Low-Complexity Tensor-Based Monostatic Sensing for IRS-Assisted Communication Systems
arXiv:2605.29164 · doi:10.1109/ISWCS61526.2024.10639156
Abstract
This paper proposes a tensor-based parameter estimation algorithm for sensing in an intelligent reflecting surface-assisted system. We present a higher-order singular value decomposition-based solution that exploits the tensor structure of the received echo signal to jointly estimate the target's delay, Doppler, and angular information. Our tensor-based solution can estimate the parameters individually at low complexity, benefiting from parallel computation. Complexity analysis is carried out in comparison with a baseline scheme that does not exploit the intrinsic multilinear structure of the sensed signal. Simulation results show that our proposed tensor-based method can achieve the same performance as the reference method while drastically reducing the computational complexity.
References in corpus (6)
- An Overview of Signal Processing Techniques for RIS/IRS-aided Wireless Systems
- Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Bound Optimization
- Spatial Diversity in Radar Detection via Active Reconfigurable Intelligent Surfaces
- Tensor-Based Channel Estimation and Data-Aided Tracking in IRS-Assisted MIMO Systems
- RIS in Cellular Networks -- Challenges and Issues
- Tensor-based modeling/estimation of static channels in IRS-assisted MIMO systems