Deep Learning-Based Communication Over the Air (1707.03384v1)

Abstract: End-to-end learning of communications systems is a fascinating novel concept that has so far only been validated by simulations for block-based transmissions. It allows learning of transmitter and receiver implementations as deep neural networks (NNs) that are optimized for an arbitrary differentiable end-to-end performance metric, e.g., block error rate (BLER). In this paper, we demonstrate that over-the-air transmissions are possible: We build, train, and run a complete communications system solely composed of NNs using unsynchronized off-the-shelf software-defined radios (SDRs) and open-source deep learning (DL) software libraries. We extend the existing ideas towards continuous data transmission which eases their current restriction to short block lengths but also entails the issue of receiver synchronization. We overcome this problem by introducing a frame synchronization module based on another NN. A comparison of the BLER performance of the "learned" system with that of a practical baseline shows competitive performance close to 1 dB, even without extensive hyperparameter tuning. We identify several practical challenges of training such a system over actual channels, in particular the missing channel gradient, and propose a two-step learning procedure based on the idea of transfer learning that circumvents this issue.

• The paper introduces an autoencoder-based system that jointly trains transmitter and receiver networks to minimize reconstruction errors in wireless transmissions.
• It explores various neural architectures, including CNNs and RNNs, and employs advanced optimization techniques to tackle non-convex challenges.
• Key results show improved bit-error rates and robust performance under channel variations, underscoring the approach's adaptability in real-world settings.

Deep Learning-Based Communication Over the Air

The paper "Deep Learning-Based Communication Over the Air" by Sebastian Dörner, Sebastian Cammerer, Jakob Hoydis, and Stephan ten Brink presents an exploration of employing deep learning techniques to enhance wireless communication systems. The research investigates the potential of deep neural networks (DNNs) to optimize end-to-end communication systems by integrating them as primary components in the transmission and reception processes.

Overview

Traditional wireless communication systems rely heavily on modular, handcrafted signal processing techniques at both the transmitter and receiver ends. The authors propose a paradigm shift by utilizing deep learning models that can learn to perform these tasks holistically. This approach allows for the creation of a communication system that adapts to varying environmental conditions, which is particularly pertinent in dynamic and complex wireless channels.

Methodology

The central contribution of the research lies in the construction of an end-to-end system using autoencoder-based architectures. Both the transmitter and receiver are represented as deep neural networks trained jointly to optimize the transmission and reception processes. By employing a supervised learning framework, the system minimizes the reconstruction error between the transmitted and received data, effectively learning to counteract channel impairments.

The paper explores several architectural considerations for the DNNs:

• Various architectures, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are explored for their suitability in different scenarios.
• The training processes leveraged state-of-the-art optimization techniques to handle the non-convex nature of neural networks.
• Consideration of practical issues such as quantization and computational complexity ensures the applicability of the model in real-world systems.

Results

Key findings demonstrate that the deep learning-based approach offers competitive performance compared to traditional systems, especially in challenging communication scenarios. Notable numerical results include:

• Improved bit-error rates (BER) under varying Signal-to-Noise Ratio (SNR) conditions when tested on standard communication channel models.
• Robustness against model mismatches and channel variations, indicating adaptability that is often difficult to achieve in traditional systems.

Implications and Future Directions

The integration of deep learning into wireless communication systems has several practical and theoretical implications:

• Adaptive Systems: The ability of neural networks to learn and adapt fine-grained parameters increases the resilience of communication systems against unforeseen channel conditions.
• System Complexity: Transitioning to end-to-end learning architectures could simplify system design and reduce the dependency on domain-specific signal processing expertise.
• Generalization and Transfer Learning: The capacity for transfer learning could be explored to facilitate rapid deployment across different channels or communication standards.

Future research may extend this work by investigating the implications of integrating other deep learning paradigms, such as reinforcement learning, for real-time adaptation and optimization of more complex network architectures. Additionally, considerations for energy efficiency and hardware implementation remain crucial for practical deployment.

In conclusion, by advancing the understanding of deep learning's role in wireless communication, this paper sets a foundation for continued exploration into more intelligent and adaptable communication systems. As the field progresses, these methodologies could significantly influence both academia and industry standards.