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Channel Agnostic End-to-End Learning based Communication Systems with Conditional GAN
Hao Ye, Geoffrey Ye Li, Biing-Hwang Fred Juang, Kathiravetpillai Sivanesan
TL;DR
The paper addresses the need for instantaneous channel information when training DNN-based end-to-end communication systems, especially when channels are difficult to obtain and vary over time and location. It uses a conditional GAN conditioned on encoded signals and, for time-varying channels, received pilot data to model channel effects and pass gradients to the transmitter. Simulations report effectiveness on AWGN and Rayleigh fading channels, including performance similar to traditional methods in the reported evaluations.
Problem
DNN-based end-to-end communication requires accurate instantaneous CSI to compute transmitter gradients, but channel transfer functions can be difficult to obtain and time-varying.
Method
A conditional GAN models the channel-output distribution from encoded transmitter signals and received pilot information, enabling supervised joint training of the transmitter, GAN, and receiver.
Results
The proposed method is effective on AWGN and Rayleigh fading channels, with reported end-to-end performance similar to traditional methods in the evaluated tasks.
Takeaways & Limitations
The approach provides a channel-agnostic way to build a pure data-driven end-to-end communication system without prior channel information.
Abstract
from arXiv · showhide
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.
I. INTRODUCTION
The paper targets end-to-end communication designs whose separate blocks and assumed channel models may prevent globally optimal, robust operation. It proposes a channel-agnostic system using a conditional GAN so transmitter, channel model, and receiver can be trained together without prior channel information.
- Separate design of communication blocks makes global system optimality difficult to ascertain, while assumed channel models may mismatch actual transmission conditions.
- End-to-end learning represents the transmitter and receiver with DNNs, optimizing recovery accuracy through supervised learning.
- Unknown channels block gradient back-propagation to the transmitter, and accurate instantaneous CSI is difficult to obtain when channel effects vary or lack analytical models.
- The proposed end-to-end system is designed to address time-varying channels by incorporating received pilot data into the conditional GAN input.
- A conditional GAN learns the channel-output distribution from encoded signals and received pilot information, enabling supervised joint optimization of the transmitter, GAN, and receiver.
II. MODELING CHANNEL WITH CONDITIONAL GAN
The channel blocks transmitter-to-receiver gradient flow in DNN-based end-to-end communication. The proposed remedy is to learn channel effects with a conditional GAN that serves as a differentiable bridge.
- A conditional GAN learns channel effects and acts as a bridge allowing end-to-end loss gradients to reach the transmitter.
A. Conditional GAN
GANs learn target data distributions through adversarial training between a generator and discriminator. Conditioning both networks on extra information adapts the generated channel-output distribution to encoded signals and pilot data.
- A GAN learns to generate samples close to a target distribution, using a generator and discriminator trained in opposition.
- The discriminator distinguishes real from generated samples, while feedback from discrimination guides the generator toward more realistic outputs.
- Conditional GANs feed extra information into both generator and discriminator, producing outputs conditioned on that information.
- In this communication system, the conditional GAN models channel-output distributions using encoded signals and received pilot data as conditioning information.
B. Modeling Channels
A conditional GAN learns the channel-output distribution from encoded transmitter signals, optionally conditioned on received pilot information for time-varying channels.
- The proposed channel model is intended to represent channel effects without requiring direct prior channel knowledge.
- The conditional GAN models the channel-output distribution using the encoded transmitter signal as conditioning information.The generator produces channel-output samples while the discriminator distinguishes real-channel samples from generated samples.
- Received pilot information can be added to the conditioning input so generated outputs reflect channel conditions.
III. END-TO-END COMMUNICATION SYSTEM
The end-to-end system treats transmission as M-class classification and trains its transmitter, receiver, and conditional-GAN channel generator iteratively. Testing evaluates reconstruction using the learned transmitter and receiver over real channels.
- Finite-alphabet transmit symbols are represented as length-M one-hot vectors, while the receiver outputs probabilities over M classes.The receiver computes cross-entropy loss between the transmitted one-hot vector and its output probability vector.
- During training, randomly generated symbols and randomly sampled instantaneous CSI provide data from the channel set.The transmitter, receiver, and conditional-GAN channel generator are trained iteratively, with the other components fixed during each update.
- Testing evaluates end-to-end reconstruction with the learned transmitter and receiver operating on real channels.The testing stage uses the trained components rather than the surrogate channel generator for performance evaluation.
A. Training Receiver
The receiver uses received data and pilot observations as joint inputs, enabling simultaneous channel estimation and symbol detection for time-varying channels.
- The receiver DNN takes the received signal y and receive pilot data y_p as inputs.Both observations are supplied directly to the receiver for time-varying-channel processing.
- The receiver loss is computed at the receiver, so its training gradients are directly available.This makes receiver training straightforward within the end-to-end system.
- Jointly inputting y and y_p lets the receiver infer channel conditions while performing channel estimation and detection simultaneously.The procedure avoids explicitly estimating the channel as a separate operation.
B. Training Transmitter
A surrogate channel generator allows transmitter training through the receiver's end-to-end loss. The generator is learned adversarially by matching real-channel and generated-channel data.
- The transmitter is trained by back-propagating end-to-end cross-entropy gradients through the conditional-GAN channel generator.Transmitter weights are updated with SGD while the receiver and channel-generator weights remain fixed.
- During transmitter updates, the conditional-GAN channel generator acts as a surrogate channel.This makes transmitter training analogous to receiver training despite the channel stage.
- The channel generator trains jointly with the discriminator using real-channel data and fake data produced through the generator.The adversarial training objective is specified by Equation (2).
IV. EXPERIMENTS
The experiments evaluate the channel-agnostic approach on AWGN using conditional-GAN channel modeling and compare its recovery performance with a traditional coded system.
- The experiments compare the channel-agnostic learning approach with traditional methods designed from channel transfer functions.
- Model structures and parameters are listed in Table I, with Adam updates and a training batch size of 320.
- For AWGN, the channel output is modeled as y = x + w, so only the transmitter's encoded signal conditions the channel generator.
- The conditional GAN generates AWGN channel-output samples that are very similar to the actual channel output when conditioned on standard 16 QAM.
- The learning-based approach achieves BLER similar to Hamming (7,4) code with maximum-likelihood decoding on AWGN.Each transmission carries four information bits, and the transmitter output length is seven.
B. Rayleigh Fading Channel
The Rayleigh-fading experiments condition the channel generator and receiver on time-varying channel information, then evaluate generated samples and end-to-end reconstruction.
- For time-varying Rayleigh fading, additional conditioning information is supplied through either real h for coherent detection or received pilot data for joint estimation and detection.The channel is modeled as y_n = h_n · x_n + w_n, with h_n drawn from CN(0, 1).
- The conditional GAN generates samples with various means according to the supplied channel conditioning information.
- The end-to-end learning approach shows performance similar to traditional methods in both Rayleigh-fading tasks.
V. CONCLUSIONS
The paper concludes that conditional GANs can model channel effects for end-to-end learning without prior channel information, while pilot conditioning supports specific instantaneous channels.
- The conditional GAN models the channel conditional distribution from data, enabling end-to-end learning without prior channel information.
- Adding pilot information to the conditioning data allows the GAN to generate samples corresponding to the specific instantaneous channel.
- The end-to-end pipeline uses DNNs for the transmitter, channel generator, and receiver, trained iteratively to optimize supervised end-to-end loss.
- Simulations show performance similar to traditional approaches based on expert knowledge and channel models.
- The simulations cover only AWGN and Rayleigh fading channels, although the authors state that the method can be extended to other channels.