Sparse recovery with quantized multiple measurement vectors

We address the quantized sparse recovery problem with multiple measurement vectors, where each measurement is represented using a finite number of bits. The source signals are sparse and share the same sparse support, and the goal is to recover that support. A general framework based on sparse Bayesian learning is developed, which is applicable to both 1-bit and multi-bit scalar quantization. We show through numerical simulations that as the number of the measurements increases, the proposed algorithm can provide support recovery performance similar to that when the measurements are unquantized.

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