Integrated sensing and communication with mmWave massive MIMO: a compressed sampling perspective
Integrated sensing and communication with mmWave massive MIMO: a compressed sampling perspective
Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for realizing future wireless systems. In this paper, we propose an ISAC processing framework relying on millimeter-wave (mmWave)
massive multiple-input multiple-output (MIMO) systems. Specifically, we provide a compressed sampling (CS) perspective to facilitate ISAC processing, which can not only recover the highdimensional channel state information or/and radar imaging information, but also significantly reduce pilot overhead. First, an energy-efficient widely spaced array (WSA) architecture is tailored for the radar receiver, which enhances the angular resolution of radar sensing at the cost of angular ambiguity. Then, we propose an ISAC frame structure for time-varying ISAC systems considering different timescales. The pilot waveforms are judiciously designed by taking into account both CS theories and hardware constraints induced by hybrid beamforming (HBF) architecture. Next, we design the dedicated dictionary for WSA that serves as a building block for formulating the ISAC processing as sparse signal recovery problems. The orthogonal matching pursuit with support refinement (OMP-SR) algorithm is proposed to effectively solve the problems in the existence of the angular ambiguity. We also provide a framework for estimating the Doppler frequencies during payload data transmission to guarantee communication performances. Simulation results
demonstrate the good performances of both communications and radar sensing under the proposed ISAC framework.
Array signal processing, Integrated sensing and communication (ISAC), Millimeter wave communication, Radar, Radar antennas, Radar imaging, Sensors, Wireless communication, compressive sensing (CS), dual-functional radar-communication (DFRC), hybrid beamforming (HBF) architecture, massive MIMO, mmWave
11536-1276
Gao, Zhen
e0ab17e4-5297-4334-8b64-87924feb7876
Wan, Ziwei
c4126aa2-3e83-4c0d-a83c-5ab1048545c2
Zheng, Dezhi
da0121e5-27b5-4e40-ac8b-ec8b10160fce
Tan, Shufeng
32a0e967-7517-4d7e-b44c-ca9f1c7c5d4c
Masouros, Christos
b006d653-8a58-4021-8235-415c8ac18eb9
Ng, Derrick Wing Kwan
0b489020-9ba1-4ce9-b0f6-8b5c314bd45a
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
21 September 2022
Gao, Zhen
e0ab17e4-5297-4334-8b64-87924feb7876
Wan, Ziwei
c4126aa2-3e83-4c0d-a83c-5ab1048545c2
Zheng, Dezhi
da0121e5-27b5-4e40-ac8b-ec8b10160fce
Tan, Shufeng
32a0e967-7517-4d7e-b44c-ca9f1c7c5d4c
Masouros, Christos
b006d653-8a58-4021-8235-415c8ac18eb9
Ng, Derrick Wing Kwan
0b489020-9ba1-4ce9-b0f6-8b5c314bd45a
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Gao, Zhen, Wan, Ziwei, Zheng, Dezhi, Tan, Shufeng, Masouros, Christos, Ng, Derrick Wing Kwan and Chen, Sheng
(2022)
Integrated sensing and communication with mmWave massive MIMO: a compressed sampling perspective.
IEEE Transactions on Wireless Communications, 22 (3), .
(doi:10.1109/TWC.2022.3206614).
Abstract
Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for realizing future wireless systems. In this paper, we propose an ISAC processing framework relying on millimeter-wave (mmWave)
massive multiple-input multiple-output (MIMO) systems. Specifically, we provide a compressed sampling (CS) perspective to facilitate ISAC processing, which can not only recover the highdimensional channel state information or/and radar imaging information, but also significantly reduce pilot overhead. First, an energy-efficient widely spaced array (WSA) architecture is tailored for the radar receiver, which enhances the angular resolution of radar sensing at the cost of angular ambiguity. Then, we propose an ISAC frame structure for time-varying ISAC systems considering different timescales. The pilot waveforms are judiciously designed by taking into account both CS theories and hardware constraints induced by hybrid beamforming (HBF) architecture. Next, we design the dedicated dictionary for WSA that serves as a building block for formulating the ISAC processing as sparse signal recovery problems. The orthogonal matching pursuit with support refinement (OMP-SR) algorithm is proposed to effectively solve the problems in the existence of the angular ambiguity. We also provide a framework for estimating the Doppler frequencies during payload data transmission to guarantee communication performances. Simulation results
demonstrate the good performances of both communications and radar sensing under the proposed ISAC framework.
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Accepted/In Press date: 9 September 2022
Published date: 21 September 2022
Additional Information:
Publisher Copyright:
IEEE
Keywords:
Array signal processing, Integrated sensing and communication (ISAC), Millimeter wave communication, Radar, Radar antennas, Radar imaging, Sensors, Wireless communication, compressive sensing (CS), dual-functional radar-communication (DFRC), hybrid beamforming (HBF) architecture, massive MIMO, mmWave
Identifiers
Local EPrints ID: 470392
URI: http://eprints.soton.ac.uk/id/eprint/470392
ISSN: 1536-1276
PURE UUID: fae2c807-e5c2-430f-b98f-bb035309aff0
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Date deposited: 07 Oct 2022 16:50
Last modified: 16 Mar 2024 22:12
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Contributors
Author:
Zhen Gao
Author:
Ziwei Wan
Author:
Dezhi Zheng
Author:
Shufeng Tan
Author:
Christos Masouros
Author:
Derrick Wing Kwan Ng
Author:
Sheng Chen
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