Several key results in source coding offer the intuition that distributed encoding via vector-quantize-and-bin is only slightly suboptimal to joint encoding and oftentimes is just as good. However, when source acquisi...
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ISBN:
(纸本)9781467345378
Several key results in source coding offer the intuition that distributed encoding via vector-quantize-and-bin is only slightly suboptimal to joint encoding and oftentimes is just as good. However, when source acquisition requires the blocklength to be small, collaboration between sensors can greatly reduce distortion. For a distributed acquisition network where sensors are allowed to "chat" using a side channel, we provide exact characterization of distortion performance and quantizer design in the high-resolution (low-distortion) regime using a framework called distributed functional scalar quantization (DFSQ). The key result is that chatting can dramatically improve performance even when the intersensor communication is at very low rate. We also solve the rate allocation problem when communication links have heterogeneous costs and provide examples to demonstrate that this theory predicts performance at practical communication rates.
In this paper a new coding scheme is proposed to accomplish unequal error protection (UEP) for distributed rateless codes. This coding scheme can be applied when multi-sources symbols share a single relay and the rela...
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ISBN:
(纸本)9781510821279
In this paper a new coding scheme is proposed to accomplish unequal error protection (UEP) for distributed rateless codes. This coding scheme can be applied when multi-sources symbols share a single relay and the relay has no knowledge about their importance levels. We design this coding scheme by encoding the symbols with a second LT process at the relay. UEP property is provided via several equal error protection (EEP) LT processes. Asymptotical analyses have been made on how the encoding parameters affect the error performances. Experimental results show that the second LT process at the relay significantly improves the error performances of the symbols. Asymptotic analysis and experimental results prove this coding scheme viable.
We consider remote state estimation of a scalar stationary linear Gauss-Markov process observed via noisy measurements obtained by two sensors. The sensors can construct a causal linear function of their measurements,...
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ISBN:
(纸本)9781509017508
We consider remote state estimation of a scalar stationary linear Gauss-Markov process observed via noisy measurements obtained by two sensors. The sensors can construct a causal linear function of their measurements, which are quantized and transmitted to a decoder (or fusion centre (FC)) over channels which are prone to packet erasures. We design linear encoding and decoding strategies for estimating the state of the linear system that allow improved estimation performance in the presence of packet erasures and quantization errors. To this end, we construct and compare various distributed encoding and decoding methods without any feedback from the FC regarding the channel erasures. We also design various decentralized benchmark methods that either assume perfect feedback from the FC or in addition co-location of the two sensors resulting in a centralized scheme with diversity. These benchmark methods provide various lower bounds for the distributed encoding-decoding schemes designed without feedback. Numerical results indicate i) that optimal decentralized design of the encoders and the decoder in the absence of feedback can provide a remote state estimation performance that is comparable to those achieved by the lower bounds (with feedback) particularly when the sensors are identical and their channels are symmetric, and (ii) a little feedback from the decoder can improve the performance considerably when the channels are asymmetric (i.e. the packet erasure probabilities are unequal).
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