Title: A comparative performance analysis of different machine learning techniques for SNR prediction in microcell and picocell wireless environment
Authors: Nikola Sekulović; Miloš Stojanović; Aleksandra Panajotović; Miloš Banđur
Addresses: Department of Communication Technologies, College of Applied Technical Sciences, Aleksandra Medvedeva 20, 18000 Niš, Serbia ' Department of Modern Computer Technologies, College of Applied Technical Sciences, Aleksandra Medvedeva 20, 18000 Niš, Serbia ' Department of Telecommunications, Faculty of Electronic Engineering, University of Niš, Aleksandra Medvedeva 14, 18000 Niš, Serbia ' Department of Power, Electronic and Telecommunication Engineering, Faculty of Technical Sciences, University of Priština, Knjaza Miloša 7, 38220 Kosovska Mitrovica, Serbia
Abstract: Knowledge of propagation channel conditions enables adaptive data transmission which improves the quality and efficiency of communication system. Wireless channels are characterised by highly dynamic time-varying nature. This means that information regarding propagation channel condition obtained by channel estimation can become outdated because of delay caused by processing and feedback phases. In fast fading environments, prediction of channel based on channel states in previous moments can ensure timely information. In this paper, a comparative performance analysis of an echo state network (ESN), an extreme learning machine (ELM) and least squares support vector machines (LS-SVM) for prediction of wireless channel conditions for single-input single-output (SISO) systems in microcellular and picocellular environments is carried out. Normalised mean squared error (NMSE) and time consumption are used as performance indicators. The experimental results on measured values for signal-to-noise ratio (SNR) show that all models have better and comparable prediction accuracy in microcell environment, while prediction framework based on the ESN outperforms the others in picocell environment.
Keywords: channel prediction; echo state network; extreme learning machines; ELMs; least squares support vector machines; LS-SVMs; microcellular environment; picocellular environment.
International Journal of Reasoning-based Intelligent Systems, 2021 Vol.13 No.4, pp.212 - 218
Received: 28 Mar 2020
Accepted: 13 Aug 2020
Published online: 30 Oct 2021 *