Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels (1911.03038v1)

Abstract: Designing codes that combat the noise in a communication medium has remained a significant area of research in information theory as well as wireless communications. Asymptotically optimal channel codes have been developed by mathematicians for communicating under canonical models after over 60 years of research. On the other hand, in many non-canonical channel settings, optimal codes do not exist and the codes designed for canonical models are adapted via heuristics to these channels and are thus not guaranteed to be optimal. In this work, we make significant progress on this problem by designing a fully end-to-end jointly trained neural encoder and decoder, namely, Turbo Autoencoder (TurboAE), with the following contributions: ($a$) under moderate block lengths, TurboAE approaches state-of-the-art performance under canonical channels; ($b$) moreover, TurboAE outperforms the state-of-the-art codes under non-canonical settings in terms of reliability. TurboAE shows that the development of channel coding design can be automated via deep learning, with near-optimal performance.

• The paper introduces TurboAE, a novel deep learning model that automates both encoder and decoder design using interleaved encoding and iterative decoding.
• TurboAE utilizes a CNN-based architecture and tailored training algorithms to achieve near-optimal performance in AWGN channels and superior reliability in non-AWGN environments.
• Experimental results validate TurboAE's effectiveness, demonstrating improved error correction efficiency over traditional codes and paving the way for automated code design.

Turbo Autoencoder: Enhancements in Channel Coding Through Deep Learning

The paper "Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels" presents a significant advancement in the field of channel coding by introducing a novel approach leveraging deep learning techniques. The Turbo Autoencoder (TurboAE) represents a shift from traditional methods, which depend heavily on heuristic adaptations and manually crafted algorithms, to a system that can automate the design of both encoder and decoder components for communication channels.

Overview of Turbo Autoencoder

TurboAE is conceptualized as an end-to-end trained neural network that combines the functionalities of an encoder and decoder. This model is specifically designed to tackle both canonical and non-canonical channel settings effectively. In canonical channels, characterized by Additive White Gaussian Noise (AWGN), TurboAE demonstrates performances close to state-of-the-art channel codes. In more complex non-canonical channels, where noise characteristics deviate from Gaussian distributions, TurboAE excels significantly, outperforming existing models in terms of reliability.

Key Contributions

The paper introduces several innovative elements in the design and training of TurboAE:

1. Interleaved Encoder Structure: Inspired by turbo codes, TurboAE implements interleaved encoding which provides long-range memory and enhances error correction capabilities. This mechanism ensures that the encoded data carries more structured redundancy, improving decoding in varied noise environments.
2. Iterative Decoder: The decoder employs an iterative process that alternates between decoding based on original and interleaved data. This iterative approach refines the posterior probabilities of transmitted bits progressively, enhancing the accuracy of the message reconstruction.
3. Neural Network Architecture: The design employs Convolutional Neural Networks (CNN) rather than Recurrent Neural Networks (RNN) for both encoding and decoding tasks, offering advantages in terms of computational efficiency and parallel processing capabilities.
4. Training Algorithms: To circumvent local optima and enhance model stability, the training process independently updates the encoder and decoder parameters, utilizing large batch sizes and distinct signal-to-noise ratio (SNR) settings for encoder and decoder training.
5. Binary Constraint Handling: For applications necessitating binary transmission signals, TurboAE achieves binarization using a straight-through estimator, facilitating effective training despite the inherent non-differentiability of binary constraints.

Experimental Results

The effectiveness of TurboAE is evident in its performance across various experimental setups:

• AWGN Channels: For moderate block lengths, TurboAE compares favorably to traditional codes like LDPC, Polar, and Turbo codes. In certain SNR ranges, TurboAE achieves superior performance, showcasing its ability to adaptively optimize encoding even with limited block length, which traditional codes struggle to maintain.
• Non-AWGN Channels: TurboAE demonstrates remarkable reliability in channels with non-standard noise settings, such as T-distribution noise and Markovian noise. Its ability to fine-tune its encoder and decoder offers a significant advantage where handcrafted decoding strategies are insufficient.

The results highlight TurboAE’s potential as a versatile solution for contemporary communication challenges, capable of maintaining high error correction efficiencies across diverse channel conditions.

Implications and Future Directions

The implications of TurboAE extend beyond improved channel coding solutions; it opens avenues for automated code design, minimizing reliance on human intervention. The approach underscores deep learning's role in advancing communication systems, promoting adaptability and efficiency without exhaustive manual tweaking.

Given current performance, areas requiring further research include enhancing TurboAE's capabilities for larger block lengths and high SNR environments, optimizing BLER, and integrating meta-learning techniques for rapid adaptation.

In conclusion, TurboAE marks a critical step towards harnessing deep learning to optimize communication systems, potentially redefining how channel codes are constructed and utilized in dynamic environments. As computational resources expand and AI technology advances, TurboAE’s foundational principles hold promise for future innovations in wireless communications.

The authors have provided detailed methodologies and outcomes that invite continued exploration and collaboration to realize full potential in practical applications, making their source code publicly available for further refinement and research.