Distributed Decision Fusion With M -ary Source Coding On Sensor Observation and Uncoded Data Transmission

Cheng, Victor W.; Wang, Tsang-Yi; Wang, Hao

doi:10.1109/ccnc46108.2020.9045746

Cited by 1 publication

(6 citation statements)

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“…A multi-level decision algorithm is applied on the observation at each sensor [24,25,26], where W l at the l-th sensor is divided into M equally spacing regions as shown in Figure 1.…”

Section: -Ary Signalingmentioning

confidence: 99%

“…The optimal MAP rule on the log-likelihood [25,26] is applied to obtain the final decisionÂ = ±γ, i.e., H 0 or H 1 .…”

Section: -Ary Signalingmentioning

confidence: 99%

“…As opposed to the existing works mentioned above, this study considers both uncoded (analog) and digital transmission schemes between the local sensors and the FC. The sensor observation is divided into M regions, M ≥ 2 [26]. The information of the soft local decision in each region is sent via uncoded (analog) transmission to the FC without digitalizing and coding process, which is defined as M -ary signaling (MS).…”

Section: Introductionmentioning

confidence: 99%

“…In the proposed MS scheme, the detection on the phenomenon is divided into M equally spacing regions, where M is an arbitrary integer with M ≥ 2. When the detection falls within a specific region, a conditional mean is adopted as a representation of the observation in the region, and an uncoded transmission carrying the information of the quantization is sent to the FC [26]. The goal of the proposed scheme is to minimize the decision errors at the FC via optimizing the region allocation, i.e., varying the width of each of the M regions [26].…”

Section: Introductionmentioning

confidence: 99%

“…When the detection falls within a specific region, a conditional mean is adopted as a representation of the observation in the region, and an uncoded transmission carrying the information of the quantization is sent to the FC [26]. The goal of the proposed scheme is to minimize the decision errors at the FC via optimizing the region allocation, i.e., varying the width of each of the M regions [26]. The error performances using maximuma-posteriori (MAP) and equal-gain-combining (EGC) fusion rules at the FC are both analyzed.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Decision Fusion with M-ary Signaling and k-bit Transmission on Sensor Observation in Wireless Sensor Networks

Cheng¹,

Wang²

2021

IJICS

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Multi-level decisions on sensor detection is able to improve the detection performance on the final decision made at the fusion center (FC) in wireless sensor networks (WSN). In this paper, the performance analysis of an M -ary signaling (MS) scheme using analog transmission and a k-bit transmission (KB) scheme is both examined for distributed binary detection. Under the multi-level decision algorithms, each sensor sends a signal carrying the information of a quantized version of a local decision statistic such as the conditional mean or the log-likelihood ratio. In MS, the output of the quantizer is transmitted directly without digitalizing and coding process, while in KB, each quantization output is coded with k bits and hereby a sensor sends a k-bit hard local decision to the FC. At the FC, the linear combiner detection rule on the transmission schemes is both adopted to make the final decision. The effects of the sensor decision and the transmission errors are incorporated in the analysis of the erroneous performance of the final decision. The goal of the proposed schemes is to minimize the final errors at the FC via optimizing the region allocation on the multi-level decision at the sensor. The numerical results illustrate that the proposed schemes achieve significant improvement in error performance over the conventional schemes under either additive white Gaussian noise (AWGN) channel or Rayleigh faded channel.

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“…A multi-level decision algorithm is applied on the observation at each sensor [24,25,26], where W l at the l-th sensor is divided into M equally spacing regions as shown in Figure 1.…”

Section: -Ary Signalingmentioning

confidence: 99%

“…The optimal MAP rule on the log-likelihood [25,26] is applied to obtain the final decisionÂ = ±γ, i.e., H 0 or H 1 .…”

Section: -Ary Signalingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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