Channel-robust compressed sensing via vector pre-quantization in wireless sensor networks

This paper addresses channel-robust compressed sensing (CS) acquisition of sparse sources under complexity-constrained encoding over noisy channels in wireless sensor networks. We propose a single-sensor joint source-channel coding method based on channel-optimized vector quantization by designing a CS-aware encoder-decoder pair to minimize the end-to-end mean square error (MSE) distortion of the signal reconstruction. As our key target is to obtain tolerable encoding complexity at the resource-limited sensor, the method relies on vector pre-quantization of the measurement space. We derive the necessary optimality conditions for the system blocks using alternating optimization. Numerical results show that our proposed method achieves higher robustness against the joint effect of CS reconstruction, quantization, and channel errors with lower encoding complexity as compared to state of the art CS methods.