标题： 量化但未编码的分布式检测（QDD）与不可靠的汇报信道 显示英文标题

标题： Quantized-but-uncoded Distributed Detection (QDD) with Unreliable Reporting Channels

摘要： 分布式检测主要围绕两种方法：非量化分布式检测（UDD），其中每个传感器将其完整的观测结果报告给融合中心（FC），以及量化与编码的分布式检测（CDD），其中每个传感器首先将观测空间进行划分，然后向FC报告一个码字。 在本文中，我们引入了量化但未编码的分布式检测（QDD），其中每个传感器在量化之后，向FC传输一个汇总值，而不是码字。 我们证明，与CDD相比，QDD在传输功率约束下表现出良好的适应性，尽管在参数选择方面复杂度有所增加。 此外，我们证明，在独立观测的情况下，QDD保持了CDD固有的必要条件。 具体而言，最优的传感器决策规则是似然比量化器（LRQ），与信道条件无关。 在涉及传感器二元决策的单传感器场景中，我们发现QDD中的最优传感器规则通常不再“信道盲”，这是CDD的一个特征。 此外，我们在相同的传输功率和带宽下对这些系统进行了数值比较，同时假设传感和报告阶段均存在加性高斯白噪声（AWGN）。 最后，我们提出了一些未来研究的潜在方向。 显示英文摘要

摘要： Distributed detection primarily centers around two approaches: Unquantized Distributed Detection (UDD), where each sensor reports its complete observation to the fusion center (FC), and quantized-and-Coded DD (CDD), where each sensor first partitions the observation space and then reports to the FC a codeword. In this paper, we introduce Quantized-but-uncoded DD (QDD), where each sensor, after quantization, transmits a summarized value, instead of a codeword, to the FC. We show that QDD well adapts to the constraint of transmission power when compared to CDD, albeit with increased complexity in parameter selection. Moreover, we establish that, in the presence of independent observations, QDD upholds a necessary condition inherent in CDD. Specifically, the optimal sensor decision rules are the likelihood ratio quantizers (LRQ), irrelevant to the channel conditions. In the context of a single-sensor scenario involving binary decision at the sensor, we find that the optimal sensor rule in QDD is in general no longer ``channel blind", a feature presented in CDD. In addition, we compare these systems numerically under the same transmission power and bandwidth, while assuming additive white Gaussian noise (AWGN) in both sensing and reporting stages. Finally, we present some potential directions for future research.