Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam Prediction

6G – sixth generation – is the latest cellular technology currently under development for wireless communication systems. In recent years, machine learning (ML) algorithms have been applied widely in various fields, such as healthcare, transportation, energy, autonomous cars, and many more. Those algorithms have also been used in communication technologies to improve the system performance in terms of frequency spectrum usage, latency, and security. With the rapid developments of ML techniques, especially deep learning (DL), it is critical to consider the security concern when applying the algorithms. While ML algorithms offer significant advantages for 6G networks, security concerns on artificial intelligence (AI) models are typically ignored by the scientific community so far. However, security is also a vital part of AI algorithms because attackers can poison the AI model itself. This paper proposes a mitigation method for adversarial attacks against proposed 6G ML models for the millimeter-wave (mmWave) beam prediction using adversarial training. The main idea behind generating adversarial attacks against ML models is to produce faulty results by manipulating trained DL models for 6G applications for mmWave beam prediction. We also present a proposed adversarial learning mitigation method’s performance for 6G security in mmWave beam prediction application a fast gradient sign method attack. The results show that the defended model under attack’s mean square errors (i.e., the prediction accuracy) are very close to the undefended model without attack

DL-based CSI Feedback and Cooperative Recovery in Massive MIMO

In this paper, we exploit the correlation between nearby user equipment (UE) and develop a deep learning-based channel state information (CSI) feedback and cooperative recovery framework, CoCsiNet, to reduce the feedback overhead. The CSI information can be divided into two parts: shared by nearby UE and owned by individual UE. The key idea of exploiting the correlation is to reduce the overhead used to repeatedly feedback shared information. Unlike in the general autoencoder framework, an extra decoder and a combination network are added at the base station to recover the shared information from the feedback CSI of two nearby UE and combine the shared and individual information, respectively, but no modification is performed at the UEs. For a UE with multiple antennas, we also introduce a baseline neural network architecture with long short-term memory modules to extract the correlation of nearby antennas. Given that the CSI phase is not sparse, we propose two magnitude-dependent phase feedback strategies that introduce statistical and instant CSI magnitude information to the phase feedback process, respectively. Simulation results on two different channel datasets show the effectiveness of the proposed CoCsiNet.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Defending Adversarial Attacks on Deep Learning Based Power Allocation in Massive MIMO Using Denoising Autoencoders

Recent work has advocated for the use of deep learning to perform power allocation in the downlink of massive MIMO (maMIMO) networks. Yet, such deep learning models are vulnerable to adversarial attacks. In the context of maMIMO power allocation, adversarial attacks refer to the injection of subtle perturbations into the deep learning model's input, during inference (i.e., the adversarial perturbation is injected into inputs during deployment after the model has been trained) that are specifically crafted to force the trained regression model to output an infeasible power allocation solution. In this work, we develop an autoencoder-based mitigation technique, which allows deep learning-based power allocation models to operate in the presence of adversaries without requiring retraining. Specifically, we develop a denoising autoencoder (DAE), which learns a mapping between potentially perturbed data and its corresponding unperturbed input. We test our defense across multiple attacks and in multiple threat models and demonstrate its ability to (i) mitigate the effects of adversarial attacks on power allocation networks using two common precoding schemes, (ii) outperform previously proposed benchmarks for mitigating regression-based adversarial attacks on maMIMO networks, (iii) retain accurate performance in the absence of an attack, and (iv) operate with low computational overhead.Comment: This work is currently under review for publicatio