Decentralized Sequential Composite Hypothesis Test Based on One-Bit Communication
Decentralized Sequential Composite Hypothesis Test Based on One-Bit Communication
复制标题
DOI：
10.1109/tit.2017.2693156
复制
发表时间：
2017-06-01
影响因子：
2.5
通讯作者：
Liu, Jingchen
中科院分区：
文献类型：
--
作者：
Li, Shang;Li, Xiaoou;Liu, Jingchen
研究了多传感器序贯复合假设检验问题。传感器并行观察随机样本，并与融合中心通信，融合中心根据传感器输入做出全局决策。一方面，在集中式的情况下，本地样本被精确地传输到融合中心，广义序贯似然比检验（GSPRT）被证明是渐近最优的预期样本量的错误率趋于零。另一方面，对于功率和带宽资源有限的系统，仅向融合中心发送本地样本摘要（我们特别关注一位通信协议）的分散式解决方案非常重要。为此，我们首先考虑一个分散的计划，传感器发送他们的一位量化统计每隔一段固定的时间到融合中心。我们发现，这样一个均匀的采样和量化方案是严格的次优和它的次优性可以量化的量化统计分布的KL分歧下的假设。然后，我们提出了一个分散的GSPRT基于电平触发采样。也就是说，每个传感器重复运行自己的GSPRT，并异步地向融合中心报告其本地决策。我们表明，该计划是渐近最优的局部阈值和全局阈值以不同的速度增长大。最后，两个特定的模型和相关的应用进行了研究，比较集中和分散的方法。数值结果表明，所提出的基于电平触发采样的分散方案与集中式方案密切相关，通信开销大大降低，并显着优于均匀采样和基于量化的分散方案。
This paper considers the sequential composite hypothesis test with multiple sensors. The sensors observe random samples in parallel and communicate with a fusion center, who makes the global decision based on the sensor inputs. On the one hand, in the centralized scenario, where local samples are precisely transmitted to the fusion center, the generalized sequential likelihood ratio test (GSPRT) is shown to be asymptotically optimal in terms of the expected sample size as error rates tend to zero. On the other hand, for systems with limited power and bandwidth resources, decentralized solutions that only send a summary of local samples (we particularly focus on a one-bit communication protocol) to the fusion center is of great importance. To this end, we first consider a decentralized scheme where sensors send their one-bit quantized statistics every fixed period of time to the fusion center. We show that such a uniform sampling and quantization scheme is strictly suboptimal and its suboptimality can be quantified by the KL divergence of the distributions of the quantized statistics under both the hypotheses. We then propose a decentralized GSPRT based on level-triggered sampling. That is, each sensor runs its own GSPRT repeatedly and reports its local decision to the fusion center asynchronously. We show that this scheme is asymptotically optimal as the local thresholds and global thresholds grow large at different rates. Finally, two particular models and their associated applications are studied to compare the centralized and decentralized approaches. Numerical results are provided to demonstrate that the proposed level-triggered sampling based decentralized scheme aligns closely with the centralized scheme with substantially lower communication overhead, and significantly outperforms the uniform sampling and quantization-based decentralized scheme.