语义通信用于不可靠链路上的分布式频谱监测

中文摘要 AI 辅助

分布式频谱监测依赖于空间分离的接收器来推断无线电环境的状态。其核心任务是确定有多少发射器处于活动状态以及它们的位置。传感器通常通过共享的不可靠链路进行报告，在这些链路上，竞争、衰落或能量限制会抹除整个报告，因此将所有接收器的原始I/Q样本转发到融合节点变得不切实际。本文将分布式发射器计数和定位问题表述为在不可靠报告链路上的语义通信问题。每个接收器将其局部时频观测映射为紧凑的潜在表示并进行传输。融合节点将每个幸存的潜在表示与对应的接收器位置配对，并对由此产生的无序集合进行操作，这使融合规则与可变成员资格和接收器数量变化兼容。编码器和解码器在任务目标内通过报告擦除以及可选的有限速率量化进行端到端训练。在合成多发射器场景上的实验表明，信道感知训练提高了擦除下的鲁棒性，集合融合比固定顺序拼接更鲁棒，并且一个训练好的模型可以在不同接收器数量下运行而无需重新训练。速率研究表明，在测试设置中，每个潜在分量仅需几个比特就足够了，并且速率拐点在不同擦除水平下大致保持稳定。这些结果支持了在不可靠链路上进行低速率语义报告用于分布式频谱监测的可行性。

英文摘要

Distributed spectrum monitoring relies on spatially separated receivers to infer the state of the radio environment. A central task is to determine how many emitters are active and where they are located. Sensors typically report over shared unreliable links, where contention, fading, or energy limits erase whole reports, so forwarding raw I/Q samples from all receivers to a fusion node becomes impractical. This paper formulates distributed emitter counting and localization as a semantic communication problem over unreliable reporting links. Each receiver maps its local time-frequency observation to a compact latent representation and transmits it. The fusion node pairs each surviving latent with the corresponding receiver position and operates on the resulting unordered set, which keeps the fusion rule compatible with variable membership and receiver-count changes. The encoder and decoder are trained end-to-end with report erasure, and optionally finite-rate quantization, inside the task objective. Experiments on synthetic multi-emitter scenes show that channel-aware training improves robustness under erasure, that set fusion is more robust than fixed-order concatenation, and that one trained model operates across receiver counts without retraining. The rate study shows that a few bits per latent component suffice in the tested setting, and that the rate knee remains roughly stable across erasure levels. These results support the feasibility of low-rate semantic reporting for distributed spectrum monitoring over unreliable links.

发表机构

• Dalhousie University（达尔豪斯大学）
• Paris-Saclay University（巴黎萨克雷大学）

机构由 AI 辅助整理，请以论文原文为准。