Adaptive filterbank-based multi-carrier waveform design for flexible data rates

Catak, Evren; Durak–Ata, Lütfiye

doi:10.1016/j.compeleceng.2016.11.039

Cited by 9 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, 5G will be essential for Internet of Things (IoT) applications and future mobile networks. There are many challenges in the design of 5G networks [10], including a security scheme for beamforming prediction. It is a critical part of wireless networks, which has been studied in communication systems and signal processing.…”

Section: Preamblementioning

confidence: 99%

The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining

Kuzlu¹,

Çatak²,

Cali³

et al. 2022

Preprint

View full text Add to dashboard Cite

The design of a security scheme for beamforming prediction is critical for next-generation wireless networks (5G, 6G, and beyond). However, there is no consensus about protecting the beamforming prediction using deep learning algorithms in these networks. This paper presents the security vulnerabilities in deep learning for beamforming prediction using deep neural networks (DNNs) in 6G wireless networks, which treats the beamforming prediction as a multi-output regression problem. It is indicated that the initial DNN model is vulnerable against adversarial attacks, such as Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), Projected Gradient Descent (PGD), and Momentum Iterative Method (MIM), because the initial DNN model is sensitive to the perturbations of the adversarial samples of the training data. This study also offers two mitigation methods, such as adversarial training and defensive distillation, for adversarial attacks against artificial intelligence (AI)-based models used in the millimeter-wave (mmWave) beamforming prediction. Furthermore, the proposed scheme can be used in situations where the data are corrupted due to the adversarial examples in the training data. Experimental results show that the proposed methods effectively defend

show abstract

Section: Preamblementioning

confidence: 99%

The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining

Kuzlu¹,

Çatak²,

Cali³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Introducing the 5G with super-fast data-speeds is a breakthrough and significant transform in mobile networking and data communication. It offers a data transmission speed of 20 times faster than the 4G networks and delivers less than a millisecond data latency [2], [3], [4]. The main difference of 5G is to use a new digital technology called massive multiple-input multiple-output (MIMO) and using multiple targeted beams to spotlight [5], [6].…”

Section: Introductionmentioning

confidence: 99%

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

Çatak¹,

Catak²,

Kuzlu³

et al. 2021

Preprint

View full text Add to dashboard Cite

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

show abstract

“…The new waveforms such as filter bank-based multicarrier (FBMC), generalized frequency division multiplexing (GFDM), universal filtered multicarrier (UFMC) are proposed to solve the drawbacks of the OFDM. Accordingly, FBMC is the waveform that utilizes the low-pass filters, which are shifted in the frequency domain [7,8]. Unlike FBMC, more than one subcarriers are filtered instead of filtering every subcarrier.…”

Section: Introductionmentioning

confidence: 99%

Transceiver Design for GFDM with Hexagonal Time–Frequency Allocation Using the Polyphase Decomposition

2020

Self Cite

View full text Add to dashboard Cite

Generalized frequency division multiplexing (GFDM) is a waveform for the next-generation communication systems to succeed in the drawbacks of orthogonal frequency division multiplexing (OFDM). The symbols of users are transmitted with the time- and frequency-shifted versions of a prototype filter. According to filtering operation, the computational complexity and processing load are high for the devices that suffer from energy consumption. The communication systems are required to support the new generation devices that need low energy consumption and low latency issues. Motivated by such demands of the next-generation communication system, we propose a novel GFDM waveform that we call hexagonal GFDM. The contributions of the hexagonal GFDM are that it: (i) supports short transmission time based on its hexagonal time–frequency allocations; and (ii) provides low latency communication with low computational complexity manner. Furthermore, we design a transmitter and receiver structure in a less complicated way with mathematical derivation by using polyphase decomposition and Fourier transform (FT) transformation. The proposed systems are realized analytically and investigated over Rayleigh fading channel model through computer simulations.

show abstract

Adaptive filterbank-based multi-carrier waveform design for flexible data rates

Cited by 9 publications

References 11 publications

The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining

The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining

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

Transceiver Design for GFDM with Hexagonal Time–Frequency Allocation Using the Polyphase Decomposition

Contact Info

Product

Resources

About