Canonical distributed quantization schemes do not scale to large sensor networks due to the exponential decoder storage complexity that they entail. Prior efforts to tackle this issue have largely been limited to the ...
详细信息
ISBN:
(纸本)9781457705397
Canonical distributed quantization schemes do not scale to large sensor networks due to the exponential decoder storage complexity that they entail. Prior efforts to tackle this issue have largely been limited to the suboptimal schemes of source grouping and decoding, thus failing to use all available information at the decoder. We propose a new decoding paradigm where all received bits are used in decoding. Essentially, to decode each source, we partition the space of received bit-tuples using a nearest neighbor quantizer at a decoding rate consistent with the allowed complexity and each partition is then mapped to a reconstruction value for that source. To avoid local minima in design, we resort to deterministic annealing to determine the nearest neighbor partition function (the partitioning prototypes) from the training set. Results on several data-sets show substantial gains over naive and other competing approaches.
Canonical distributed quantization schemes do not scale to large sensor networks due to the exponential decoder storage complexity that they entail. Prior efforts to tackle this issue have largely been limited to the ...
详细信息
ISBN:
(纸本)9781457705380
Canonical distributed quantization schemes do not scale to large sensor networks due to the exponential decoder storage complexity that they entail. Prior efforts to tackle this issue have largely been limited to the suboptimal schemes of source grouping and decoding, thus failing to use all available information at the decoder. We propose a new decoding paradigm where all received bits are used in decoding. Essentially, to decode each source, we partition the space of received bit-tuples using a nearest neighbor quantizer at a decoding rate consistent with the allowed complexity and each partition is then mapped to a reconstruction value for that source. To avoid local minima in design, we resort to deterministic annealing to determine the nearest neighbor partition function (the partitioning prototypes) from the training set. Results on several data-sets show substantial gains over naive and other competing approaches.
This paper considers the problem of distributed source coding for a large sensor network. A typical shortcoming of current approaches to true distributed coding is the exponential growth of the decoder codebook size w...
详细信息
ISBN:
(纸本)9781424442966
This paper considers the problem of distributed source coding for a large sensor network. A typical shortcoming of current approaches to true distributed coding is the exponential growth of the decoder codebook size with the number of sources in the network. This growth in complexity renders many traditional approaches impractical for even moderately sized sensor networks. Inspired by our recent results on fusion coding for selective retrieval, we propose a new distributed coding approach that scales to a large number of sources. Central to our approach is a "bit-subset selector" module whose role is to judiciously extract an appropriate subset of the received bits for decoding per individual source. This, together with joint design of all system components, enables direct optimization of the decoder complexity-distortion tradeoff, and thereby the desired scalability. Experiments on both real and synthetic data-sets show considerable gains over heuristic schemes.
This paper considers the problem of distributed source coding for a large sensor network. A typical shortcoming of current approaches to true distributed coding is the exponential growth of the decoder codebook size w...
详细信息
ISBN:
(纸本)9781424442959
This paper considers the problem of distributed source coding for a large sensor network. A typical shortcoming of current approaches to true distributed coding is the exponential growth of the decoder codebook size with the number of sources in the network. This growth in complexity renders many traditional approaches impractical for even moderately sized sensor networks. Inspired by our recent results on fusion coding for selective retrieval, we propose a new distributed coding approach that scales to a large number of sources. Central to our approach is a "bit-subset selector" module whose role is to judiciously extract an appropriate subset of the received bits for decoding per individual source. This, together with joint design of all system components, enables direct optimization of the decoder complexity-distortion tradeoff, and thereby the desired scalability. Experiments on both real and synthetic data-sets show considerable gains over heuristic schemes.
暂无评论