Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training

Author:

Manoj B. R.1ORCID,Sadeghi Meysam1ORCID,Larsson Erik G.2ORCID

Affiliation:

1. Linköping University, Linköping, Sweden

2. Department of Electrical Engineering (ISY), Linköping University, Linköping, Sweden

Funder

Security-Link and the SSF SURPRISE project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Unmasking Vulnerabilities: Adversarial Attacks against DRL-based Resource Allocation in O-RAN;ICC 2024 - IEEE International Conference on Communications;2024-06-09

2. Input Agnostic Trojan Attack for Deep Learning-Based Wireless Signal Classification;2024 National Conference on Communications (NCC);2024-02-28

3. Adversarial attack and training for deep neural network based power quality disturbance classification;Engineering Applications of Artificial Intelligence;2024-01

4. Defending Adversarial Attacks on Deep Learning-Based Power Allocation in Massive MIMO Using Denoising Autoencoders;IEEE Transactions on Cognitive Communications and Networking;2023-08

5. Towards an Adversarial Machine Learning Framework in Cyber-Physical Systems;2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA);2023-05-23

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