Reinforcement learning based secondary user transmissions in cognitive radio networks

Arunthavanathan, Senthuran; Kandeepan, Sithamparanathan; Evans, R.J.

doi:10.1109/glocomw.2013.6825016

Cited by 12 publications

(6 citation statements)

References 24 publications

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“…Arunthavanathan et al [17] uses the RL method to assist in selecting the channel for route finding algorithm of a multi-hop network. The channel selection is based on the agent that has got the highest reward points.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Priority-based reserved spectrum allocation by multi-agent through reinforcement learning in cognitive radio network

Jaishanthi¹,

Ganesh

Sheela³

2019

Automatika

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Research in cognitive radio networks aims at maximized spectrum utilization by giving access to increased users with the help of dynamic spectrum allocation policy. The unknown and rapid dynamic nature of the radio environment makes the decision making and optimized resource allocation to be a challenging one. In order to support dynamic spectrum allocation, intelligence is needed to be incorporated in the cognitive system to study the environment parameters, internal state, and operating behaviour of the radio and based on which decisions need to be made for the allocation of under-utilized spectrum. A novel priority-based reserved allocation method with a multi-agent system is proposed for spectrum allocation. The multi-agent system performs the task of gathering environmental artefacts used for decision making to give the best of effort service in this adaptive communication.

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Section: Reinforcement Learningmentioning

confidence: 99%

Priority-based reserved spectrum allocation by multi-agent through reinforcement learning in cognitive radio network

Jaishanthi¹,

Ganesh

Sheela³

2019

Automatika

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“…However, the aforementioned methods are mostly dependent on resource optimisation, and the cost-efficiency of the parameters listed is still insignificant. The CR prototype, on the other hand, holds the promise of efficient dynamic exploitation of the underutilized spectrum [ 5 ]. In a cognitive radio sensor network (CRSN), cognitive functionality is integrated with sensor nodes to form a new sensor networking paradigm.…”

Section: Introductionmentioning

confidence: 99%

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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“…In single user case, reinforcement learning is suggested in some literature sources to model the spectrum occupancy [50,108]. However, the study in [108] questions reinforcement learning as useful tool to improve spectrum occupancy modelling of their own spectrum campaign measurements.…”

Section: Cooperation and Contentionmentioning

confidence: 99%

“…Performance comparison metrics such as secondary user's throughput, spectrum interference and wastage, and probability of error (or mean square error) are generally defined based on the probability density of the one step-ahead prediction, as well as, the prediction loss function. For example, the probability of incorrect prediction of an available spectrum hole generally describe spectrum interference or spectrum wastage [50].…”

Section: Prediction Model Selectionmentioning

confidence: 99%

Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

Eltom

Kandeepan

Evans

et al. 2018

J Wireless Com Network

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Spectrum scarcity due to inefficient utilisation has ignited a plethora of dynamic spectrum access solutions to accommodate the expanding demand for future wireless networks. Dynamic spectrum access systems allow secondary users to utilise spectrum bands owned by primary users if the resulting interference is kept below a pre-designated threshold. Primary and secondary user spectrum occupancy patterns determine if minimum interference and seamless communications can be guaranteed. Thus, spectrum occupancy prediction is a key component of an optimised dynamic spectrum access system. Spectrum occupancy prediction recently received significant attention in the wireless communications literature. Nevertheless, a single consolidated literature source on statistical spectrum occupancy prediction is not yet available in the open literature. Our main contribution in this paper is to provide a statistical prediction classification framework to categorise and assess current spectrum occupancy models. An overview of statistical sequential prediction is presented first. This statistical background is used to analyse current techniques for spectrum occupancy prediction. This review also extends spectrum occupancy prediction to include cooperative prediction. Finally, theoretical and implementation challenges are discussed.

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Reinforcement learning based secondary user transmissions in cognitive radio networks

Cited by 12 publications

References 24 publications

Priority-based reserved spectrum allocation by multi-agent through reinforcement learning in cognitive radio network

Priority-based reserved spectrum allocation by multi-agent through reinforcement learning in cognitive radio network

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

Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

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