Asymptotically Optimal Estimation Algorithm for the Sparse Signal with Arbitrary Distributions

Abstract

In this paper, we propose a sparse signal estimation algorithm that is suitable for many wireless communication systems, especially for the future millimeter wave and underwater communication systems. This algorithm is not only asymptotically optimal, but also robust to the distribution of non-zero entries of the sparse signal. Then, we derive its upper bound and lower bound, and show that the Mean Square Error (MSE) of the proposed algorithm can approach the Minimum Mean Square Error (MMSE) bound when the Signal Noise Ratio (SNR) goes to infinite or zero. Numerical simulations verify our theoretical analysis and also show that the proposed algorithm converges faster than existing algorithms, e.g., TSR-DFT, AMP, etc.

arXiv commentThis is a 6-pages' paper. It is accepted by the IEEE Transactions on Vehicular Technology

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• DNN-Aided Message Passing Based Block Sparse Bayesian Learning for Joint User Activity Detection and Channel Estimation
• User Activity Detection and Channel Estimation for Grant-Free Random Access in LEO Satellite-Enabled Internet-of-Things
• Randomized Channel Sparsifying Hybrid Precoding for FDD Massive MIMO Systems