Energy aware resource allocation and complexity reduction approach for cognitive radio networks using game theory

Gudihatti, K N Shyleshchandra; Roopa, .; Tanuja, R; Manjula, S H; Venugopal, K. R.

doi:10.1016/j.phycom.2020.101152

Cited by 14 publications

(2 citation statements)

References 28 publications

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“…KN et al [37] Improves energy efficiency and reduces complexity, leading to cost savings and improved network performance.…”

Section: Authors Year Advantage Limitationmentioning

confidence: 99%

“…This also helps minimize latency and improve the overall performance of edge computing systems. KN, S. G., et al [37] have discussed an energy-aware resource allocation and complexity reduction approach for cognitive radio networks using game theory, which is a cognitive radio network optimization technique that aims to maximize energy efficiency and minimize the complexity of resource allocation by utilizing game theory principles. It involves strategic decision-making for resource allocation among multiple users, considering the trade-off between energy consumption and system performance.…”

Section: Literature Reviewmentioning

confidence: 99%

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Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems

Sangeetha,

Logeshwaran,

Faheem

et al. 2024

The Journal of Engineering

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This paper presents an analysis of the performance of the Energy Aware Scheduling Algorithm (EASA) in a 5G green communication system. 5G green communication systems rely on EASA to manage resource sharing. The aim of the proposed model is to improve the efficiency and energy consumption of resource sharing in 5G green communication systems. The main objective is to address the challenges of achieving optimal resource utilization and minimizing energy consumption in these systems. To achieve this goal, the study proposes a novel energy‐aware scheduling model that takes into consideration the specific characteristics of 5G green communication systems. This model incorporates intelligent techniques for optimizing resource allocation and scheduling decisions, while also considering energy consumption constraints. The methodology used involves a combination of mathematical analysis and simulation studies. The mathematical analysis is used to formulate the optimization problem and design the scheduling model, while the simulations are used to evaluate its performance in various scenarios. The proposed EASM reached a 91.58% false discovery rate, a 64.33% false omission rate, a 90.62% prevalence threshold, and a 91.23% critical success index. The results demonstrate the effectiveness of the proposed model in terms of reducing energy consumption while maintaining a high level of resource utilization.

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“…KN et al [37] Improves energy efficiency and reduces complexity, leading to cost savings and improved network performance.…”

Section: Authors Year Advantage Limitationmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems

Sangeetha,

Logeshwaran,

Faheem

et al. 2024

The Journal of Engineering

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Binary Jaya‐based channel allocation in cognitive radio networks

Saoucha

2022

Concurrency and Computation

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Summary Cognitive radio is a key solution for wireless networks, as it offers the possibility to cope with spectrum scarcity problem. In this work, we are interested in the channel allocation problem (CA) in cognitive radio networks which is a well‐known NP‐hard optimization problem. CA has been resolved using classic evolutionary approaches such as genetic algorithms (GAs) and particle swarm optimization (PSO). However, the performance of these latters depends strongly on the parameters tuning which is a hard time‐consuming task. Under this umbrella, in this article, we propose a new solution based on Jaya algorithm which is a recent free parameters approach. The performance of our proposed algorithm is compared against GA and PSO under different network topologies, and this in terms of max‐sum‐reward, max‐min‐reward, and max‐proportional‐fair. The obtained simulation results confirm the superiority of our proposed approach under all the tested scenarios.

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Congestion centric multi‐objective reptile search algorithm‐based clustering and routing in cognitive radio sensor network

Sunitha¹,

Balmuri

Prado

et al. 2022

Trans Emerging Tel Tech

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In recent trends, Cognitive Radio Sensor Networks (CRSNs) are investigated in‐depth and getting momentum in all types of applications. CRSN can make use of the underutilized frequency spectrum in a suitable manner. Due to the above‐mentioned advantage, the scholars have initiated to study of the domain of cognitive radio routing. Network congestion produces transmission delays and packet loss, as well as time and energy wasted on recovery. In order to fulfill the energy efficiency and network lifetime in CRSN, Congestion Centric Multi‐Objective Reptile Search Algorithm (CC‐MORSA)‐based Clustering and Routing are used. The main objective of proposed CC‐MORSA is to improve the lifetime by minimizing the distance among the designated Cluster Head nodes which creates the fitness function by multiple objectives like energy, distance, and load. This technique is appropriate for common sensor nodes in coordinated communications infrastructure and large networks. The simulation results are analyzed through MATALB in terms of remaining energy (999.5 J), average delay (0.36 s), Packet Delivery Ratio (99.8%), Energy Consumption (24.1 J), Throughput (0.98 Mbps), routing overhead (0.54), and Packet Loss Rate (0.2%). From the outcomes, it shows that the presented CC‐MORSA outperformed conventional Stability‐Aware Cluster‐based Routing and Drop Factor‐Based Energy Efficient Routing technique.

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Energy aware resource allocation and complexity reduction approach for cognitive radio networks using game theory

Cited by 14 publications

References 28 publications

Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems

Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems

Binary Jaya‐based channel allocation in cognitive radio networks

Congestion centric multi‐objective reptile search algorithm‐based clustering and routing in cognitive radio sensor network

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