Work

A Novel Approach Based on Generative Adversarial Network for Interference Detection in Wireless Communications

Year: 2022

Type: article

Abstract: With the rapid growth of wireless devices, the communication environment gets complex. The detection of interference or unauthorized signals can improve spectrum efficiency, which is a key technology for limited spectrum resources. Traditional detection methods analyze the parameter characteristics of the received signal. But it is difficult to detect interference with the same time and frequency as the original signal by those feature engineering. As a classical problem in deep learning, anomaly detection is usually solved by supervised learning. But a more challenging situation is to detect unknown or invisible anomalies. It means that the number of abnormal samples is insufficient and the data is highly biased toward the normal samples. In this paper, a wireless communication interference detection algorithm based on generative adversarial network (GAN) is proposed. In the semi‐supervised learning scenario, the algorithm detects the time‐frequency overlapped interference by the reconstruction strategy. The generator adopts the encoder‐decoder‐encoder architecture. In the training process, the model jointly learns the data distribution of normal samples by minimizing the distance in both the signal space and the latent space. In the inference phase, a large distance metric implies an abnormal sample. Experiments on simulated communication datasets show the superiority of the proposed algorithm.

Source: Wireless Communications and Mobile Computing

DOI: 10.1155/2022/7050573

Authors: Shoushuai He, Lei Zhu, Changhua Yao, Weijun Zeng, Zhen Qin

Institutions: PLA Army Engineering University, Nanjing University of Information Science and Technology

Language: English

Topic: Wireless Signal Modulation Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,550