Channel Type Recognition in Wireless Communications: A Deep Learning Approach

TL;DR

This paper introduces two deep learning algorithms for real-time wireless channel type recognition in 5G networks, demonstrating high accuracy and improved performance over existing methods.

Contribution

The paper presents two novel deep learning algorithms for wireless channel type recognition, utilizing single-task and multi-task learning approaches for improved accuracy.

Findings

High classification accuracy achieved

Outperforms baseline WCT determination scheme

Results show improved throughput and block error rate

Abstract

In this paper, we propose two novel and practical deep-learning-based algorithms to solve the wireless channel type (WCT) recognition problem. Specifically, the WCT recognition problem is recast as a classification problem in deep learning due to their similarities, where a deep neural network (DNN) is trained off-line with a diversity of typical WCTs for fifth-generation (5G) and beyond-5G wireless communications, which is then utilized to perform online WCT determination. In the first algorithm, one WCT is regarded as a single task. While in the second scheme, one WCT is jointly characterized by several independent features, each of which is treated as a task and is classified respectively by training a DNN in a multi-task-learning manner, and the final WCT is identified by the combination of those channel features. Simulation results show that the proposed algorithms can classify various WCTs instantaneously with high accuracy, result in satisfactory block error rate and throughput, and outperform a representative baseline WCT determination scheme.