Article Sidebar
Published:
Nov 6, 2023Keywords:
Article Details
Tanzania Journal of Engineering and Technology (Tanz. J. Engrg. Technol.) publications are written as a basis for academic discussions. Whereas effort is made by the Editorial Board to screen out inaccurate or misleading statements, data or opinions, Tanz. J. Engrg. Technol. wishes to make it clear that the contents of the articles are the sole responsibility of the authors. Accordingly, the Editorial Board and editors, publisher, College of Engineering and Technology and the University of Dar es Salaam accept no responsibility or liability whatsoever for the opinions, inaccurate or misleading data or statement and views expressed by the authors.
However, the College of Engineering and Technology, the publisher of the Journal, reserve all rights of the articles published in the Journal. Whereas the contents of the articles are available electronically without cost, no part of the publication may be transmitted in any form or reproduced electronically or mechanically, except for short extract in critical scholarly review, research or discourse with an acknowledgement.
Main Article Content
A Multitask Learning Framework for Pilot Decontamination in 5G Massive MIMO
Abstract
Reference signals enable the acquisition of channel state information (CSI) for purposes such as channel estimation, beam selection, precoding, and symbol detection in 5G massive multiple-input multiple-output (MAMIMO) systems. Eventually, as more and more users and cells are added, orthogonal reference signals become few which leads to pilot contamination. Pilot contamination limits the performance and occurs when non-orthogonal reference signals occupy time-frequency resources that are alike. Learning-based techniques have been proposed to alleviate it. However, each can only learn to perform a single task namely pilot assignment, power allocation, pilot design, or de-noising for pilot decontamination. In addition, each learner can only be successful if postulated conditions are met. This study proposes a multitask learning framework that can be trained to dynamically select from the multitude of deep learning models which have been suggested for pilot decontamination. Under all signal-to-noise (SNR) ratios, experiments conducted on the deep residual learning aided channel estimator using the multitask learning framework showed minimum channel estimation errors compared to single-task learning.
