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.

翻译：分布式检测主要围绕两种方法展开：未量化的分布式检测（UDD），即每个传感器将其完整观测值报告给融合中心（FC）；以及量化并编码的分布式检测（CDD），即每个传感器先对观测空间进行划分，再向FC报告一个码字。本文提出了一种量化但未编码的分布式检测（QDD），其中每个传感器在量化后向FC传输一个汇总值而非码字。研究表明，与CDD相比，QDD能更好地适应传输功率约束，尽管参数选择的复杂度有所增加。此外，我们证明了在独立观测条件下，QDD保持了CDD所固有的必要条件，即最优传感器决策规则为似然比量化器（LRQ），且与信道条件无关。在涉及传感器二元决策的单传感器场景中，我们发现QDD的最优传感器规则通常不再具有CDD中“信道盲”的特征。最后，我们在相同的传输功率和带宽条件下，假设感知和报告阶段均存在加性高斯白噪声（AWGN），对这些系统进行了数值比较，并提出了未来研究的潜在方向。