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

Coding for Gaussian Two-Way Channels: Linear and Learning-Based Approaches