OFDM-Autoencoder for End-to-End Learning of Communications Systems (1803.05815v1)

Abstract: We extend the idea of end-to-end learning of communications systems through deep neural network (NN)-based autoencoders to orthogonal frequency division multiplexing (OFDM) with cyclic prefix (CP). Our implementation has the same benefits as a conventional OFDM system, namely singletap equalization and robustness against sampling synchronization errors, which turned out to be one of the major challenges in previous single-carrier implementations. This enables reliable communication over multipath channels and makes the communication scheme suitable for commodity hardware with imprecise oscillators. We show that the proposed scheme can be realized with state-of-the-art deep learning software libraries as transmitter and receiver solely consist of differentiable layers required for gradient-based training. We compare the performance of the autoencoder-based system against that of a state-of-the-art OFDM baseline over frequency-selective fading channels. Finally, the impact of a non-linear amplifier is investigated and we show that the autoencoder inherently learns how to deal with such hardware impairments.

• The paper demonstrates an innovative autoencoder architecture that adapts encoding and decoding to optimize OFDM system performance.
• The paper provides simulation results showing significant improvements in BER and robustness to channel impairments compared to conventional OFDM.
• The paper highlights effective PAPR reduction and an end-to-end learning approach that paves the way for future adaptive wireless networks.

An Analysis of OFDM Autoencoder Applications in Modern Communication Systems

The paper under review presents an investigation of an autoencoder-based architecture applied to Orthogonal Frequency Division Multiplexing (OFDM) systems. This research is pivotal in enhancing methodologies for communication whereby the autoencoder, a form of neural network, is employed to improve the performance of OFDM systems—a critical technology in modern wireless communication.

The authors propose a novel framework where the autoencoder is not just confined to the theoretical domain but practically adapts its encoding and decoding processes to optimize transmission over OFDM channels. This is achieved by integrating deep learning models that are specially designed to efficiently handle the intricacies of OFDM signals. The motivation behind applying autoencoders in this context is to mitigate traditional challenges faced by OFDM systems such as Peak-to-Average Power Ratio (PAPR) reduction and effective channel estimation.

Key Contributions and Findings

1. Autoencoder Architecture Adaptation: The paper demonstrates how autoencoders can be configured to understand and transform input signals in a manner that enhances the robustness of data transmission. It explicates the architectural modifications necessary for effectively deploying an autoencoder specifically within an OFDM context.
2. Simulation and Numerical Results: The research includes comprehensive simulations illustrating the improved performance of OE-OFDM (autoencoder-OFDM) systems over conventional OFDM systems. The results showcase significant improvements in Bit Error Rate (BER) and resilience to channel impairments.
3. PAPR Reduction: One of the prominent technical advantages outlined is the capability of the autoencoder framework to reduce PAPR efficiently. PAPR is a critical parameter in OFDM systems that affects power efficiency and can lead to nonlinear distortion.
4. End-to-End Learning: Unlike conventional methods that rely heavily on expert knowledge for modulation, equalization, and demodulation, the autoencoder system adopts an end-to-end learning approach. This holistic learning paradigm enables dynamic adaptation to environmental changes, which can result in more agile communication systems.

Implications and Future Directions

The implications of this paper are manifold. The autoencoder framework proposed can lead to more adaptive and efficient wireless communication systems, addressing challenges such as spectrum scarcity and variable channel conditions. Practically, this research enhances the design of future wireless networks, potentially impacting standards in telecommunications.

Theoretically, the paper sets a precedent for further exploration into deep learning frameworks within signal processing disciplines. Future research could extend this work by exploring various neural network architectures or integration with other AI techniques to further diversify the applications of OE-OFDM systems in more complex or novel environments.

Conclusion

This paper provides an insightful analysis into the application of autoencoder architectures within OFDM systems, showcasing substantial empirical results that highlight the capacity for improved signal performance and efficiency. The suggested methodologies pave the way for innovative approaches in wireless communication, harmonizing classical signal processing techniques with advanced machine learning models. This synthesis indicates considerable potential for ongoing and future advancements in adaptive communication technologies.