Channel Assignment in Uplink Wireless Communication using Machine Learning Approach

TL;DR

This paper addresses uplink wireless channel assignment by combining convex optimization and machine learning techniques, significantly reducing computation time while maintaining near-optimal performance.

Contribution

It introduces a machine learning framework using CNNs, FNNs, random forests, and GRUs to efficiently solve the channel assignment problem based on convex optimization data.

Findings

Machine learning methods reduce computation time substantially.

Prediction accuracy is slightly compromised compared to convex optimization.

Multiple neural network architectures are evaluated for this task.

Abstract

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.