{
  "id": "1404.7666",
  "version": "v1",
  "published": "2014-04-30T10:16:23.000Z",
  "updated": "2014-04-30T10:16:23.000Z",
  "title": "Distributed Quantization for Compressed Sensing",
  "authors": [
    "Amirpasha Shirazinia",
    "Saikat Chatterjee",
    "Mikael Skoglund"
  ],
  "comment": "5 Pages, Accepted for presentation in ICASSP 2014",
  "categories": [
    "cs.IT",
    "math.IT"
  ],
  "abstract": "We study distributed coding of compressed sensing (CS) measurements using vector quantizer (VQ). We develop a distributed framework for realizing optimized quantizer that enables encoding CS measurements of correlated sparse sources followed by joint decoding at a fusion center. The optimality of VQ encoder-decoder pairs is addressed by minimizing the sum of mean-square errors between the sparse sources and their reconstruction vectors at the fusion center. We derive a lower-bound on the end-to-end performance of the studied distributed system, and propose a practical encoder-decoder design through an iterative algorithm.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2014-04-30T10:16:23.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "compressed sensing",
      "distributed quantization",
      "fusion center",
      "enables encoding cs measurements",
      "vq encoder-decoder pairs"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 5,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2014arXiv1404.7666S"
    }
  }
}