A Bayesian hard-thresholding denoiser exploiting beamspace sparsity and composite noise modeling delivers near-linear complexity and FPGA-implementable performance comparable to intensive methods for mmWave massive MIMO with low-resolution ADCs.
PARAMOUNT: Toward generalizable deep learning for mmWave beam selection using sub-6 GHz channel measurements
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Low-Complexity Beamspace Channel Denoiser for mmWave Massive MIMO with Low-Resolution ADCs
A Bayesian hard-thresholding denoiser exploiting beamspace sparsity and composite noise modeling delivers near-linear complexity and FPGA-implementable performance comparable to intensive methods for mmWave massive MIMO with low-resolution ADCs.