D2D Communication Using Distributive Deep Learning with Coot Bird Optimization Algorithm

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Nethravathi H. M. 1,* Akhila S. 2 Vinayakumar Ravi 3

1. B.M.S. College of Engineering, VTU, Bengaluru, Karnataka 560019, India

2. Department of Electronics and Communication Engineering, B.M.S. College of Engineering, VTU, Bengaluru, Karnataka 560019, India

3. Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia

* Corresponding author.

DOI: https://doi.org/10.5815/ijcnis.2023.05.01

Received: 14 Feb. 2023 / Revised: 24 Apr. 2023 / Accepted: 11 Jun. 2023 / Published: 8 Oct. 2023

Index Terms

D2D, Distributive Deep Learning, Coot Bird Optimization


D2D (Device-to-device) communication has a major role in communication technology with resource and power allocation being a major attribute of the network. The existing method for D2D communication has several problems like slow convergence, low accuracy, etc. To overcome these, a D2D communication using distributed deep learning with a coot bird optimization algorithm has been proposed. In this work, D2D communication is combined with the Coot Bird Optimization algorithm to enhance the performance of distributed deep learning. Reducing the interference of eNB with the use of deep learning can achieve near-optimal throughput. Distributed deep learning trains the devices as a group and it works independently to reduce the training time of the devices. This model confirms the independent resource allocation with optimized power value and the least Bit Error Rate for D2D communication while sustaining the quality of services. The model is finally trained and tested successfully and is found to work for power allocation with an accuracy of 99.34%, giving the best fitness of 80%, the worst fitness value of 46%, mean value of 6.76 and 0.55 STD value showing better performance compared to the existing works.

Cite This Paper

Nethravathi H. M., Akhila S., Vinayakumar Ravi, "D2D Communication Using Distributive Deep Learning with Coot Bird Optimization Algorithm", International Journal of Computer Network and Information Security(IJCNIS), Vol.15, No.5, pp.1-12, 2023. DOI:10.5815/ijcnis.2023.05.01


