Security concerns on machine learning solutions for 6G networks in mmWave beam prediction

Çatak, Ferhat Özgür; Kuzlu, Murat; Catak, Evren; Cali, Ümit; Ünal, Devrim

doi:10.1016/j.phycom.2022.101626

Cited by 41 publications

(25 citation statements)

References 24 publications

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“…These calculations explain why BGS-Net has good performance on i.i.d Gaussian channels. Moreover, it is better than MMNet-iid's I − θ (1) t H T H on correlated channels, where channel hardening disappears when the channel is correlated and there is no way for I − θ (1) t H T H to converge to 0 as the number of antennas increases, while I − (D + L) −1 (H T H + σ 2 2 I) , since A is symmetric and D + L itself contains all the information in A . When the number of antennas increases, it can be approximated as I − A −1 A , tending to 0 but not to 0.…”

Section: Error Analysismentioning

confidence: 98%

“…t H T . It has two training parameters (θ (1) t , θ (2) t ) , and its complexity is much smaller than OAMPNet. MMNet-iid performs well when the number of antennas is large and the linear Gaussian channel is good, and has poor performance in correlated channels or when the number of antennas is low.…”

Section: Mmnet-iidmentioning

confidence: 99%

“…9 Block matrix approximation of A small matrice in order is set to D (2,t) ∈ R m UE ×m UE , (t ∈ [1 : T]) respectively. The matrix A is again divided into 4 matrices, with the upper left and lower right matrices set to D (1,1) , D (1,2) ∈ R M/2×M/2 respectively. To ensure convergence of the Neumann Series, here, unlike in [32], the parameter α opt = 1 + η , η = M/N was introduced [39].…”

Section: Approximation Of a −1mentioning

confidence: 99%

“…(1,1) has both diagonal elements and non-diagonal elements, unlike only diagonal elements under channel hardening conditions. In this paper, the Neumann Series is used to approximate it [32,40] by the following method: use E to approximate the off-diagonal block part of D (1,1) and use N to approximate D −1 In this way we can get the required D −1 . The same can be obtained:…”

Section: Approximation Of a −1mentioning

confidence: 99%

“…These algorithms are also being used in communication technologies to improve system performance in terms of spectrum utilization, latency and security. With the rapid development of machine learning techniques, especially deep learning techniques, it is crucial to consider symbol error ratio (SER) and complexity when applying algorithms [1]. Massive multi-input multi-output (MIMO) is a key technology in B5G, where tens, hundreds or thousands of antennas are equipped at the base station.…”

mentioning

confidence: 99%

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Low-complexity signal detection networks based on Gauss-Seidel iterative method for massive MIMO systems

Yao

Song

et al. 2022

EURASIP J. Adv. Signal Process.

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In massive multiple-input multiple-output (MIMO) systems with single- antenna user equipment (SAUE) or multiple-antenna user equipment (MAUE), with the increase of the number of received antennas at base station, the complexity of traditional detectors is also increasing. In order to reduce the high complexity of parallel running of the traditional Gauss-Seidel iterative method, this paper proposes a model-driven deep learning detector network, namely Block Gauss-Seidel Network (BGS-Net), which is based on the Gauss-Seidel iterative method. We reduce complexity by converting a large matrix inversion to small matrix inversions. In order to improve the symbol error ratio (SER) of BGS-Net under MAUE system, we propose Improved BGS-Net. The simulation results show that, compared with the existing model-driven algorithms, BGS-Net has lower complexity and similar the detection performance; good robustness, and its performance is less affected by changes in the number of antennas; Improved BGS-Net can improve the detection performance of BGS-Net.

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Section: Error Analysismentioning

confidence: 98%

Section: Mmnet-iidmentioning

confidence: 99%

Section: Approximation Of a −1mentioning

confidence: 99%

Section: Approximation Of a −1mentioning

confidence: 99%

mentioning

confidence: 99%

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Low-complexity signal detection networks based on Gauss-Seidel iterative method for massive MIMO systems

Yao

Song

et al. 2022

EURASIP J. Adv. Signal Process.

