AI-Based Secure NOMA and Cognitive Radio-Enabled Green Communications: Channel State Information and Battery Value Uncertainties

Sheikhzadeh, Saeed; Pourghasemian, Mohsen; Javan, Mohammad Reza; Mokari, Nader; Jorswieck, Eduard A.

doi:10.1109/tgcn.2021.3135479

Cited by 10 publications

(6 citation statements)

References 47 publications

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“…The utility of SUs 𝑢 that selects channel 𝑗 using shared channel access mechanism is established as shown in (15).…”

Section: Non-shared Channel Access Mechanismmentioning

confidence: 99%

“…Recently, number of works have been designed for maximizing throughput (i.e., spectrum usage) of SUs with minimal interference (i.e., less collision) to primary network [10]- [15] under small to large density wireless sensor networks. The existing methods predominantly focused on allocating optimal channel access in CR-WSNs for maximizing the objective function with certain quality of service (QoS) constraint [10] using known channel availability statistics [11], [12].…”

Section: Introductionmentioning

confidence: 99%

“…However, when sensor nodes mobility is dynamic in nature all these methods provide poor performance [14]. Further, it is important to study the channel access model under imperfect channel sensing considering dynamic mobile nature of LS-WSNs [15]. How to bring a balance between maximizing throughput with minimal collision and assures access fairness is the key to design efficient spectrum access model.…”

Section: Introductionmentioning

confidence: 99%

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Throughput maximization with channel access fairness model using game theory approach

Tauseef,

Fatima,

Khanam

2023

IJEECS

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The <span>adoption of cognitive radio (CR) technology into wireless sensor networks (WSNs) effectively addresses the spectrum scarcity problem of traditional unlicensed spectrum. Allocating and managing limited network channel to secondary user (SUs) considering dynamic behavior pattern of primary users (PUs) is a critical issue of CR-WSN. Recently, various channel access methodologies using statistical, reinforcement learning (RL), game theory (GT), and deep learning (DL) model have been presented for CR-WSN. However, the existing channel access methodologies has following two limitations: i) fails to assure balance between maximizing throughput of SUs with minimal interference to PUs considering multi-channel CR-WSNs environment; and ii) maximizing throughput with minimal collision assuring access fairness among SUs considering energy constraint CR-WSNs. In addressing the research issues, this paper present throughput maximization channel access fairness using game theory (TMCAF-GT) model. The TMCAF employ both shared and non-shared channel access mechanism employing GT model for assuring throughput maximization with minimal interference and access fairness. Experiment outcome shows the TMCAF-GT provides superior throughput with minimal collision.</span>

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“…The utility of SUs 𝑢 that selects channel 𝑗 using shared channel access mechanism is established as shown in (15).…”

Section: Non-shared Channel Access Mechanismmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Throughput maximization with channel access fairness model using game theory approach

Tauseef,

Fatima,

Khanam

2023

IJEECS

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“…The current cloud access network faces the following bottlenecks. On one hand, a forward link with a high transmission rate and high reliability is required between the BBU pool and the RRH [8,9]. The traditional forward link uses a wired connection, such as optical fiber or cable.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Reflecting Surface Aided Cloud Access Networks with Federated Learning

Fan¹,

Ou²,

et al. 2023

EAI Endorsed Trans Mob Com Appl

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Compared with traditional cellular network architecture, cloud access network has some significant advantages in spectrum utilization, energy consumption and network construction cost. However, a high-quality forward link is required between the baseband processing unit pool and remote radio head (RRH) in the cloud access network, which results in limited RRH deployment and affects user access link transmission and coverage. To solve this issue, this paper introduces the intelligent reflector technology into the cloud access network as a solution with low energy consumption, low cost and easy deployment to deal with the existing bottlenecks. Firstly, an efficient channel information acquisition strategy based on federated learning is designed for the smart reflector to enhance the user access link, so as to achieve a compromise between the channel estimation accuracy and cost. On this basis, a robust beamforming design and optimization method of the compression mechanism of the forward link are proposed for the smart reflector to enhance the user access link and the wireless forward link, so as to improve the system transmission performance. Finally, we explore the joint resource allocation method of intelligent reflector assisted cloud access network, and improve the system energy efficiency through the collaborative configuration of intelligent reflector and cloud access network communication resources. The research of this paper will provide an important theoretical basis for the application of intelligent reflectors in cloud access networks, especially for the federated learning.

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“…With the increasing scarcity of frequency and harmonic resources, the exploitation of airspace resources has become an effective way to achieve and enhance covert communications [11,12]. Singleantenna transceivers have extremely limited airspace freedom, which makes it difficult to reconcile the conflict between the system's frequency-harmonic efficiency and its stealthiness [13,14].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Reflecting Surface Aided Secure Communication with Federated Learning

Lai

Tang

et al. 2023

EAI Endorsed Trans Mob Com Appl

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Applying federated learning into the covert communication can not only ensure the communication reliability, but also reduce the probability of enemy detection. The use of airspace resources is an effective way to achieve covert communication. However, most of the existing works on the airspace covert communication represented by MIMO need to adapt to the channel state and cannot improve the channel, resulting in the performance bottleneck of covert communication. The intelligent reflective surface (IRS) provides a new perspective for the covert communication by flexibly adjusting the reflection phase shift of the incident signal and intelligently configuring the wireless channel. However, the potential of IRS in the covert communication is far from being fully exploited. To solve this issue, this paper firstly performs a comprehensive literature review on the secure communication with the aid of federated learning, and then gives some challenges on the secure communication in poor channel state. In further, this paper provides some solutions to the challenges on the secure communication, where some results are provided to show the advantages. The research results have important theoretical and practical significance for forming a new research paradigm of airspace intelligent and controllable covert communication and promoting the application and popularization of covert communication in various fields of security.

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AI-Based Secure NOMA and Cognitive Radio-Enabled Green Communications: Channel State Information and Battery Value Uncertainties

Cited by 10 publications

References 47 publications

Throughput maximization with channel access fairness model using game theory approach

Throughput maximization with channel access fairness model using game theory approach

Intelligent Reflecting Surface Aided Cloud Access Networks with Federated Learning

Intelligent Reflecting Surface Aided Secure Communication with Federated Learning

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