期刊论文详细信息
Entropy
Distributed Quantization for Partially Cooperating Sensors Using the Information Bottleneck Method
Steffen Steiner1  Volker Kuehn1  Abdulrahman Dayo Aminu2 
[1] Institute of Communications Engineering, University of Rostock, 18119 Rostock, Germany;Pydro GmbH, 18119 Rostock, Germany;
关键词: distributed compression;    chief executive officer problem;    cooperating sensors;    distributed source coding;    information bottleneck;   
DOI  :  10.3390/e24040438
来源: DOAJ
【 摘 要 】

This paper addresses the optimization of distributed compression in a sensor network with partial cooperation among sensors. The widely known Chief Executive Officer (CEO) problem, where each sensor has to compress its measurements locally in order to forward them over capacity limited links to a common receiver is extended by allowing sensors to mutually communicate. This extension comes along with modified statistical dependencies among involved random variables compared to the original CEO problem, such that well-known outer and inner bounds do not hold anymore. Three different inter-sensor communication protocols are investigated. The successive broadcast approach allows each sensor to exploit instantaneous side-information of all previously transmitting sensors. As this leads to dimensionality problems for larger networks, a sequential point-to-point communication scheme is considered forwarding instantaneous side-information to only one successor. Thirdly, a two-phase transmission protocol separates the information exchange between sensors and the communication with the common receiver. Inspired by algorithmic solutions for the original CEO problem, the sensors are optimized in a greedy manner. It turns out that partial communication among sensors improves the performance significantly. In particular, the two-phase transmission can reach the performance of a fully cooperative CEO scenario, where each sensor has access to all measurements and the knowledge about all channel conditions. Moreover, exchanging instantaneous side-information increases the robustness against bad Wyner–Ziv coding strategies, which can lead to significant performance losses in the original CEO problem.

【 授权许可】

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