Reconsidering Sparse Sensing Techniques for Channel Sounding Using Splicing

Multi-band splicing offers a promising solution to extend existing band-limited communication systems to support high-precision sensing applications. This technique involves performing narrow-band measurements at multiple center frequencies, which are then combined to effectively increase the bandwidth without changing the sampling rate. In this paper, we introduce a mmWave channel sounder based on multi-band splicing, leveraging the sparse nature of wireless channels through compressed sensing and sparse recovery techniques for channel reconstruction. We focus on three sparse recovery methods: the widely used grid-based orthogonal matching pursuit (OMP) algorithm as a baseline, our newly developed two-stage mmSplicer algorithm, which extends the OMP method by introducing an additional stage for improving its performance for our application, and our adaptation of sparse reconstruction by separable approximation (SpaRSA), named Net-SpaRSA, optimized for wireless applications. All three algorithms are integrated into an experimental OFDM-based IEEE 802.11ac system. Our analysis centers on evaluating the performance of these algorithms under limited number of narrow-band measurements, demonstrating that accurate CIR estimation is achievable even using only 50% of the full wideband spectrum. Additionally, we analyze and compare the computational complexity of these algorithms to assess their practical feasibility.