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access icon free Hybrid deep learning-based throughput analysis for UAV-assisted cellular networks

© The Institution of Engineering and Technology
Received 18/04/2020, Accepted 04/01/2021, Revised 22/11/2020, Published 08/01/2021

Mobile users are interested in utilising high network capabilities without time and place constraints. However, with a high level of interest in the usage of mobile phones and internet facilities, the limited capacity of terrestrial base stations (BSs) is unbalanced. As a potential alternative to BSs, unmanned aerial vehicles (UAVs) are emerging as a means of transmitting wireless data to ground mobile users. As an air-to-ground communication network, the real UAVs deployed and collected communication data from ground mobile users. The main objective of this study is to analyse and evaluate user throughput, interference, and power transmission when the UAVs are at different heights. The parameters used include the locations of the UAVs and users, the altitudes and elevation angles from the users to UAVs, signal-to-noise-ratio, throughput values, the categories of line-of-sight, and non-line-of-sight links. Furthermore, K-means used as a clustering method for class identification, long short-term memory (LSTM), and gated recurrent unit (GRU) to analyse and evaluate system performance. The system's performance was compared with a multi-layer perceptron approach. The evaluation results show that the proposed LSTM–GRU provides reliable and encouraging performance with low computational complexity, which is appropriate for heterogeneous networks.

Inspec keywords: autonomous aerial vehicles; recurrent neural nets; feature extraction; telecommunication computing; multilayer perceptrons; cellular radio; learning (artificial intelligence)

Other keywords: place constraints; mobile phones; unmanned aerial vehicles; LSTM–GRU hybrid; BSs; communication data; UAV-assisted cellular networks; air-to-ground communication network; wireless data; nonline-of-sight links; terrestrial base stations; heterogeneous networks; ground mobile users; hybrid deep learning-based; high network capabilities

Subjects: Mobile radio systems; Neural nets; Aerospace engineering computing; Mobile robots; Communications computing; Telerobotics

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