计算机科学
正规化(语言学)
人工神经网络
人工智能
深度学习
频道(广播)
无线
钥匙(锁)
传输(电信)
宽带
过程(计算)
机器学习
电子工程
计算机网络
电信
工程类
操作系统
计算机安全
作者
Tomer Fireaizen,Gal Metzer,Dan Ben-David,Yair Moshe,Israel Cohen,Emil Björnson
标识
DOI:10.1109/icc45855.2022.9838732
摘要
An Intelligent Reflecting Surface (IRS) is an emerging technology for improving the data rate over wireless channels by controlling the underlying channel. In this paper, we describe a novel solution for IRS configuration to maximize the data rate over wideband channels. The optimization is obtained by online training of a deep generative neural network. Inspired by related works in image processing, this network is randomly initialized and acts as a regularization term for the optimization process since the structure of the generator is sufficient to capture a great deal of IRS statistics prior to any learning. In contrast to recent deep learning techniques for IRS configuration, the proposed technique does not require an offline training stage and can adapt quickly to any environment. Compared to the previous state-of-the-art algorithm, the proposed method is significantly faster and obtains IRS configurations that achieve higher data transmission rates.
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