A Smaller Subspace is All You Need: Efficient ISAC Beamforming via Dimensionality Reduction
Abstract
Integrated sensing and communication (ISAC) has been recognized as a critical technology for the sixth generation (6G) wireless networks. Under monostatic sensing mode, achieving a balance between communication and sensing performance via optimal beamforming is a critical research problem. In multiple input multiple output (MIMO) systems, such beamforming problems are of substantial computational complexity due to the large number of antennas. Existing methods exploit exact subspaces of lower dimensions to speed up the beamforming process where the complexity is proportional to the number of communication users and sensing targets instead of antennas. In this paper, it is demonstrated that the beamforming problem can be solved in an even lower-dimensional subspace with only a small sensing performance loss. Numerical results compare the sensing performance and computational complexity of ISAC beamforming problem in the exact subspace and smaller subspaces.
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BibTeX
@misc{liu2026smaller,
title = {{A Smaller Subspace is All You Need: Efficient ISAC Beamforming via Dimensionality Reduction}},
author = {Ruiqi Liu and Yuanshuo Gang and Tianqi Mao and Qingqing Wu},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.23462},
eprint = {2610.23462},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.23462},
}
RIS
TY - UNPB
TI - A Smaller Subspace is All You Need: Efficient ISAC Beamforming via Dimensionality Reduction
AU - Ruiqi Liu
AU - Yuanshuo Gang
AU - Tianqi Mao
AU - Qingqing Wu
PY - 2026
DA - 2026/10/16
PB - arXiv
DO - 10.48550/arxiv.2610.23462
UR - https://arxiv.org/abs/2610.23462
AB - Integrated sensing and communication (ISAC) has been recognized as a critical technology for the sixth generation (6G) wireless networks. Under monostatic sensing mode, achieving a balance between communication and sensing performance via optimal beamforming is a critical research problem. In multiple input multiple output (MIMO) systems, such beamforming problems are of substantial computational complexity due to the large number of antennas. Existing methods exploit exact subspaces of lower dimensions to speed up the beamforming process where the complexity is proportional to the number of communication users and sensing targets instead of antennas. In this paper, it is demonstrated that the beamforming problem can be solved in an even lower-dimensional subspace with only a small sensing performance loss. Numerical results compare the sensing performance and computational complexity of ISAC beamforming problem in the exact subspace and smaller subspaces.
ER -