.png){kind=icon}
.png){kind=icon}
Return
Capacity-Driven Autoencoders for Communications
.png){kind=icon}
OA
AI
DOI：10.1109/OJCOMS.2021.3087815.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The autoencoder concept has fostered the reinterpretation and the design of modern communication systems. It consists of an encoder, a channel and a decoder block that modify their internal neural structure in an end-to-end learning fashion. However, the current approach to train an autoencoder relies on the use of the cross-entropy loss function. This approach can be prone to overfitting issues and often fails to learn an optimal system and signal representation (code). In addition, less is known about the autoencoder ability to design channel capacity-approaching codes, i.e., codes that maximize the input-output mutual information under a certain power constraint. The task being even more formidable for an unknown channel for which the capacity is unknown and therefore it has to be learnt. In this paper, we address the challenge of designing capacity-approaching codes by incorporating the presence of the communication channel into a novel loss function for the autoencoder training. In particular, we exploit the mutual information between the transmitted and received signals as a regularization term in the cross-entropy loss function, with the aim of controlling the amount of information stored. By jointly maximizing the mutual information and minimizing the cross-entropy, we propose a theoretical approach that a) computes an estimate of the channel capacity and b) constructs an optimal coded signal approaching it. Theoretical considerations are made on the choice of the cost function and the ability of the proposed architecture to mitigate the overfitting problem. Simulation results offer an initial evidence of the potentiality of the proposed method.
Keywords:
Mutual information
Decoding
Channel capacity
Training
Task analysis
Cost function
Receivers
Digital communications
physical layer
statistical learning
autoencoders
coding theory
mutual information
channel capacity
explainable machine learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
4.3
Papers:
1.7K
Citations:
991