Compressed-Sensing-Based Sparse Channel Estimation for Frequency-Selective MIMO-OFDM Systems Under Reduced Pilot Observations
TL;DR
The results show that sparse recovery exploits the assumed delay-domain sparsity more effectively than non-sparse baselines in the underdetermined regime, while pilot density remains a dominant factor in support identification and reconstruction error.
Abstract
Accurate channel estimation in frequency-selective multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems requires balancing pilot overhead, reconstruction accuracy, and computational cost. This paper presents a reproducible compressed sensing benchmark for sparse delay-domain channel estimation with reduced pilot observations. Its novelty is not the invention of Orthogonal Matching Pursuit (OMP), Compressive Sampling Matching Pursuit (CoSaMP), or Subspace Pursuit (SP), but the construction of a transparent and auditable evaluation protocol in which all estimators operate on the same channel realizations, sensing matrices, pilot budgets, signal-to-noise ratios (SNRs), stopping rules, and Monte Carlo trials. The framework explicitly defines the underdetermined observation model, the per-link 4 × 4 MIMO interpretation, the minimum-norm least-squares (LS) baseline, the identity-prior linear minimum mean-square error (LMMSE) baseline, the oracle-known sparsity assumption, uncertainty reporting, runtime protocol, and the mapping from delay-domain estimates to link- and subcarrier-domain quantities. OMP, CoSaMP, SP, LS, and LMMSE are evaluated for a 128-element delay dictionary, five active taps, sampling ratios from 0.10 to 0.70, SNRs from 0 to 30 dB, and 80 independent trials per operating point. The results show that sparse recovery exploits the assumed delay-domain sparsity more effectively than non-sparse baselines in the underdetermined regime, while pilot density remains a dominant factor in support identification and reconstruction error. The accompanying Python human–machine interface (HMI) produces confidence-aware metrics and publication-ready figures, enabling exact repetition of the benchmark and controlled extension to more realistic channel models. The conclusions are limited to simulation-based algorithmic evidence and define a direct pathway toward standardized-channel, software-defined radio (SDR), and measured radio-frequency (RF) validation.