The relay channel, consisting of a source-destination pair along with a relay, is a fundamental component of cooperative communications. While the capacity of a general relay channel remains unknown, various relaying strategies, including compress-and-forward (CF), have been proposed. In CF, the relay forwards a quantized version of its received signal to the destination. Given the correlated signals at the relay and destination, distributed compression techniques, such as Wyner--Ziv coding, can be harnessed to utilize the relay-to-destination link more efficiently. Leveraging recent advances in neural network-based distributed compression, we revisit the relay channel problem and integrate a learned task-aware Wyner--Ziv compressor into a primitive relay channel with a finite-capacity out-of-band relay-to-destination link. The resulting neural CF scheme demonstrates that our compressor recovers binning of the quantized indices at the relay, mimicking the optimal asymptotic CF strategy, although no structure exploiting the knowledge of source statistics was imposed into the design. The proposed neural CF, employing finite order modulation, operates closely to the rate achievable in a primitive relay channel with a Gaussian codebook. We showcase the advantages of exploiting the correlated destination signal for relay compression through various neural CF architectures that involve end-to-end training of the compressor and the demodulator components. Our learned task-oriented compressors provide the first proof-of-concept work toward interpretable and practical neural CF relaying schemes.

翻译：中继信道由源-目的节点对及一个中继节点构成，是协作通信的基础组件。尽管通用中继信道的容量尚属未知，但包括压缩转发（CF）在内的多种中继策略已被提出。在CF中，中继将接收信号的量化版本转发至目的节点。鉴于中继与目的节点间存在相关信号，可采用如Wyner-Ziv编码等分布式压缩技术，以更高效地利用中继-目的节点链路。借助基于神经网络的分布式压缩最新进展，我们重新审视中继信道问题，将学习型任务感知Wyner-Ziv压缩器集成到具有有限容量带外中继-目的节点链路的基本中继信道中。所提出的神经CF方案表明，尽管设计未利用信源统计特性的先验知识，我们的压缩器仍能恢复中继处量化索引的装箱操作，模仿最优渐近CF策略。采用有限阶调制的神经CF方案，其性能接近采用高斯码本的基本中继信道可达速率。通过包含压缩器与解调器端到端训练的各种神经CF架构，我们展示了利用相关目的节点信号进行中继压缩的优势。此学习型任务导向压缩器为可解释且实用的神经CF中继方案提供了首个概念验证工作。