Channel Agnostic End-to-End Learning based Communication Systems with Conditional GAN (1807.00447v1)

Abstract: In this article, we use deep neural networks (DNNs) to develop a wireless end-to-end communication system, in which DNNs are employed for all signal-related functionalities, such as encoding, decoding, modulation, and equalization. However, accurate instantaneous channel transfer function, \emph{i.e.}, the channel state information (CSI), is necessary to compute the gradient of the DNN representing. In many communication systems, the channel transfer function is hard to obtain in advance and varies with time and location. In this article, this constraint is released by developing a channel agnostic end-to-end system that does not rely on any prior information about the channel. We use a conditional generative adversarial net (GAN) to represent the channel effects, where the encoded signal of the transmitter will serve as the conditioning information. In addition, in order to deal with the time-varying channel, the received signal corresponding to the pilot data can also be added as a part of the conditioning information. From the simulation results, the proposed method is effective on additive white Gaussian noise (AWGN) and Rayleigh fading channels, which opens a new door for building data-driven communication systems.

• The paper presents a channel agnostic framework integrating encoder, decoder, and equalizer using conditional GANs to overcome the need for precise CSI.
• It achieves performance parity with traditional methods in both AWGN and Rayleigh fading channels, matching competitive BLER metrics.
• The study demonstrates the potential of deep neural network-based end-to-end learning for developing adaptive and robust communication systems.

Channel Agnostic End-to-End Learning Based Communication Systems with Conditional GAN

The paper under discussion introduces a novel method to develop wireless end-to-end communication systems using deep neural networks (DNNs). This approach seeks to integrate all functionalities inherent to digital communication, such as encoding, decoding, modulation, and equalization, within a unified framework harnessing the capabilities of DNNs. The primary objective is to transcend the limitations posed by traditional communication systems, where individual components are often designed in isolation with varying assumptions that potentially undermine global system optimality.

One of the most significant challenges identified in conventional end-to-end systems is the reliance on precise instantaneous channel state information (CSI) for efficient gradient computation during DNN training. However, acquiring accurate and timely CSI is typically problematic due to the dynamic nature of wireless channels and their dependency on variables like time and location. The paper offers a pioneering solution by introducing a channel agnostic system that employs conditional generative adversarial networks (GANs) to model channel effects without any prior channel-specific information.

Methodology

The authors propose using conditional GANs wherein the encoded signal from the transmitter serves as conditioning information for learning the channel effects. The method further incorporates the received signal corresponding to pilot data as part of the conditioning dataset to adapt to time-varying channels, making the GAN more responsive to real-world conditions. This approach facilitates the effective transmission of gradients back to the transmitter, thus enabling comprehensive end-to-end learning without explicit channel knowledge.

Experimental Results

The proposed system's performance is validated through simulations on additive white Gaussian noise (AWGN) and Rayleigh fading channels. Particularly noteworthy is the observed performance parity of the channel agnostic approach with traditional methods under various realistic channel conditions. This is evidenced by the performance evaluation metrics exhibited in the paper, which demonstrate that the learning-based approach showcases comparable Block Error Rate (BLER) metrics to conventional methods like Hamming code coupled with maximum-likelihood decoding.

Implications and Future Directions

The introduction of a channel agnostic approach to end-to-end communication using conditional GANs presents numerous implications for both theoretical and practical applications. Theoretically, this work represents a substantial step forward in adopting machine learning to optimize communication systems by utilizing generative models for channel representation. Practically, it offers a flexible framework for developing communication systems that can self-adapt to real-world conditions without dependency on predetermined channel assumptions, thus enhancing robustness and efficiency.

Future research could explore the extension of this methodology to encompass broader channel conditions and different communication standards. Additionally, further developments could leverage advancements in reinforcement learning to enhance these systems' adaptive capabilities continuously. The integration of such learning frameworks could foster the evolution of more intelligent and autonomous communication systems that hurdle the constraints of traditional models.

In summary, this paper contributes significantly to the field of communication system design, proposing a robust methodology that demonstrates the potential of machine learning to revolutionize how we perceive and implement data-driven signal processing and communication technology.