Antenna selection with beam squint compensation for integrated sensing and communications
Antenna selection with beam squint compensation for integrated sensing and communications
Next-generation wireless networks strive for higher communication rates, ultra-low latency, seamless connectivity, and high-resolution sensing capabilities. To meet these demands, terahertz (THz) band signal processing is envisioned as a key technology offering wide bandwidth and sub-millimeter wavelength. Furthermore, THz integrated sensing and communications (ISAC) paradigm has emerged to jointly access the spectrum and reduce the hardware costs through a unified platform. To address the challenges in THz propagation, THz-ISAC systems employ extremely large antenna arrays to improve the beamforming gain for communications with high data rates and sensing with high resolution. However, the cost and power consumption of implementing fully digital beamformers are prohibitive. While hybrid analog/digital beamforming can be a potential solution, the use of subcarrier-independent analog beamformers leads to the beam-squint phenomenon where different subcarriers observe distinct directions because of adopting the same analog beamformer across all subcarriers. In this paper, we develop a sparse array architecture for THz-ISAC with hybrid beamforming to provide a cost-effective solution. We analyze the antenna selection problem under beam-squint influence and introduce a manifold optimization approach for hybrid beamforming design. To reduce computational and memory costs, we propose novel algorithms leveraging grouped subarrays, quantized performance metrics, and sequential optimization. These approaches yield a significant reduction in the number of possible subarray configurations, which enables us to devise a neural network with classification model to accurately perform antenna selection. Numerical simulations show that the proposed approach exhibits up to 95% lower complexity for large antenna arrays while maintaining satisfactory communications with approximately 6% loss in the achievable rate.
Antenna selection, integrated sensing and communications, machine learning, massive MIMO, terahertz
857-870
Elbir, Ahmet M.
e6ed2099-e755-4a39-ab80-d79bd1a53082
Abdallah, Asmaa
86b80268-48be-4bc8-9577-c989e496e459
Celik, Abdulkadir
f8e72266-763c-4849-b38e-2ea2f50a69d0
Eltawil, Ahmed M.
5eb9e965-5ec8-4da1-baee-c3cab0fb2a72
20 March 2024
Elbir, Ahmet M.
e6ed2099-e755-4a39-ab80-d79bd1a53082
Abdallah, Asmaa
86b80268-48be-4bc8-9577-c989e496e459
Celik, Abdulkadir
f8e72266-763c-4849-b38e-2ea2f50a69d0
Eltawil, Ahmed M.
5eb9e965-5ec8-4da1-baee-c3cab0fb2a72
Elbir, Ahmet M., Abdallah, Asmaa, Celik, Abdulkadir and Eltawil, Ahmed M.
(2024)
Antenna selection with beam squint compensation for integrated sensing and communications.
IEEE Journal on Selected Topics in Signal Processing, 18 (5), .
(doi:10.1109/JSTSP.2024.3378909).
Abstract
Next-generation wireless networks strive for higher communication rates, ultra-low latency, seamless connectivity, and high-resolution sensing capabilities. To meet these demands, terahertz (THz) band signal processing is envisioned as a key technology offering wide bandwidth and sub-millimeter wavelength. Furthermore, THz integrated sensing and communications (ISAC) paradigm has emerged to jointly access the spectrum and reduce the hardware costs through a unified platform. To address the challenges in THz propagation, THz-ISAC systems employ extremely large antenna arrays to improve the beamforming gain for communications with high data rates and sensing with high resolution. However, the cost and power consumption of implementing fully digital beamformers are prohibitive. While hybrid analog/digital beamforming can be a potential solution, the use of subcarrier-independent analog beamformers leads to the beam-squint phenomenon where different subcarriers observe distinct directions because of adopting the same analog beamformer across all subcarriers. In this paper, we develop a sparse array architecture for THz-ISAC with hybrid beamforming to provide a cost-effective solution. We analyze the antenna selection problem under beam-squint influence and introduce a manifold optimization approach for hybrid beamforming design. To reduce computational and memory costs, we propose novel algorithms leveraging grouped subarrays, quantized performance metrics, and sequential optimization. These approaches yield a significant reduction in the number of possible subarray configurations, which enables us to devise a neural network with classification model to accurately perform antenna selection. Numerical simulations show that the proposed approach exhibits up to 95% lower complexity for large antenna arrays while maintaining satisfactory communications with approximately 6% loss in the achievable rate.
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Published date: 20 March 2024
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© 2024 IEEE.
Keywords:
Antenna selection, integrated sensing and communications, machine learning, massive MIMO, terahertz
Identifiers
Local EPrints ID: 504501
URI: http://eprints.soton.ac.uk/id/eprint/504501
ISSN: 1932-4553
PURE UUID: 42ce30e8-33d3-4fb8-8ba7-a8d32b7d43fa
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Date deposited: 10 Sep 2025 15:41
Last modified: 11 Sep 2025 03:49
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Contributors
Author:
Ahmet M. Elbir
Author:
Asmaa Abdallah
Author:
Abdulkadir Celik
Author:
Ahmed M. Eltawil
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