Although user cooperation cannot improve the capacity of Gaussian two-way channels (GTWCs) with independent noises, it can improve communication reliability. In this work, we aim to enhance and balance the communication reliability in GTWCs by minimizing the sum of error probabilities via joint design of encoders and decoders at the users. We first formulate general encoding/decoding functions, where the user cooperation is captured by the coupling of user encoding processes. The coupling effect renders the encoder/decoder design non-trivial, requiring effective decoding to capture this effect, as well as efficient power management at the encoders within power constraints. To address these challenges, we propose two different two-way coding strategies: linear coding and learning-based coding. For linear coding, we propose optimal linear decoding and discuss new insights on encoding regarding user cooperation to balance reliability. We then propose an efficient algorithm for joint encoder/decoder design. For learning-based coding, we introduce a novel recurrent neural network (RNN)-based coding architecture, where we propose interactive RNNs and a power control layer for encoding, and we incorporate bi-directional RNNs with an attention mechanism for decoding. Through simulations, we show that our two-way coding methodologies outperform conventional channel coding schemes (that do not utilize user cooperation) significantly in sum-error performance. We also demonstrate that our linear coding excels at high signal-to-noise ratios (SNRs), while our RNN-based coding performs best at low SNRs. We further investigate our two-way coding strategies in terms of power distribution, two-way coding benefit, different coding rates, and block-length gain.

翻译：尽管用户协作无法提升具有独立噪声的高斯双向信道（GTWCs）的容量，但能改善通信可靠性。本研究旨在通过联合设计用户的编码器与解码器，以最小化误码概率之和的方式，增强并平衡GTWCs中的通信可靠性。我们首先建立通用编解码函数模型，其中用户协作通过编码过程的耦合得以体现。耦合效应使得编解码器设计具有挑战性：需要有效的解码机制捕获此效应，同时确保编码器在功率约束下实现高效功率管理。为应对这些挑战，我们提出两种不同的双向编码策略：线性编码与基于学习的编码。对于线性编码，我们提出最优线性解码，并探讨用户协作以平衡可靠性的编码新见解，进而提出高效的编解码器联合设计算法。对于基于学习的编码，我们引入新颖的循环神经网络（RNN）编码架构，采用交互式RNN与功率控制层进行编码，并融合双向RNN与注意力机制进行解码。仿真结果表明，所提出的双向编码方法在总误码性能上显著优于传统信道编码方案（未利用用户协作）。我们还证明线性编码在高信噪比（SNRs）条件下表现优异，而基于RNN的编码在低信噪比下性能最佳。此外，我们从功率分布、双向编码增益、不同编码速率及块长度增益等方面对双向编码策略进行了深入分析。