A novel approach of power allocation for secondary users in cognitive radio networks

Mukherjee, Amrit; Choudhury, Sagarika; Goswami, Pratik; Bayessa, Gezahegn Abdissa; Tyagi, Sumarga Kumar Sah

doi:10.1016/j.compeleceng.2018.03.006

Cited by 17 publications

(7 citation statements)

References 10 publications

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“…During the node location process, the position of all nodes in the static node location is fixed [35]. Due to the instability of the statistical characteristics of the system, Mukherjee et al [36] proposed the ED-AE method to solve the power allocation of cooperative sensing and proved the importance of the problem of active node identification. The positioning algorithm can be divided into two types: centralized and distributed [37].…”

Section: Related Workmentioning

confidence: 99%

Back Propagation Neural Network Based Cluster Head Identification in MIMO Sensor Networks for Intelligent Transportation Systems

et al. 2020

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Wireless Sensor Network (WSN) is an essential technology for the Internet-of-Things (IoT) and intelligence-based applications. In the case of Intelligent Transportation Systems (ITS), the WSNs play an important role in safety and efficient traffic management. Therefore, there is enormous demand for energy efficient WSNs for dynamic resource allocation in vehicles and infrastructures. This work presents a Multi-Input Multi-Output (MIMO) technique model in WSNs, which addresses the Cluster Head (CH) recognition issue for MIMO sensor networks by using Back Propagation Neural Network (BPNN). The conventional CH identification suffers from a lack of location identification due to the dynamic and real-time environment. Thus, to obtain more precise positioning accuracy, the proposed work uses BPNN combined with a distributed gradient drop technique to calculate the position of the unknown CH. This reduces the distance estimation error, and the particle swarm optimization technique is further used to obtain the optimal weight and threshold of the network. The work is validated by using mathematical analysis, simulations, and comparison with existing techniques. The proposed model shows a better performance in terms of energy consumption, error rate, and computation time.

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Section: Related Workmentioning

confidence: 99%

Back Propagation Neural Network Based Cluster Head Identification in MIMO Sensor Networks for Intelligent Transportation Systems

et al. 2020

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“…Here, T denotes the present time instant. Therefore, the autocorrelation function at i and i + 1 time instances can be written as [41][42][43] R a l i a l i + 1 (k)(Γ) = lim Fig. 1 illustrates a hybrid approach to calculate BSS based on the subsequent time-difference spectrum sensing by individual SUs.…”

Section: Source Identification and Performance Modelmentioning

confidence: 99%

“…Since the sources are the dominating frequency components, the autocorrelation between the received signals with lower end Fig. 1 Modified BSS-based autocorrelation setup [43] IET Commun., 2020, Vol. 14 Iss.…”

Section: Source Identification and Performance Modelmentioning

confidence: 99%

“…The PSO algorithm can converge to the global optimal solution with a large probability, and has faster calculation speed and better global search ability [42][43][44][45]. Suppose that in a D-dimensional target search space, there are N particles forming a community, where the ith particle is represented as a D-dimensional vector:…”

Section: Energy Efficiency Analysis Based On Apsomentioning

confidence: 99%

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Adaptive Particle Swarm Optimisation based Energy Efficient Dynamic Correlation Behavior of Secondary Nodes in Cognitive Radio Sensor Networks

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“…(iii) current cluster and nearby nodes which are similar are updated, (iv) until the last node the formation of cluster process is repeated. [3]…”

Section: Introductionmentioning

confidence: 99%

Cooperative Spectrum Sensing Based on HML and Vector Quantization for Cognitive Radio Networks

Alpna¹,

Mukherjee²,

Datta³

2018

IJET

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The proposed work illustrates a novel technique for cooperative spectrum sensing in a cognitive radio (CR) network. The work includes an approach of identifying secondary users (SUs) based on Hierarchical Maximum Likelihood (HML) technique followed by Vector Quantization. Initially, the arrangement of the SUs are been observed using HML with respect to a spatial domain and then the active SUs among them are identified using VQ. The approach will not only save the energy, but the decision of the real-time and dynamic cooperative communication network becomes more accurate as we can predict the behavior of SUs movement and spectrum sensing by each individual SU at that particular place. The results and simulations of the real-time experiment justifies with the proposed approach.

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A novel approach of power allocation for secondary users in cognitive radio networks

Cited by 17 publications

References 10 publications

Back Propagation Neural Network Based Cluster Head Identification in MIMO Sensor Networks for Intelligent Transportation Systems

Back Propagation Neural Network Based Cluster Head Identification in MIMO Sensor Networks for Intelligent Transportation Systems

Adaptive Particle Swarm Optimisation based Energy Efficient Dynamic Correlation Behavior of Secondary Nodes in Cognitive Radio Sensor Networks

Cooperative Spectrum Sensing Based on HML and Vector Quantization for Cognitive Radio Networks

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