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A Group Teaching Optimization Algorithm for Priority-Based Resource Allocation in Wireless Networks

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Abstract

In wireless networks, resource allocation (RA) is considered as a very important and it is used to enhance the wireless and communication networks performances. D2D (Device-to-device) communication based RA is expressed as a MINLP (mixed integer nonlinear programming) issue. Existing strategies based on RA cannot offer integrative consideration to spectrum efficiency, QoS (Quality of Service) for networks with mixed traffic. When multiple users with various priorities utilize the similar network resource, then it is necessary to utilize the resource on priority basis. In this work, priority based RA is introduced using the GTOA (group teaching optimization algorithm) by combining spectrum resources from unlicensed and licensed spectrum bands to the D2D users in the mixed traffic scenarios. The GTOA optimization framework maximizes all the D2D pair’s utility functions by reducing the whole energy utilization. The network contains the two types of users such as QoS services and BE (best effort) services. Along with several traffics, the function of utility is described for users. The priorities are assigned to the QoS services by the developed GTOA based RA mechanism for performing the resource allotment. As related to other current approaches, the GTOA procedure devours lessor complexity. For the real-time traffic, the developed GTOA can spontaneously assurance the QoS constraint and also owing to the utility function the developed GTOA system create the balance among fairness and throughput for BE traffic users. As a result, by executing RA, the resources and a user can be deliberated. The priority based RA is performed using MATLAB. Simulation outcomes show that the developed GTOA is most appropriate for the priority based RA to the D2D users in the mixed traffic scenarios.

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References

  1. Gao, X., Yang, K., Yang, N., & Wu, J. (2019). Energy-efficient resource block assignment and power control for underlay device-to-device communications in multi-cell networks. Computer Networks, 149, 240–251.

    Article  Google Scholar 

  2. Xu, Y., Jiang, S., & Wu, J. (2018). Towards energy efficient device-to-device content dissemination in cellular networks. IEEE Access, 6, 25816–25828.

    Article  Google Scholar 

  3. Phunchongharn, P., Hossain, E., & Kim, D. I. (2013). Resource allocation for device-to-device communications underlaying LTE-advanced networks. IEEE Wireless Communications, 20(4), 91–100.

    Article  Google Scholar 

  4. Ribeiro, A. (2012). Optimal resource allocation in wireless communication and networking. EURASIP Journal on Wireless Communications and Networking, 2012(1), 272.

    Article  Google Scholar 

  5. Yang, K., Martin, S., Yahiya, T.A and Wu, J. (2014). Energy-efficient resource allocation for downlink in LTE heterogeneous networks. In: 2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall), 1–5.

  6. Yang, K., Martin, S., Xing, C., Wu, J., & Fan, R. (2016). Energy-efficient power control for device-to-device communications. IEEE Journal on Selected Areas in Communications, 34(12), 3208–3220.

    Article  Google Scholar 

  7. Huang, S., Liang, B., & Li, J. (2017). Distributed interference and delay aware design for D2D communication in large wireless networks with adaptive interference estimation. IEEE Transactions on Wireless Communications, 16(6), 3924–3939.

    Article  Google Scholar 

  8. Cheng, W., Zhang, X., & Zhang, H. (2015). Optimal power allocation with statistical QoS provisioning for D2D and cellular communications overunderlaying wireless networks. IEEE Journal on Selected Areas in Communications, 34(1), 151–162.

    Article  Google Scholar 

  9. Yang, K., Wu, J., Gao, X., Bu, X and Guo, S. (2016, September). Energy-efficient power control for device-to-device communications with max-min fairness. In: 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall) 1–5.

  10. Wang, F., Xu, C., Song, L., & Han, Z. (2014). Energy-efficient resource allocation for device-to-device underlay communication. IEEE Transactions on Wireless Communications, 14(4), 2082–2092.

    Article  Google Scholar 

  11. Zappone, A., Björnson, E., Sanguinetti, L and Jorswieck, E. (2016). A framework for globally optimal energy-efficient resource allocation in wireless networks. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3616–3620.

  12. Moodi, Y., Mousavi, S. R., Ghavidel, A., Sohrabi, M. R., & Rashki, M. (2018). Using response surface methodology and providing a modified model using whale algorithm for estimating the compressive strength of columns confined with FRP sheets. Construction and Building Materials, 183, 163–170.

