Deep Learning based End-to-End Wireless Communication Systems with Conditional GAN as Unknown Channel (1903.02551v1)

Abstract: In this article, we develop an end-to-end wireless communication system using deep neural networks (DNNs), in which DNNs are employed to perform several key functions, including encoding, decoding, modulation, and demodulation. However, an accurate estimation of instantaneous channel transfer function, i.e., channel state information (CSI), is needed in order for the transmitter DNN to learn to optimize the receiver gain in decoding. This is very much a challenge since CSI varies with time and location in wireless communications and is hard to obtain when designing transceivers. We propose to use a conditional generative adversarial net (GAN) to represent channel effects and to bridge the transmitter DNN and the receiver DNN so that the gradient of the transmitter DNN can be back-propagated from the receiver DNN. In particular, a conditional GAN is employed to model the channel effects in a data-driven way, where the received signal corresponding to the pilot symbols is added as a part of the conditioning information of the GAN. To address the curse of dimensionality when the transmit symbol sequence is long, convolutional layers are utilized. From the simulation results, the proposed method is effective on additive white Gaussian noise (AWGN) channels, Rayleigh fading channels, and frequency-selective channels, which opens a new door for building data-driven DNNs for end-to-end communication systems.

• The paper pioneers the use of conditional GANs to model unknown channel effects, enabling effective gradient back-propagation across deep neural networks.
• It integrates pilot signal conditioning and CNNs to adapt to specific channel characteristics and efficiently manage long symbol sequences.
• The approach achieves competitive performance across AWGN, Rayleigh fading, and frequency-selective channels, outperforming traditional model-based systems.

Deep Learning-Based End-to-End Wireless Communication Systems with Conditional GAN as Unknown Channel

The paper by Hao Ye et al. presents a novel approach to developing end-to-end wireless communication systems through the application of deep neural networks (DNNs), addressing the challenges associated with the estimation of instantaneous channel state information (CSI). The central proposition involves the use of a conditional generative adversarial network (GAN) to model channel effects, enabling the bridging of the transmitter and receiver DNNs by allowing back-propagation of gradients. This approach enables the construction of data-driven end-to-end wireless communication systems equipped to handle various channel conditions without predefined models.

Summary of Key Contributions

1. Conditional GAN Usage: The paper pioneers the use of conditional GANs to model unknown channel transfer functions, training the network in a data-driven manner without requiring pre-existing channel models. This contrasts with traditional methods that heavily depend on accurate channel models.
2. Adaptability through Pilot Information: By incorporating received pilot signals into the conditioning information for the GAN, the network can adapt to specific channel characteristics, thereby generating outputs that align closely with the current channel conditions.
3. Handling Long Symbol Sequences: The work addresses the curse of dimensionality in training processes by employing convolutional neural networks (CNNs). This inclusion aids in efficiently learning and processing long sequences, presenting a solution that maintains practicality in the field’s applications.
4. End-to-End System Training: The approach involves iteratively training the transmitter, channel generator, and receiver networks, optimizing the entire communication system’s performance without the necessity of explicit channel model information.

Strong Numerical Results

The methodology exhibits strong numerical results across various channel conditions. Specifically, the simulations illustrate competitive performance on additive white Gaussian noise (AWGN) channels, Rayleigh fading channels, and frequency-selective channels. Noteworthy is the system's ability to surpass traditional communication systems incorporating expert-designed modules in conditions where channel models are presumed known.

Theoretical and Practical Implications

The proposed system provides a notable contribution towards the evolution of communication systems. It offers a framework for real-time adaptability, reducing dependence on static and potentially inaccurate channel models. Such a framework is of particular interest for future wireless systems that are expected to operate in dynamic and unpredictable environments. Furthermore, the insights into overcoming the curse of dimensionality extend across domains involving complex sequence processing.

Future Developments

Potential future developments may include deploying this system in real-world scenarios, where inherent impairments—such as hardware imperfections and unstructured noise—challenge the reliability of pre-defined channel models. Furthermore, expanding this framework to integrate more complex forms of modulation and coding schemes can augment its applicability. As research continues, adapting this approach to integrate seamlessly with existing infrastructure could drive significant innovation in commercial wireless networks.

In summary, this paper presents a robust strategy for utilizing deep learning-based architectures in end-to-end communication systems, demonstrating practical advantages over traditional methodologies by leveraging GANs to model unknown channel conditions effectively. This advances the potential for highly adaptive and reliable wireless communication technologies, underscoring a shift towards data-driven design in the evolving landscape of communication systems.