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Artificial intelligence enabled Luong Attention and Hosmer Lemeshow Regression Window‐based attack detection in 6G

Bhuvaneshwari¹,

Balusamy

Dhanaraj

et al. 2023

Int J Communication

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SummaryRegardless of the developments of networking and communication technologies, security is without exception a predominant feature to ensure network reliability. The future sixth‐generation (6G) network is anticipated to be carried out with artificial intelligence (AI) powered communication via machine learning (ML), post‐quantum cryptography, and so on. AI‐powered communication has been in recent years utilized in enhancing network traffic performance with respect to resource management, optimal frequency spectrum design, security, and latency. The studies of modern wireless communications and anticipated features of 6G networks revealed a prerequisite for designing a trustworthy attack detection mechanism. In this work, a method called, Luong Attention and Hosmer Lemeshow Regression Window‐based (LA‐HLRW) attack detection in 6G is proposed. Initially, with the raw Botnet Attack dataset obtained as input, preprocessing is performed to normalize network traffic features. Next, the dimensionality of network traffic feature of large‐scale network traffic data is reduced using the Luong Attention integrated with Long Short Term Memory (LSTM)‐based Feature extraction model. Finally, with the objective of classifying network traffic samples for attack detection in 6G, we analyze the low dimensional network traffic feature set produced by Luong Attention integrated with LSTM using the Hosmer Lemeshow Logistic Regression Window‐based Attack Detection model. Extensive experiments are performed with the Botnet Attack dataset to validate the efficiency of the proposed LA‐HLRW method by using different parameters such as attack detection accuracy, attack detection time, precision, and recall. The overall analysis of proposed LA‐HLRW results significantly reduced the attack detection time by 24%, and additionally improved attack detection accuracy, precision, and recall by 5%, 5%, and 6% as compared to existing attack detection methods respectively.

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Evolution toward intelligent communications: Impact of deep learning applications on the future of 6G technology

Abd Elaziz,

Al‐qaness,

Dahou

et al. 2023

WIREs Data Min & Knowl

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The sixth generation (6G) represents the next evolution in wireless communication technology and is currently under research and development. It is expected to deliver faster speeds, reduced latency, and greater capacity compared to the current 5G wireless technology. 6G is envisioned as a technology capable of establishing a fully data‐driven network, proficient in analyzing and optimizing end‐to‐end behavior and handling massive volumes of real‐time data at rates of up to terabits per second (Tb/s). Moreover, 6G is designed to accommodate an average of 1000+ substantial connections per person over the course of a decade. The concept of a data‐driven network introduces a new service paradigm, which offers fresh opportunities for applications within 6G wireless communication and network design in the future. This paper aims to provide a survey of existing applications of 6G that are based on deep learning techniques. It also explores the potential, essential technologies, scenarios, challenges, and related topics associated with 6G. These aspects are crucial for meeting the requirements for the development of future intelligent networks. Furthermore, this work delves into various research gaps between deep learning and 6G that remain unexplored. Different potential deep learning applications for 6G networks, including privacy, security, environmentally friendly communication, sustainability, and various wireless applications, are discussed. Additionally, we shed light on the challenges and future trends in this field.This article is categorized under: Technologies > Computational Intelligence Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning

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Security concerns on machine learning solutions for 6G networks in mmWave beam prediction

Cited by 41 publications

References 24 publications

Low-complexity signal detection networks based on Gauss-Seidel iterative method for massive MIMO systems

Low-complexity signal detection networks based on Gauss-Seidel iterative method for massive MIMO systems

Artificial intelligence enabled Luong Attention and Hosmer Lemeshow Regression Window‐based attack detection in 6G

Evolution toward intelligent communications: Impact of deep learning applications on the future of 6G technology

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