Secure resource management in beyond 5G heterogeneous networks with decoupled access

Khan, Humayun Zubair; Ali, Mudassar; Naeem, Muhammad; Rashid, Imran; Mumtaz, Shahid; Khan, Adnan Ahmad; Akhtar, Ahmad Naeem

doi:10.1016/j.adhoc.2021.102737

Cited by 21 publications

(8 citation statements)

References 23 publications

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“…To all independents implementation of Eq. ( 12), x chebychev 0 represents the outcome arbitrarily [22,23]. The chaotic value x chebychev j created utilizing a logistical map with 300 runs and x chebychev 0 ¼ 0:001.…”

Section: Hyperparameter Tuning Of Dbn Modelmentioning

confidence: 99%

Deep Learning Based Autonomous Transport System for Secure Vehicle and Cargo Matching

Shanthi¹,

Ramprasath²,

Kavitha³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The latest 6G improvements secured autonomous driving's realism in Intelligent Autonomous Transport Systems (IATS). Despite the IATS's benefits, security remains a significant challenge. Blockchain technology has grown in popularity as a means of implementing safe, dependable, and decentralised independent IATS systems, allowing for more utilisation of legacy IATS infrastructures and resources, which is especially advantageous for crowdsourcing technologies. Blockchain technology can be used to address security concerns in the IATS and to aid in logistics development. In light of the inadequacy of reliance and inattention to rights created by centralised and conventional logistics systems, this paper discusses the creation of a blockchain-based IATS powered by deep learning for secure cargo and vehicle matching (BDL-IATS). The BDL-IATS approach utilises Ethereum as the primary blockchain for storing private data such as order and shipment details. Additionally, the deep belief network (DBN) model is used to select suitable vehicles and goods for transportation. Additionally, the chaotic krill herd technique is used to tune the DBN model's hyperparameters. The performance of the BDL-IATS technique is validated, and the findings are inspected under a variety of conditions. The simulation findings indicated that the BDL-IATS strategy outperformed recent state-of-the-art approaches.

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Section: Hyperparameter Tuning Of Dbn Modelmentioning

confidence: 99%

Deep Learning Based Autonomous Transport System for Secure Vehicle and Cargo Matching

Shanthi¹,

Ramprasath²,

Kavitha³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…For instance, intelligent matching theory is used in [8] to address user access problems in ultra-dense HetNets to boost energy efficiency. Dual decomposition is employed in [9] to allocate small cell frequencies in heterogeneous ultradense networks to improve spectrum efficiency. Reinforcement learning is used in [10] to control the downlink and maximize network capacity while improving user experience.…”

Section: Udn Resource Allocation Application Scenariosmentioning

confidence: 99%

Resource allocation trends for ultra dense networks in 5G: a comprehensive survey

jiangliang,

donghui,

siyi

2023

Third International Conference on Digital Signal and Computer Communications (DSCC 2023)

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With the widespread application of the fifth-generation mobile communication system, mobile data traffic has seen rapid growth, bringing new challenges to traditional cellular networks. Ultra-dense networks (UDN) are one of the key technologies used in 5G communication to address user capacity and high-speed transmission. How to effectively solve the problems of inter-site interference, spectrum resource scarcity, and high energy consumption in UDN has become a major research topic, and resource allocation technology is essential in this regard. This article summarizes the spectrum resource allocation methods and applications in UDN through literature review. Firstly, the applicable scenarios, models, and various indicators of resource allocation in UDN are summarized and analyzed. Secondly, this paper systematically sorts and classifies recent research on resource allocation methods for UDNs, analyzing their applicability, advantages, and disadvantages. Additionally, the paper discusses the future development of spectrum resource allocation methods for UDNs.

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“…Small cells can be found scattered throughout the network, in addition to macrocells, which are the most common type. A smaller number of antennas is used in small cell base stations (BS) compared to a more significant number of antennas used in macro base stations (MBS) [2]. Microcells and massive multiple-input multipleoutput (MMaMIMO) technology, which are currently in development, have the potential to have a significant impact on future wireless networks [3].…”

Section: Introductionmentioning

confidence: 99%

Millimeter Wave Massive MIMO Heterogeneous Networks Using Fuzzy-Based Deep Convolutional Neural Network (FDCNN)

Alaaedi¹,

Sabaei²

2023

Intelligent Automation &Amp; Soft Computing

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Enabling high mobility applications in millimeter wave (mmWave) based systems opens up a slew of new possibilities, including vehicle communications in addition to wireless virtual/augmented reality. The narrow beam usage in addition to the millimeter waves sensitivity might block the coverage along with the reliability of the mobile links. In this research work, the improvement in the quality of experience faced by the user for multimedia-related applications over the millimeter-wave band is investigated. The high attenuation loss in high frequencies is compensated with a massive array structure named Multiple Input and Multiple Output (MIMO) which is utilized in a hyperdense environment called heterogeneous networks (HetNet). The optimization problem which arises while maximizing the Mean Opinion Score (MOS) is analyzed along with the QoE(Quality of Experience) metric by considering the Base Station(BS) powers in addition to the needed Quality of Service (QoS). Most of the approaches related to wireless network communication are not suitable for the millimeter-wave band because of its problems due to high complexity and its dynamic nature. Hence a deep reinforcement learning framework is developed for tackling the same optimization problem. In this work, a Fuzzy-based Deep Convolutional Neural Network (FDCNN) is proposed in addition to a Deep Reinforcing Learning Framework (DRLF) for extracting the features of highly correlated data. The investigational results prove that the proposed method yields the highest satisfaction to the user by increasing the number of antennas in addition with the smallscale antennas at the base stations. The proposed work outperforms in terms of MOS with multiple antennas.

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Secure resource management in beyond 5G heterogeneous networks with decoupled access

Cited by 21 publications

References 23 publications

Deep Learning Based Autonomous Transport System for Secure Vehicle and Cargo Matching

Deep Learning Based Autonomous Transport System for Secure Vehicle and Cargo Matching

Resource allocation trends for ultra dense networks in 5G: a comprehensive survey

Millimeter Wave Massive MIMO Heterogeneous Networks Using Fuzzy-Based Deep Convolutional Neural Network (FDCNN)

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