Neural Networks Applied for Broadcast Channels

Mohammad Abuabdoh
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Abstract

The famous mathematical definition of the capacity region of broadcast channels assumes that the channel is a degraded. However, not all wireless communication channels can be mathematically represented as degrades ones. Motivated by the artificial intelligence revolution, this paper provides a novel approach for tackling this problem. In particular, this paper utilizes artificial neural networks for providing a solution for the problem of converting a broadcast channel into a degraded one. Creating a degraded channel implies modeling the broadcast channel as a series of cascaded channels connecting the transmitter to the receivers sequentially which in turn implies finding a middle (relay) channel connecting the receivers. This process of finding the middle channel is performed in this paper utilizing different models of neural networks. This approach is applied for binary symmetric channels and Gaussian channel. For the Gaussian case, this paper provides a novel approach for establishing the middle channel by providing an estimation of the distribution of the middle channel itself, not only the crossover probability or the variance of the distribution. As a result, several possible extensions for practical channels (like Rayleigh) is suggested. Furthermore, this paper provides an abundant evidence that artificial intelligent is capable of modernizing classical information theory that faces major problems with the complexity of the analytical solutions.
神经网络在广播频道中的应用
著名的广播信道容量区域的数学定义假设信道是降级的。然而,并不是所有的无线通信信道都可以在数学上表示为降级信道。在人工智能革命的推动下,本文为解决这一问题提供了一种新的方法。特别地,本文利用人工神经网络为广播信道转换为退化信道的问题提供了解决方案。创建降级信道意味着将广播信道建模为一系列级联信道,依次连接发射器和接收器,这反过来意味着找到连接接收器的中间(中继)信道。本文利用不同的神经网络模型来完成寻找中间信道的过程。该方法适用于二进制对称信道和高斯信道。对于高斯分布的情况,本文提供了一种建立中间信道的新方法,通过对中间信道本身的分布进行估计,而不仅仅是交叉概率或分布的方差。因此,对实际通道(如Rayleigh)提出了几种可能的扩展。此外,本文还提供了大量的证据,表明人工智能能够使经典信息论现代化,而经典信息论面临着解析解复杂的主要问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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