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 17 publications

(4 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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“…A matched filter detector is better than the equivalent unmatched filter detector when prior knowledge about the PU signal is known. However, PU information is rarely disseminated among SU [4], [5]. No prior knowledge of the PU is required before conducting energy detection in low SNR situations.…”

Section: Literature Reviewmentioning

confidence: 99%

Improvement of energy consumption in MIMO with cognitive radio networks

Dawood

Fadhil

2022

Bulletin EEI

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The employment of cognitive radio (CR) is critical to the successful development of wireless communications. In this field, especially when using the multiple input multiple output (MIMO) antenna technology, energy consumption is critical. If the principal user (PU) is present, developers can utilize the energy detecting approach to tell. The researchers employed two distinct phases to conduct their research: the intense and accurate sensing stages. After the furious sensing step was completed, the PU user was identified as having a maximum or minimal energy channel. There are two situations in which the proposed algorithm's performance is tested: channels for fading AWGN and Rayleigh. When the proposed methods' simulation results are compared with conventional approaches, the complexity of MIMO was reduced by roughly 42% at low SNR levels. With 70 samples and an acceptable drop in detection performance, the results were suitable for further testing.

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“…An intelligent access network selection algorithm was designed in [8], depending on multiple attribute decision-making (MADM), but the throughput was not improved. A new approach was presented in [9] to assign the resources and distribution depending on the cooperative game theory in order to ensure maximized payoff. However, time and complexity have remained higher.…”

Section: Related Workmentioning

confidence: 99%

Multicriteria Oppositional-Learnt Dragonfly Resource-Optimized QoS Driven Channel Selection for CRNs

Devi¹,

Maloj²

2022

JTIT

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Cognitive radio networks (CRNs) allow their users to achieve adequate QoS while communicating. The major concern related to CRN is linked to guaranteeing free channel selection to secondary users (SUs) in order to maintain the network’s throughput. Many techniques have been designed in the literature for channel selection in CRNs, but the throughput of the network has not been enhanced yet. Here, an efficient technique, known as multicriteria oppositional-learnt dragonfly resource optimized QoS-driven channel selection (MOLDRO-QoSDCS) is proposed to select the best available channel with the expected QoS metrics. The MOLDRO-QoSDCS technique is designed to improve energy efficiency and throughput, simultaneously reducing the sensing time. By relying on oppositional-learnt multiobjective dragonfly optimization, the optimal available channel is selected depending on signal-to-noise ratio, power consumption, and spectrum utilization. In the optimization process, the population of the available channels is initialized. Then, using multiple criteria, the fitness function is determined and the available channel with the best resource availability is selected. Using the selected optimal channel, data transmission is effectively performed to increase the network’s throughput and to minimize the sensing time. The simulated outputs obtained with the use of Matlab are compared with conventional algorithms in order to verify the performance of the solution. The MOLDRO-QoSDCS technique performs better than other methods in terms of throughput, sensing time, and energy efficiency.

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

Cited by 17 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

Improvement of energy consumption in MIMO with cognitive radio networks

Multicriteria Oppositional-Learnt Dragonfly Resource-Optimized QoS Driven Channel Selection for CRNs

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