Improved Gaussian-Bernoulli Restricted Boltzmann Machines for UAV-Ground Communication Systems

TL;DR

This paper introduces an advanced deep learning framework using Gaussian-Bernoulli Restricted Boltzmann Machines and autoencoders to improve channel state information prediction for UAV-ground communication systems, addressing dynamic channel challenges.

Contribution

The paper presents a novel combination of GBRBM and autoencoder-based DNNs for effective CSI prediction in UAV communications, outperforming traditional methods.

Findings

Accurate CSI prediction across various UAV scenarios.

Outperforms conventional deep neural networks.

Validated with real UAV communication data.

Abstract

Unmanned aerial vehicle (UAV) is steadily growing as a promising technology for next-generation communication systems due to their appealing features such as wide coverage with high altitude, on-demand low-cost deployment, and fast responses. UAV communications are fundamentally different from the conventional terrestrial and satellite communications owing to the high mobility and the unique channel characteristics of air-ground links. However, obtaining effective channel state information (CSI) is challenging because of the dynamic propagation environment and variable transmission delay. In this paper, a deep learning (DL)-based CSI prediction framework is proposed to address channel aging problem by extracting the most discriminative features from the UAV wireless signals. Specifically, we develop a procedure of multiple Gaussian Bernoulli restricted Boltzmann machines (GBRBM) for dimension reduction and pre-training utilization incorporated with an autoencoder-based deep neural networks (DNNs). To evaluate the proposed approach, real data measurements from an UAV communicating with base-stations within a commercial cellular network are obtained and used for training and validation. Numerical results demonstrate that the proposed method is accurate in channel acquisition for various UAV flying scenarios and outperforms the conventional DNNs.