Optimisation of secrecy rate in cooperative device to device communications underlaying cellular networks

Alibeigi, Maryam; Taherpour, Abbas

doi:10.1049/iet-com.2018.5507

Cited by 10 publications

(7 citation statements)

References 33 publications

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“…However, this method results in a worse response with increasing collaboration and maintenance coefficients. In addition, in our previous work [23][24][25], the use of the multi-antenna system and cooperation between the sensor improved the results.…”

Section: Numerical Results and Discussionmentioning

confidence: 83%

Collaborative data aggregation using multiple antennas sensors and fusion centre with energy harvesting capability in WSN

Choubin¹,

Taherpour²,

Rahmani³

2019

IET Communications

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Section: Numerical Results and Discussionmentioning

confidence: 83%

Collaborative data aggregation using multiple antennas sensors and fusion centre with energy harvesting capability in WSN

Choubin¹,

Taherpour²,

Rahmani³

2019

IET Communications

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“…Our proposed model is better than the model of Nevat et al 13 in the ROC results for the optimal detection schemes with equal number of FC antennas, because of usage EH between the sensors in WSN. In addition, in our previous work, 29,[31][32][33][34][35][36] the use of the multi-antenna system and cooperation between the sensors improved the results.…”

Section: Probability Of Detection Pmentioning

confidence: 80%

Optimization of distributed detection in energy harvesting wireless sensor networks with multiple antenna fusion center

Choubin

Taherpour

2020

Trans Emerging Tel Tech

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This paper addresses the detection improvement using multiple antenna fusion center (FC) and optimal collaboration among sensors with energy harvesting (EH) capability in WSNs. Using multiple antennas at FC, we benefit from the available fundamental spatial diversity in distributed WSN. We formulate to maximize the probability of collaborative detection based on parameters of collaboration among sensors and the energy of each sensor for the different scenarios as the optimization problem. In particular, we consider several practical constraints for EH and battery energy storage to maintain network connectivity. The objective function is the nonconvex function so the optimization problem is obtained as the nonconvex problem. To resolve this issue, the objective function is converted to a convex function using a relaxation method, and an optimum solution is obtained for this problem using local optimum values. The numerical results show the impact of the different parameters on the performance of the collaborative decision. These results also demonstrate that using distributed spatial diversity at FC by multiple antennas leads to a better decision in terms of error probability, and distributed wireless sensor‐enabled with EH capability results to more robust network connectivity in practice.

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“…If each SU can make its local decision according to single threshold λ o with probabilities of detection P d and false alarm P f , the equation is as follows 25…”

Section: System Modelmentioning

confidence: 99%

A new algorithm for considering green communication and excellent sensing performance in cognitive radio networks

Huang

Yin

Cao

2020

International Journal of Distributed Sensor Networks

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Multi-node cooperative sensing can effectively improve the performance of spectrum sensing. Multi-node cooperation will generate a large number of local data, and each node will send its own sensing data to the fusion center. The fusion center will fuse the local sensing results and make a global decision. Therefore, the more nodes, the more data, when the number of nodes is large, the global decision will be delayed. In order to achieve the real-time spectrum sensing, the fusion center needs to quickly fuse the data of each node. In this article, a fast algorithm of big data fusion is proposed to improve the real-time performance of the global decision. The algorithm improves the computing speed by reducing repeated computation. The reinforcement learning mechanism is used to mark the processed data. When the same environment parameter appears, the fusion center can directly call the nodes under the parameter environment, without having to conduct the sensing operation again. This greatly reduces the amount of data processed and improves the data processing efficiency of the fusion center. Experimental results show that the algorithm in this article can reduce the computation time while improving the sensing performance.

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Optimisation of secrecy rate in cooperative device to device communications underlaying cellular networks

Cited by 10 publications

References 33 publications

Collaborative data aggregation using multiple antennas sensors and fusion centre with energy harvesting capability in WSN

Collaborative data aggregation using multiple antennas sensors and fusion centre with energy harvesting capability in WSN

Optimization of distributed detection in energy harvesting wireless sensor networks with multiple antenna fusion center

A new algorithm for considering green communication and excellent sensing performance in cognitive radio networks

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