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

2023-12-31

arXiv.org

AI Summary

This work addresses the low and unbalanced reliability in Gaussian two-way channels (GTWCs). We propose the first linear-and-learning-driven joint encoding-decoding framework. Methodologically, we design an interactive RNN-based encoder and a bidirectional attention decoder, incorporate a power control layer, and jointly optimize to minimize the sum of bidirectional bit error rates (BERs). Theoretically, we characterize the intrinsic mechanism by which user cooperation promotes reliability balancing. Experimentally, the linear variant achieves optimal performance at high SNR, while the RNN variant significantly outperforms unidirectional baselines at low SNR. We empirically validate gains from adaptive power allocation, bidirectional coding, and blocklength extension. Compared to conventional unidirectional coding schemes, our framework simultaneously improves both reliability and fairness across users.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

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.

Problem

• Enhancing reliability in Gaussian two-way channels
• Balancing error probabilities via joint encoder-decoder design
• Developing linear and learning-based coding strategies

Innovation

• Joint encoder/decoder design for GTWCs
• Linear coding with optimal decoding
• RNN-based coding with power control