Multi-Processor Approximate Message Passing Using Lossy Compression
arXiv:1601.04595
Abstract
In this paper, a communication-efficient multi-processor compressed sensing framework based on the approximate message passing algorithm is proposed. We perform lossy compression on the data being communicated between processors, resulting in a reduction in communication costs with a minor degradation in recovery quality. In the proposed framework, a new state evolution formulation takes the quantization error into account, and analytically determines the coding rate required in each iteration. Two approaches for allocating the coding rate, an online back-tracking heuristic and an optimal allocation scheme based on dynamic programming, provide significant reductions in communication costs.
to appear at icassp 2016
References in corpus (5)
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Cited by in corpus (6)
- An Approximate Message Passing Framework for Side Information
- Statistical Physics and Information Theory Perspectives on Linear Inverse Problems
- Optimal Trade-offs in Multi-Processor Approximate Message Passing
- An Overview of Multi-Processor Approximate Message Passing
- Multiprocessor Approximate Message Passing with Column-Wise Partitioning
- Source Coding Optimization for Distributed Average Consensus