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

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 det...

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Veröffentlicht in:IEEE transactions on vehicular technology Jg. 74; H. 5; S. 7666 - 7675
Hauptverfasser: Liu, Fei, Yuan, Zhengdao, Guo, Qinghua, Zhang, Yuanyuan, Wang, Zhongyong, Zhang, J. Andrew
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Antenna arrays
Antennas
Bayesian analysis
Carrier frequencies
Channel estimation
Communications systems
Estimation
joint communication and localization
Localization
Location awareness
Machine learning
Message passing
Near field
Near fields
Noise measurement
Robot learning
Signal detection
Simulation
sparse Bayesian learning (SBL)
Sparse matrices
Symbols
Uplink
Uplinking
Vectors
ISSN:0018-9545, 1939-9359
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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.
AbstractList 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.
Author Guo, Qinghua
Wang, Zhongyong
Liu, Fei
Zhang, J. Andrew
Zhang, Yuanyuan
Yuan, Zhengdao
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SubjectTerms Algorithms
Antenna arrays
Antennas
Bayesian analysis
Carrier frequencies
Channel estimation
Communications systems
Estimation
joint communication and localization
Localization
Location awareness
Machine learning
Message passing
Near field
Near fields
Noise measurement
Robot learning
Signal detection
Simulation
sparse Bayesian learning (SBL)
Sparse matrices
Symbols
Uplink
Uplinking
Vectors
Title Joint Near Field Uplink Communication and Localization Using Message Passing-Based Sparse Bayesian Learning
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