Efficient isac beamforming achieved with smaller subspace dimension
A Smaller Subspace is All You Need: Efficient ISAC Beamforming via Dimensionality Reduction
Information Theory
Summary
Balancing communication and sensing in wireless systems with many antennas is usually very computationally complex. The authors show that it is possible to perform the signal processing in a smaller reduced space, which drastically cuts computation time with only a slight trade-off in sensing quality. This means future wireless systems like 6G can manage communication and sensing more efficiently. Their work includes simulations comparing the performance and speed of traditional and smaller subspace methods.
What this means in practice
- •For wireless network engineers: Implement faster beamforming algorithms that run in smaller computational spaces to optimize sensing and communication in 6G base stations.
- •For mobile device developers: Design communication hardware and software that balances sensing and communication more efficiently with less processing power.
Authors
Ruiqi Liu, Yuanshuo Gang, Tianqi Mao, Qingqing Wu
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.