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image of A Policy Configured Resource Management Scheme for Ahns Using LR-KMA and WD-BMO

Abstract

Introduction

A critical technique that provides quality service for users by solving the conflicts between severe spectrum scarcity and the explosive growth of traffic is Cognitive Radio Ad Hoc Networks (CRAHNs). Nevertheless, a critical challenge is the coexistence of primary and secondary users for reasonable resource allocation to satisfy system performance. Many approaches have been developed to allocate resources efficiently; however, they possess some existing limitations, such as abnormal traffic networks, user collisions, and high data transmission error rates.

Method

So, to overcome such limitations, this paper proposes an efficient policy-configured reinforcement learning-based Ad Hoc Network (AHN) model. The system begins with modeling the Cognitive Radio (CR) network in which the nodes are initialized and clustered using the Link Reliability K-Means clustering Algorithm (LR-KMA) method to derive the optimal policy configuration for the network. Then, to sense the available spectrum and divide it into several bands, spectrum sensing using Coherent Based Detection (CBD) and signal source prediction using the Parzen-Rosenblatt Window-based Restricted Boltzmann Machine (PRW-RBM) were performed.

Result

Next, the suitable bands are selected in the learning model using the Weibull Distribution-based Blue Monkey Optimization (WD-BMO) technique for the resource allocation process. The experimental outcomes were ultimately analyzed to evaluate the proposed resource allocation model's performance in CRAHNs. The LR-KMA algorithm showed 1.5% higher clustering efficiency than traditional methods, while PRW-RBM achieved 1.07% higher classification accuracy.

Conclusion

The optimal resource allocation strategy, WD-BMO, led to lower Normalized Objective Values (NOV) compared to existing methods.

© 2024 Bentham Science Publishers
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/content/journals/rascs/10.2174/0126662558295249240922112820
2024-10-03
2024-11-26

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/content/journals/rascs/10.2174/0126662558295249240922112820
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A Policy Configured Resource Management Scheme for Ahns Using LR-KMA and WD-BMO
Bentham Science Publishers ; https://doi.org/10.2174/0126662558295249240922112820
/content/journals/rascs/10.2174/0126662558295249240922112820
/content/journals/rascs/10.2174/0126662558295249240922112820
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  • Article Type:     Research Article
Keywords: Reinforcement Learning (RL) ; Link Reliability (LR) based clustering ; policy configuration ; Weibull Distribution (WD) based optimal band selection ; Parzen-Rosenblatt Window (PRW) based signal source prediction

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