Intelligent Spectrum Management in 5G Mobile Networks based on Recurrent Neural Networks

Maksymyuk, Taras; Han, Longzhe; Larionov, Stanislav; Shubyn, Bohdan; Luntovskyy, Andriy; Klymash, Mykhailo

doi:10.1109/cadsm.2019.8779301

Cited by 19 publications

(11 citation statements)

References 8 publications

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“…Therefore, RNNs are suitable for IDSs to learn the different features of network traffic and recognize attacks. In particular, we employ an advanced RNN model, namely, the long short-term memory (LSTM) model [59,60]. The LSTM model is composed of complex cells, which allows the learning of both long-and short-term dependencies.…”

Section: Figure 1: General Architecture Of Autoencoder-based Representation Learning Of Traffic Featuresmentioning

confidence: 99%

Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems

Lutsiv¹,

Maksymyuk²,

Beshley³

et al. 2022

Computers, Materials &Amp; Continua

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The extensive proliferation of modern information services and ubiquitous digitization of society have raised cybersecurity challenges to new levels. With the massive number of connected devices, opportunities for potential network attacks are nearly unlimited. An additional problem is that many low-cost devices are not equipped with effective security protection so that they are easily hacked and applied within a network of bots (botnet) to perform distributed denial of service (DDoS) attacks. In this paper, we propose a novel intrusion detection system (IDS) based on deep learning that aims to identify suspicious behavior in modern heterogeneous information systems. The proposed approach is based on a deep recurrent autoencoder that learns time series of normal network behavior and detects notable network anomalies. An additional feature of the proposed IDS is that it is trained with an optimized dataset, where the number of features is reduced by 94% without classification accuracy loss. Thus, the proposed IDS remains stable in response to slight system perturbations, which do not represent network anomalies. The proposed approach is evaluated under different simulation scenarios and provides a 99% detection accuracy over known datasets while reducing the training time by an order of magnitude.

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Section: Figure 1: General Architecture Of Autoencoder-based Representation Learning Of Traffic Featuresmentioning

confidence: 99%

Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems

Lutsiv¹,

Maksymyuk²,

Beshley³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

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“…Various author between suggested a move from centralized (used in most 4G systems) to decentralized mobility management algorithms using DRL. DRL in 5G ably learns and builds knowledge about different dynamics of mmWave channels [11][12][13][14][15][16][17][18]. For instance, by interacting with environment data, the authors utilized DRL to observe the available resource at network edges and provide a resource allocation scheme.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, by interacting with environment data, the authors utilized DRL to observe the available resource at network edges and provide a resource allocation scheme. This enhances user mobility management at the edge given user mobility context, transitions, and signaling exchange [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26].…”

Section: Related Workmentioning

confidence: 99%

“…Exploiting various actor-critic different DRLs in [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27], the authors e.g., in [18] proposed to jointly solve offload and resource allocation problems in 5G networks. The authors in [12] used a deep Q-learning-based task offloading scheme to select optimal BSs for users and maximize task offloading utility.…”

Section: Related Workmentioning

confidence: 99%

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Enhancing Handover for 5G mmWave Mobile Networks Using Jump Markov Linear System and Deep Reinforcement Learning

Chiputa

Zhang

Ali

et al. 2022

Sensors

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The Fifth Generation (5G) mobile networks use millimeter waves (mmWaves) to offer gigabit data rates. However, unlike microwaves, mmWave links are prone to user and topographic dynamics. They easily get blocked and end up forming irregular cell patterns for 5G. This in turn causes too early, too late, or wrong handoffs (HOs). To mitigate HO challenges, sustain connectivity, and avert unnecessary HO, we propose an HO scheme based on a jump Markov linear system (JMLS) and deep reinforcement learning (DRL). JMLS is widely known to account for abrupt changes in system dynamics. DRL likewise emerges as an artificial intelligence technique for learning highly dimensional and time-varying behaviors. We combine the two techniques to account for time-varying, abrupt, and irregular changes in mmWave link behavior by predicting likely deterioration patterns of target links. The prediction is optimized by meta training techniques that also reduce training sample size. Thus, the JMLS–DRL platform formulates intelligent and versatile HO policies for 5G. When compared to a signal and interference noise ratio (SINR) and DRL-based HO scheme, our HO scheme becomes more reliable in selecting reliable target links. In particular, our proposed scheme is able to reduce wasteful HO to less than 5% within 200 training episodes compared to the DRL-based HO scheme that needs more than 200 training episodes to get to less than 5%. It supports longer dew time between HOs and high sum rates by ably averting unnecessary HOs with almost half the HOs compared to a DRL-based HO scheme.

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“…Authors in [12] used deep Q-learning based task offloading scheme to select optimal BSs for users and maximize task offloading utility. In [13], Q-learning integrates Mobility Robustness Optimization (MRO) scheme with Mobility-Load-Balancing (MLB) scheme to tackle traffic Load and speed effects in 5G. However, in all these schemes high mobile and dynamic users are hardly considered.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Handover for 5G Mobile Networks using Jump Markov Linear System and Deep Reinforcement Learning

Chiputa¹,

Zhang²,

Ali³

et al. 2021

Preprint

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The fifth Generation (5G) mobile networks use millimeter Waves (mmWaves) to offer giga bit data rates. However, unlike microwaves, mmWave links are prone to user and topographic dynamics. They easily get blocked and end up forming irregular cell patterns for 5G. This in turn cause too early, too late, or wrong handoffs (HOs). To mitigate HO challenges, sustain connectivity and avert unnecessary HO, we propose a HO scheme based on Jump Markov Linear System (JMLS) and Deep Reinforcement Learning (DRL). JMLS is widely known to account for abrupt changes in system dynamics. DRL likewise emerges as an artificial intelligence technique for learning highly dimensional and time-varying behaviors. We combine the two techniques to account for time-varying, abrupt, and irregular changes in mmWave link behaviour by predicting likely deterioration patterns of target links. The prediction is optimized by meta training techniques that also reduces training sample size. Thus, the JMLS-DRL platform formulates intelligent and versatile HO policies for 5G. Results show our proposed prediction scheme about target link behavior post HO to be highly reliable. The scheme also averts unnecessary HOs thus ably supports longer dew time.

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Intelligent Spectrum Management in 5G Mobile Networks based on Recurrent Neural Networks

Cited by 19 publications

References 8 publications

Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems

Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems

Enhancing Handover for 5G mmWave Mobile Networks Using Jump Markov Linear System and Deep Reinforcement Learning

Enhancing Handover for 5G Mobile Networks using Jump Markov Linear System and Deep Reinforcement Learning

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