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Sparsity-Adaptive Beamspace Channel Estimation for 1-Bit mmWave Massive MIMO Systems

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Document pages: 6 pages

Abstract: We propose sparsity-adaptive beamspace channel estimation algorithms thatimprove accuracy for 1-bit data converters in all-digital millimeter-wave(mmWave) massive multiple-input multiple-output (MIMO) basestations. Ouralgorithms include a tuning stage based on Stein s unbiased risk estimate(SURE) that automatically selects optimal denoising parameters depending on theinstantaneous channel conditions. Simulation results with line-of-sight (LoS)and non-LoS mmWave massive MIMO channel models show that our algorithms improvechannel estimation accuracy with 1-bit measurements in acomputationally-efficient manner.

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