    Article  Google Scholar 

  13. Sahu, P. R., Hota, P. K., & Panda, S. (2018). Power system stability enhancement by fractional order multi input SSSC based controller employing whale optimization algorithm. Journal of Electrical Systems and Information Technology, 5(3), 326–336.

    Article  Google Scholar 

  14. Khalilpourazari, S., Pasandideh, S. H. R., & Ghodratnama, A. (2019). Robust possibilistic programming for multi-item EOQ model with defective supply batches: Whale optimization and water cycle algorithms. Neural Computing and Applications, 31(10), 6587–6614.

    Article  Google Scholar 

  15. Algabalawy, M. A., Abdelaziz, A. Y., Mekhamer, S. F., & Aleem, S. H. A. (2018). Considerations on optimal design of hybrid power generation systems using whale and sine cosine optimization algorithms. Journal of Electrical Systems and Information Technology, 5(3), 312–325.

    Article  Google Scholar 

  16. Pham, Q.-V., Mirjalili, S., Kumar, N., Alazab, M., & Hwang, W.-J. (2020). Whale optimization algorithm with applications to resource allocation in wireless networks. IEEE Transactions on Vehicular Technology, 69(4), 4285–4297.

    Article  Google Scholar 

  17. Sun, M., Xu, X., Tao, X., Zhang, P., & Leung, V. C. M. (2020). NOMA-based D2D-enabled traffic offloading for 5G and beyond networks employing licensed and unlicensed access. IEEE Transactions on Wireless Communications., 19(6), 4109–4124.

    Article  Google Scholar 

  18. Shen, Y., Shi, Y., Zhang, J and Letaief, K.B. (2019). Transfer learning for mixed-integer resource allocation problems in wireless networks. In: ICC 2019–2019 IEEE International Conference on Communications (ICC), 1–6.

  19. Youssef, M.-J., Farah, J., Nour, C. A., & Douillard, C. (2019). Resource allocation in NOMA systems for centralized and distributed antennas with Mixed Traffic using matching theory. IEEE Transactions on Communications, 68(1), 414–428.

    Article  Google Scholar 

  20. Pham, Q.-V., Le, L. B., Chung, S.-H., & Hwang, W.-J. (2019). Mobile edge computing with wireless backhaul: Joint task offloading and resource allocation. IEEE Access, 7, 16444–16459.

    Article  Google Scholar 

  21. Swain, S. N., & Murthy, C. S. R. (2020). A novel energy-aware utility maximization for efficient device-to-device communication in LTE-WiFi networks under mixed traffic scenarios. Computer Networks, 167, 106995.

    Article  Google Scholar 

  22. Zhang, Y., & Jin, Z. (2020). Group teaching optimization algorithm: A novel metaheuristic method for solving global optimization problems. Expert Systems with Applications, 148, 113246.

    Article  Google Scholar 

  23. Sun, Z. Y., & Yang, D. N. (2019). A D2D wireless resource allocation scheme based on overall fairness. 3D Research, 10(2), 1–10. https://doi.org/10.1007/s13319-019-0230-2

    Article  Google Scholar 

  24. Kumaresh, P. S., & Ramprasad, A. V. (2019). Optimal resource allocation algorithm for OFDMA-based WiMAX network using stochastic fish swarm optimization. Soft Computing, 23(8), 2509–2523.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Excel Engineering College, Namakkal, Tamilnadu, India

    S. Sreethar

  2. Department of ECE, Excel Engineering College, Namakkal, India

    N. Nandhagopal & S. Anbu Karuppusamy

  3. Department of ECE, Kongunadu College of Engineering and Technology, Thottiam, Tamilnadu, 621215, India

    M. Dharmalingam

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  1. S. Sreethar
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  2. N. Nandhagopal
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  3. S. Anbu Karuppusamy
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  4. M. Dharmalingam
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Correspondence to S. Sreethar.

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S. Sreethar, Dr. N. Nandhagopal, Dr. S. Anbu Karuppusamy & Dr. M. Dharmalingam declared that they have no conflict of interest.

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Sreethar, S., Nandhagopal, N., Karuppusamy, S.A. et al. A Group Teaching Optimization Algorithm for Priority-Based Resource Allocation in Wireless Networks. Wireless Pers Commun 123, 2449–2472 (2022). https://doi.org/10.1007/s11277-021-09249-7

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  • DOI: https://doi.org/10.1007/s11277-021-09249-7

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