Quantization constrained convex optimization for the compressive sensing reconstructions

In this paper, a convex optimization technique, which is based on the generalized quantization constraint (GQC), is proposed for the compressive sensing (CS) reconstruction that uses quantized measurements. The set size of the proposed GQC can be controlled, and through extensive numerical simulations based on the uniform scalar quantizers, the CS reconstruction errors are improved by 3.1-4.6dB compared to the previous quantization constraint method.