Prime Number Theory based Multi-Radio Rendezvous for Cognitive Radio Communication

Islam, Md. Tahidul; Kandeepan, Sithamparanathan; Evans, Robin J.

doi:10.1109/icicsp48821.2019.8958585

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…Non-assisted rendezvous, on the other hand, is one that operates without the assistance of a central controller (or another node) and does not have a CCC. There are many approaches for non-assisted rendezvous techniques presented in the conventional references [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. The general terminologies used for our articles related to rendezvous are summarized as follows: Channel Symmetricity : For the symmetric channel, the cognitive radio transmitter (CR-Tx) and cognitive radio receiver (CR-Rx) have the same available channels.…”

Section: Introductionmentioning

confidence: 99%

“…Prime number theory-based rendezvous has also achieved excellent results for blind rendezvous under non-deterministic arrangements. Some mentionable recent activity on unassisted as well as heterogeneous rendezvous with a single radio includes [ 16 , 17 , 18 , 19 , 20 , 21 ]. With the modular clock-based prime number theory [ 16 ], Nick et al proposed supported rendezvous for CR.…”

Section: Introductionmentioning

confidence: 99%

“…However, because of unpredictable random sequences, the utilization of the prime number theory is limited. The articles [ 17 , 18 , 19 ] carried out a statistical distribution analysis for efficient and guaranteed rendezvous. In a heterogeneous scenario, the rendezvous algorithm with the Chinese remainder theorem for the multi-radio method was proposed by [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Radio Based Rendezvous Technique for Heterogeneous Cognitive Radio Sensor Network

Islam

Kandeepan

Evans

2021

Sensors

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In a distributed cognitive radio (CR) sensor network, transmission and reception on vacant channels require cognitive radio nodes to achieve rendezvous. Because of the lack of adequate assistance from the network environment, such as the central controller and other nodes, assisted rendezvous for distributed CR is inefficient in a dynamic network. As a result, non-assisted blind rendezvous, which is unaware of its counterpart node, has recently led to a lot of interest in the research arena. In this paper, we study a channel rendezvous method based on prime number theory and propose a new multi-radio-based technique for non-assisted rendezvous with the blind and heterogeneous condition. The required time and the optimal number of radios for the guaranteed rendezvous are calculated using probability-based measurement. Analytical expressions for probabilistic guaranteed rendezvous conditions are derived and verified by Monte Carlo simulation. In addition, the maximum time to rendezvous (MTTR) is derived in closed form using statistical and probabilistic analysis. Under different channel conditions, our proposed solution leads to a substantial time reduction for guaranteed rendezvous. For the sake of over-performance of our proposed system, the simulation outcome is compared to a recently proposed heterogeneous and blind rendezvous method. The Matlab simulation results show that our proposed system’s MTTR gains range from 11% to over 95% for various parametric values of the system model.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Radio Based Rendezvous Technique for Heterogeneous Cognitive Radio Sensor Network

Islam

Kandeepan

Evans

2021

Sensors

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Efficient Rendezvous Technique with Channel Bonding for Cognitive Radio Network

Islam,

Kandeepan

2023

2023 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)

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Channel-Hopping Using Reinforcement Learning for Rendezvous in Asymmetric Cognitive Radio Networks

Jin,

Jang,

Jang

et al. 2024

Applied Sciences

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This paper addresses the rendezvous problem in asymmetric cognitive radio networks (CRNs) by proposing a novel reinforcement learning (RL)-based channel-hopping algorithm. Traditional methods like the jump-stay (JS) algorithm, while effective, often struggle with high time-to-rendezvous (TTR) in asymmetric scenarios where secondary users (SUs) have varying channel availability. Our proposed RL-based algorithm leverages the actor-critic policy gradient method to learn optimal channel selection strategies by dynamically adapting to the environment and minimizing TTR. Extensive simulations demonstrate that the RL-based algorithm significantly reduces the expected TTR (ETTR) compared to the JS algorithm, particularly in asymmetric scenarios where M-sequence-based approaches are less effective. This suggests that RL-based approaches not only offer robustness in asymmetric environments but also provide a promising alternative in more predictable settings.

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Prime Number Theory based Multi-Radio Rendezvous for Cognitive Radio Communication

Cited by 3 publications

References 22 publications

Multi-Radio Based Rendezvous Technique for Heterogeneous Cognitive Radio Sensor Network

Multi-Radio Based Rendezvous Technique for Heterogeneous Cognitive Radio Sensor Network

Efficient Rendezvous Technique with Channel Bonding for Cognitive Radio Network

Channel-Hopping Using Reinforcement Learning for Rendezvous in Asymmetric Cognitive Radio Networks

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