Work

Deep Learning Based Communication: an Adversarial Approach

Year: 2019

Type: other

Abstract: Deep learning based communication using autoencoder have revolutionized the design of physical layer in wireless communication. In this paper, we propose an adversarial autoencoder to mitigate vulnerability of autoencoder against adversarial attacks. Results confirm the effectiveness of adversarial training by reducing block error rate(BLER) from 90 percent to 56 percent.

Source: Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)

Authors: Yousef Emami, Rahim Taheri

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W2967747914

Cites: 3

Cited by:

Related to: 10