Pilot Allocation for Sparse Channel Estimation in MIMO-OFDM Systems

Abstract

The frequency-selective channel-estimation problem in multi-input-multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is investigated from the perspective of compressed sensing (CS). By minimizing the mutual coherence of the measurement matrix in CS theory, two pilot allocation methods for the CS-based channel estimation in MIMO-OFDM systems are proposed. Simulation results show that using the pilot patterns designed by the two proposed methods gives a much better performance than using other pilot patterns in terms of the mean square error of the channel estimate as well as the bit error rate of the system. Moreover, the optimal pilot patterns obtained by the proposed second method based on genetic algorithm and shift mechanism could offer a larger performance gain than those by the first method based on minimizing the largest element in the mutual coherence set possessed by pilot patterns for all multiple antenna ports.Index Terms-Channel estimation, compressed sensing (CS), multi-input-multi-output orthogonal frequency division multiplexing (MIMO-OFDM), mutual coherence, pilot allocation.

Low Complexity Sparse Channel Estimation Based on Compressed Sensing

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An ICA based MIMO-OFDM VLC scheme

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Sparsity‐aware, channel order‐blind pilot placement with channel estimation in orthogonal frequency division multiplexing systems

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