Sparse MIMO-OFDM Channel Estimation via RKHS Regularization
James Delfeld, Gian Marti, Chris Dick

TL;DR
This paper introduces a novel RKHS-based regularization method for sparse MIMO-OFDM channel estimation, leveraging band-sparsity and a low-dimensional approximation to improve accuracy and computational efficiency.
Contribution
It develops a new RKHS regularization framework for sparse channel estimation, including a low-dimensional surrogate and a deep-unfolding extension for enhanced performance.
Findings
Significantly outperforms linear LMMSE methods in estimation accuracy
Achieves quasi-linear computational complexity per iteration
Effective in ray-traced channel simulations
Abstract
We propose a method for channel estimation in multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) wireless communication systems. The method exploits the band-sparsity of wireless channels in the delay-beamspace domain by solving a regularized optimization problem in a reproducing kernel Hilbert space (RKHS). A suitable representer theorem allows us to transform the infinite-dimensional optimization problem into a finite-dimensional one, which we then approximate with a low-dimensional surrogate. We solve the resulting optimization problem using a forward-backward splitting (FBS)-based algorithm. By exploiting the problem's modulation structure, we achieve a computational complexity per iteration that is quasi-linear in the number of unknown variables. We also propose a data-driven deep-unfolding based extension to improve the performance at a reduced…
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Taxonomy
TopicsAdvanced Wireless Communication Techniques · Advanced MIMO Systems Optimization · Sparse and Compressive Sensing Techniques
