded sensing accuracy in both ranging and velocity measurements. In this paper, we propose an uplink sensing scheme for PMNs with asynchronous transceivers, targeting at resolving the sensing ambiguity and improving the sensing accuracy. We first adopt a cross-antenna cross-correlation (CACC) operation to remove the sensing ambiguity associated with both TOs and CFOs. Without sensing ambiguity, both actual propagation delay and actual Doppler frequency of multiple targets can be obtained using CACC outputs. To exploit the redundancy of the CACC outputs and reduce the complexity, we then propose a novel mirrored-MUSIC algorithm, which halves the number of unknown parameters to be estimated, to obtain actual values of delays and Doppler frequencies. Finally, we propose a high-resolution angles-of-arrival (AoAs) estimation algorithm, which jointly processes all measurements from spatial, temporal, and frequency domains. The proposed AoAs estimation algorithm can achieve significantly higher estimation accuracy than that of using samples from the spatial domain only. We also derive the theoretical mean-square-error of the proposed algorithms. Numerical results are provided and validate the effectiveness of the proposed scheme.