[1]P. Gandotra, R. K. Jha, and S. Jain, “A Survey on Device-to-Device (D2D) Communication: Architecture and Security Issues,” Journal of Network and Computer Applications, vol. 78, pp. 9-29, 2016. doi:10.1016/j.jnca.2016.11.002.
[2]J. Kim, J. Park, J. Noh, and S. Cho, “Completely distributed power allocation using deep neural network for device-to-device communication underlaying LTE,” arXiv preprint arXiv:1802.02736v1, 2018.
[3]J. Shi, Q. Zhang, Y. C. Liang, and X. Yuan, “Distributed Deep Learning Power Allocation for D2D Network Based on Outdated Information,” 2020 IEEE Wireless Communications and Networking Conference (WCNC), Seoul, Korea (South), pp. 1–6, 2020. doi:10.1109/wcnc45663.2020.9120717.
[4]J. Kim, J. Park, J. Noh, and S. Cho, “Autonomous power allocation based on distributed deep learning for device-to-device communication underlaying cellular network,” IEEE Access, vol. 8, pp. 107853-107864, 2020. doi: 10.1109/ACCESS.2020.3000350.
[5]J. Huang, C. Xing, and M. Guizani, “Power Allocation for D2D Communications with SWIPT,” IEEE Transactions on Wireless Communications, vol. 19, no. 4, p. 2308 – 2320, 2020. doi:10.1109/TWC.2019.2963833.
[6]Y. Jiang, Q. Liu, F. Zheng, X. Gao, and X. You, “Energy Efficient Joint Resource Allocation and Power Control for D2D Communications,” IEEE Transactions on Vehicular Technology, vol. 65, no. 8, pp. 6119-6127, 2015. doi:10.1109/TVT.2015.2472995.
[7]I. Naruei, and F.Keynia, “A new optimization method based on COOT bird natural life model,” Expert Systems with Applications, vol. 183, 2021. doi:10.1016/j.eswa.2021.115352.
[8]A. Abrardo, and M. Moretti, “Distributed Power Allocation for D2D Communications Underlaying/Overlaying OFDMA Cellular Networks,” IEEE Transactions on Wireless Communications, vol. 16, no. 3, pp. 1466–1479, 2017. doi:10.1109/TWC.2016.2646360.
[9]Q. V. Pham, S. Mirjalili, N. Kumar, M. Alazab, and W. J. Hwang, “Whale Optimization Algorithm With Applications to Resource Allocation in Wireless Networks,” IEEE Transactions on Vehicular Technology, vol. 69, no. 4, pp. 4285–4297, 2020. doi:10.1109/TVT.2020.2973294.
[10]M. Hamdi, and M. Zaied, “Resource allocation based on hybrid genetic algorithm and particle swarm optimization for D2D multicast communications,” Applied Soft Computing, vol. 83, 105605, 2019. doi:10.1016/j.asoc.2019.105605.
[11]K. Pandey, and A. Rajeev, “Lyapunov optimization machine learning resource allocation approach for uplink underlaid D2D communication in 5G networks,” IET Communications, vol. 16, no. 5, pp. 476-484, 2022. doi: 10.1049/cmu2.12264.
[12]Y. Zhi, J. Tian, X. Deng, J. Qiao, and D. Lu, “Deep reinforcement learning-based resource allocation for D2D communications in heterogeneous cellular networks,” Digital Communications and Networks, vol. 8, no. 5, pp. 834-842, 2021. doi: 10.1016/j.dcan.2021.09.013.
[13]D. Wang, H. Qin, B. Song, K. Xu, X. Du, M. Guizani, “Joint resource allocation and power control for D2D communication with deep reinforcement learning in MCC,” Physical Communication, vol. 45, 101262, 2021. doi:10.1016/j.phycom.2020.101262.
[14]W. Lee, M. Kim, D. H. Cho, “Deep Learning Based Transmit Power Control in Underlaid Device-to-Device Communication,” IEEE Systems Journal, vol. 13, no.3, pp. 2551-2554, 2018. doi:10.1109/JSYST.2018.2870483.
[15]S. Han, L. Ye, and W. Meng, Artificial Intelligence for Communications and Networks, Springer, 2019.
[16]H. ElSawy, E. Hossain, and M. S. Alouini, “Analytical Modeling of Mode Selection and Power Control for Underlay D2D Communication in Cellular Networks,” IEEE Transactions on Communications, vol. 62, no. 11, pp. 4147–4161, 2014. doi:10.1109/tcomm.2014.2363849.
[17]J. Lee, and J. H. Lee, “Performance Analysis and Resource Allocation for Cooperative D2D Communication in Cellular Networks With Multiple D2D Pairs,” IEEE Communications Letters, vol. 23, no. 5, pp. 909-912, 2019. doi:10.1109/LCOMM.2019.2907252.
[18]T. D. Hoang, L. B. Le, and T. Le-Ngoc, “Resource Allocation for D2D Communication Underlaid Cellular Networks Using Graph-based Approach,” IEEE Transactions on Wireless Communications, vol. 15, no. 10, pp. 7099-7113, 2016. doi:10.1109/TWC.2016.2597283.
[19]P. Cheng, L. Deng, H. Yu, Y. Xu, and H. Wang, “Resource allocation for cognitive networks with D2D communication: An evolutionary approach,” 2012 IEEE Wireless Communications and Networking Conference (WCNC), Paris, France, 2671–2676, 2012. doi:10.1109/WCNC.2012.6214252.
[20]H. Tang, and Z. Ding, “Mixed Mode Transmission and Resource Allocation for D2D Communication,” IEEE Transactions on Wireless Communications, vol. 15, no. 1, pp. 162-175, 2015. doi:10.1109/TWC.2015.2468725.
[21]S. Alemaishat, O. A. Saraereh, I. Khan, and B. J. Choi, “An Efficient Resource Allocation Algorithm for D2D Communications Based on NOMA,” IEEE Access, vol. 7, 120238 – 120247, 2019. doi:10.1109/ACCESS.2019.2937401.
[22]J. Zhang, Z. Xie, J. Gao, Y. Wang, and H. Xu, “Energy-efficient resource allocation for MIMO-NOMA systems with user fairness constraint and imperfect channel state information,” IEEE Wireless Communications Letters, vol. 10, no. 2, pp. 267-271, 2021.
[23]C. Liu, S. Zhou, H. Zhang, and Z. Niu, “Power Allocation for Device-to-Device Communications with Quality-of-Service Constraints,” IEEE Transactions on Vehicular Technology, vol. 67, no. 7, pp. 6098-6108, 2018.