Identification of The Number of Wireless Channel Taps Using Deep Neural Networks

TL;DR

This paper presents a deep neural network approach to accurately identify the number of wireless channel taps, improving upon existing algorithms by analyzing transmitted and received signals for better channel estimation.

Contribution

The work introduces a modified DNN model for channel tap identification and demonstrates its superior performance over the SWISS algorithm.

Findings

DNN outperforms SWISS in identifying channel taps

The modified DNN converges efficiently using only transmitted and received signals

Enhanced channel impulse response estimation accuracy

Abstract

In wireless communication systems, identifying the number of channel taps offers an enhanced estimation of the channel impulse response (CIR). In this work, efficient identification of the number of wireless channel taps has been achieved via deep neural networks (DNNs), where we modified an existing DNN and analyzed its convergence performance using only the transmitted and received signals of a wireless system. The displayed results demonstrate that the adopted DNN accomplishes superior performance in identifying the number of channel taps, as compared to an existing algorithm called Spectrum Weighted Identification of Signal Sources (SWISS).