Massive MIMO System with Sparse Channel Estimation and Pilot Optimization
Keywords:
Massive MIMO communication, compressive sensing, channel estimationAbstract
Aiming at the number of user pilots in Multiple-Input Multiple-Output (MIMO) uplink is small and the distribution is uneven, which leads to the error interpolation layer in conventional interpolation channel estimation method, a sparse channel estimation and pilot optimization are proposed. Based on the theory of Compressed Sensing (CS), the sparse channel impulse response is estimated. According to the principle of minimization of measurement matrix cross-correlation in CS theory, a pilot algorithm based on random search and pilot power optimization algorithm is proposed. Simulation results show that the performance of proposed method is better than the least squares estimation based on linear interpolation, with CS channel estimation without pilot optimization, and the CS channel estimation based on pilot pattern optimization. The simulation verifies that the uplink massiveMIMO performance with interleaved and generalized subcarrier allocation achieves reliable communication between two users when the received signal-to-noise ratio is higher than 20 dB.
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