Channel Assignment in Uplink Wireless Communication Using Machine Learning Approach

• Published in: IEEE communications letters Vol. 24; no. 4; pp. 787 - 791
• Main Authors: Jia, Guangyu, Yang, Zhaohui, Lam, Hak-Keung, Shi, Jianfeng, Shikh-Bahaei, Mohammad
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Channel allocation; Computational geometry; Convex analysis; Convex functions; convex optimization; Convexity; deep learning; Machine learning; Machine learning algorithms; Neural networks; Operations research; Optimization; Resource allocation; Uplink; Wireless communication; Wireless communication systems; Wireless communications
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2020.2968902

This letter investigates a channel assignment problem in uplink wireless communication systems. Our goal is to maximize the sum rate of all users subject to integer channel assignment constraints. A convex optimization based algorithm is provided to obtain the optimal channel assignment, where the closed-form solution is obtained in each step. Due to high computational complexity in the convex optimization based algorithm, machine learning approaches are employed to obtain computational efficient solutions. More specifically, the data are generated by using convex optimization based algorithm and the original problem is converted to a regression problem which is addressed by the integration of convolutional neural networks (CNNs), feed-forward neural networks (FNNs), random forest and gated recurrent unit networks (GRUs). The results demonstrate that the machine learning method largely reduces the computation time with slightly compromising of prediction accuracy.