A policy configured resource management scheme for AHNS using link reliability K‐means clustering algorithm and Weibull distribution‐based blue monkey optimization

• Published in: International journal of communication systems 2024-08, Vol.37 (12), p.n/a
• Main Authors: Punitha, P., Sivaparthipan, C. B., Muthu, Bala Anand, Lakshmana Kumar, R.
• Format: Article
• Language: English
• Subjects: Accuracy; Ad hoc networks; Algorithms; Clustering; Cognitive radio; collision‐aware resource management; Data transmission; deep neural network (DNN); LR‐KMA method; Machine learning; Monkeys; Optimization; policy‐based CRAHN cluster network; PRW‐RBM method; reinforcement learning (RL); Reliability; Resource allocation; Resource management; Resource utilization; Transmission error; WD‐BMO method; Weibull distribution
• ISSN: 1074-5351; 1099-1131
• DOI: 10.1002/dac.5850

Summary Cognitive Radio Ad Hoc Networks (CRAHNs) are an essential method for resolving conflicts between extreme spectrum scarcity and rapid traffic increase while maintaining high‐quality service for consumers. However, the coexistence of primary and secondary users represents a critical challenge for reasonable resource allocation in order to provide a sustaining system performance. Many approaches have been developed to efficiently allocate resources; however, these methods are currently limited by things like user collision, strange traffic networks, and high data transmission error rates. To address these constraints, this paper proposes a policy‐configured reinforcement learning‐based ad hoc network (AHN) model. To obtain the ideal policy configuration for the network, the system first models the cognitive radio (CR) network, in which nodes are initialised and grouped employing the Link Reliability K‐Means clustering Algorithm (LR‐KMA). The available spectrum was then detected and separated into multiple bands utilizing coherent‐based detection (CBD) and signal source identification employing the Parzen–Rosenblatt Window‐based Restricted Boltzmann Machine (PRW‐RBM). Next, the learning model for the resource allocation process employs the Weibull Distribution‐based Blue Monkey Optimization (WD‐BMO) approach to pick the relevant bands. Finally, the experimental results were analyzed in order to evaluate the proposed resource allocation model's performance in CRAHNs. When compared with previous findings, the proposed method improves resource utilization by 5%, the proposed model achieves a 7% higher throughput, and the PRW‐RBM's accuracy improves classification accuracy by 1.07%. CRAHNs are crucial for managing spectrum scarcity and traffic increase, but coexistence of primary and secondary users poses challenges. A policy‐configured reinforcement learning‐based Ad Hoc Network model is used in this paper to address these constraints. The proposed model initializes and groups nodes using the Link Reliability K‐Means clustering Algorithm (LR‐KMA). Further, the spectrum is detected and separated using Coherent Based Detection (CBD) and Parzen‐Rosenblatt Window‐based Restricted Boltzmann Machine (PRW‐RBM). Additionally, the learning model for resource allocation uses the Weibull Distribution‐based Blue Monkey Optimisation (WD‐BMO) approach.