Improved Channel Allocation Scheme for Cognitive Radio Networks

Latif, Shahzad; Akraam, Suhail; Малик, Аднан; Abbasi, Aaqif Afzaal; Habib, Muhammad; Lim, Sangsoon

doi:10.32604/iasc.2021.014388

Cited by 12 publications

(11 citation statements)

References 22 publications

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“…1a, AP1 and AP2 are connected to the AP controller, which coordinates the channels used by APs and synchronizes the timing for beacon transmissions of APs in the ESS. In addition, since we assume the enterprise network [16,17], the AP controller can have NAP lists of APs. In addition, APs are assumed to be strategically deployed to minimize overlapping communication areas between APs and maximize the overall coverage of extended service sets.…”

Section: Network Architecture Of Man-btsmentioning

confidence: 99%

Reinforcement Learning-Based Handover Scheme with Neighbor Beacon Frame Transmission

Kim¹,

Kim²,

Choi³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Mobility support to change the connection from one access point (AP) to the next (i.e., handover) becomes one of the important issues in IEEE 802.11 wireless local area networks (WLANs). During handover, the channel scanning procedure, which aims to collect neighbor AP (NAP) information on all available channels, accounts for most of the delay time. To reduce the channel scanning procedure, a neighbor beacon frame transmission scheme (N-BTS) was proposed for a seamless handover. N-BTS can provide a seamless handover by removing the channel scanning procedure. However, N-BTS always requires operating overhead even if there are few mobile stations (MSs) for the handover. Therefore, this paper proposes a reinforcement learning-based handover scheme with neighbor beacon frame transmission (MAN-BTS) to properly consider the use of N-BTS. The optimization equation is defined to maximize the expected reward to find the optimal policy and is solved using Q-learning. Simulation results show that the proposed scheme outperforms the comparison schemes in terms of the expected reward.

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Section: Network Architecture Of Man-btsmentioning

confidence: 99%

Reinforcement Learning-Based Handover Scheme with Neighbor Beacon Frame Transmission

Kim¹,

Kim²,

Choi³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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show abstract

“…Inspired by some biological evolution techniques, the authors in Ref. [18] proposed an evolutionary optimisation algorithm called differential evolution‐based particle swarm optimisation with the repair process (DEPSO‐RP) for channel assignment in Cognitive radio networks CRNs. The suggested heuristic optimisation approach applies PSO to maximise the network utilisation and minimise the interference among SUs.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Related studies can be found in the following sources but not restricted to Ref. [7–87]. Each of the proposed techniques has its own advantages and disadvantages, and none of them can be considered on its own to fully accomplish the realisation of CR systems.…”

Section: Introductionmentioning

confidence: 99%

Distributed reinforcement learning for dynamic spectrum allocation in cognitive radio‐based internet of things

Elhachmi

2022

IET Networks

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Cognitive Radio (CR) with other advancements such as the Internet of things and machine learning has recently emerged as the main involved technique to use spectrum in an efficient manner. It can access the spectrum in a fully dynamic way and exploit the unused spectrum resources without creating any harm to cognitive users. In this paper, the authors develop a CR access strategy founded on the implementation of an efficient Deep Multi‐user Reinforcement Learning algorithm based on a combination of a Deep neural network, Q‐learning, and cooperative multi‐agent systems. The proposed approach consists of two stages: the user choice algorithm to set up an agent's activation order, and the frequency choice method to select the optimal channel on the appropriate bandwidth. Reasonable implementation is proposed, and the obtained results demonstrate that the authors’ approach can improve wireless communication for all CR terminals. It shows satisfactory performances in terms of user satisfaction degree and the number of used channels and can keep the channel allocation plan always in the appropriate state.

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“…The doctoral dissertation [17] has presented the spectrum sharing in CRN with security constraints. Particle swarm optimization algorithm is proposed in [18] to assign the channel in CRN. The repair process is used to improve the spectrum usage in CRN and remove the conflicts between secondary user.…”

Section: Related Workmentioning

confidence: 99%

Spectrum Prediction in Cognitive Radio Network Using Machine Learning Techniques

Arivudainambi¹,

Mangairkarasi²,

Kumar³

2022

Intelligent Automation &Amp; Soft Computing

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Cognitive Radio (CR) aims to achieve efficient utilization of scarcely available radio spectrum. Spectrum sensing in CR is a basic process for identifying the existence or absence of primary users. In spectrum sensing, CR users suffer from deep fading effects and it requires additional sensing time to identify the primary user. To overcome these challenges, we frame Spectrum Prediction-Channel Allocation (SP-CA) algorithm which consists of three phases. First, clustering mechanisms to select the spectrum coordinator. Second, Eigenvalue based detection method to expand the sensing accuracy of the secondary user. Third, Bayesian inference approach to reduce the performance degradation of the secondary user. The Eigenvalue based detection method is compared with Energy detection method in terms of varying false alarm rates and samples. The Eigenvalue detection method achieves better performance than Energy detection method. The Simulation results show that our approach gives better performance in terms of reducing sensing time and increasing sensing accuracy.

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Improved Channel Allocation Scheme for Cognitive Radio Networks

Cited by 12 publications

References 22 publications

Reinforcement Learning-Based Handover Scheme with Neighbor Beacon Frame Transmission

Reinforcement Learning-Based Handover Scheme with Neighbor Beacon Frame Transmission

Distributed reinforcement learning for dynamic spectrum allocation in cognitive radio‐based internet of things

Spectrum Prediction in Cognitive Radio Network Using Machine Learning Techniques

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