基于HAGA的D2D-NOMA资源分配优化算法

doi:10.19678/j.issn.1000-3428.0060148

计算机工程 ›› 2022, Vol. 48 ›› Issue (2): 275-280,290. doi: 10.19678/j.issn.1000-3428.0060148

基于HAGA的D2D-NOMA资源分配优化算法

宋勇春^1,2, 王茜竹^1,2, 高正念^1,2

1. 重庆邮电大学电子信息与网络工程研究院, 重庆 400065;
2. 新一代信息网络与终端协同创新中心, 重庆 400065

收稿日期:2020-11-30 修回日期:2021-01-12 发布日期:2021-01-20
作者简介:宋勇春(1995-),女,硕士研究生,主研方向为移动通信技术;王茜竹,正高级工程师;高正念,硕士研究生。
基金资助:
重庆市科技重大主题专项重点示范项目（cstc2018jszx-cyztzxX0035）；重庆市教委科学技术研究项目（KJQN201800642）。

D2D-NOMA Resource Allocation Optimization Algorithm Based on HAGA

SONG Yongchun^1,2, WANG Qianzhu^1,2, GAO Zhengnian^1,2

1. Institute of Electronic Information and Network Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
2. Collaborative Innovation Center for Information Communication Technology, Chongqing 400065, China

Received:2020-11-30 Revised:2021-01-12 Published:2021-01-20

摘要/Abstract

摘要： 针对无线系统带宽资源有限、基站负载压力大、传输时延长等问题，提出一种基于非正交多址接入技术的D2D系统吞吐量最大化资源分配算法。在不同用户的服务质量约束条件下，建立D2D系统吞吐量最大化资源分配模型。该模型的优化目标是一个混合整数非线性规划问题，将其解耦为信道匹配与功率分配2个子问题并分别进行处理，利用自适应惩罚函数法处理约束条件并提出一种基于爬山策略的自适应遗传算法以对问题进行求解。仿真结果表明，与GA、AGA算法相比，该算法能够有效提高D2D系统的吞吐量，且收敛性能更好。

关键词: 非正交多址接入, 资源分配, 爬山策略, 自适应遗传算法, 惩罚函数法

Abstract: To address the limited bandwidth resources for wireless systems, heavy loading pressure of base stations, and long-distance transmission delay, this paper proposes a resource allocation algorithm to maximize the throughput of Device-to-Device(D2D) systems based on the Non-Orthogonal Multiple Access(NOMA).A resource allocation model is constructed to maximize the throughput of D2D systems based on Quality of Service(QoS) constraints of different users.The optimization problem is simplified into a mixed integer nonlinear programming problem, which is subsequently decoupled into two sub-problems:channel matching and power allocation.On this basis, the constraint conditions are treated with the adaptive penalty function method, and an adaptive genetic algorithm based on Hill-climbing strategy is proposed to solve the problem.Simulation results show that compared with GA and AGA, the proposed algorithm can effectively improve the throughput of D2D systems and provide better convergence performance.

Key words: Non-Orthogonal Multiple Access(NOMA), resource allocation, Hill-climbing, Adaptive Genetic Algorithm(AGA), penalty function method

中图分类号:

TN929.53

宋勇春, 王茜竹, 高正念. 基于HAGA的D2D-NOMA资源分配优化算法[J]. 计算机工程, 2022, 48(2): 275-280,290.

SONG Yongchun, WANG Qianzhu, GAO Zhengnian. D2D-NOMA Resource Allocation Optimization Algorithm Based on HAGA[J]. Computer Engineering, 2022, 48(2): 275-280,290.

https://www.ecice06.com/CN/Y2022/V48/I2/275

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参考文献

[1] Cisco.Cisco visual networking index:global mobile data traffic forecast update[EB/OL].[2020-10-05].http://www.audentia-gestion.fr/cisco/pdf/cisco-vni-forecast-feb2015-22.pdf.
[2] HAKOLA S, TAO C, LEHTOMÄKI J, et al.Device-To-Device(D2D) communication in cellular network-performance analysis of optimum and practical communication mode selection[C]//Proceedings of Wireless Communications & Networking Conference.Washington D.C., USA:IEEE Press, 2010:1-6.
[3] LIN X, ANDREWS J, GHOSH A, et al.An overview of 3GPP device-to-device proximity services[J].IEEE Communications Magazine, 2014, 52(4):40-48.
[4] ANSARI R I, CHRYSOSTOMOU C, HASSAN S A, et al.5G D2D networks:techniques, challenges, and future prospects[J].IEEE Systems Journal, 2018, 12(4):3970-3984.
[5] JIANG D, HUO L, LÜ Z, et al.A joint multi-criteria utility based network selection approach for vehicle-to-infrastructure networking[J].IEEE Transactions on Intelligent Transportation Systems, 2018, 19(10):3305-3319.
[6] LIU Y, QIN Z, ELKASHLAN M, et al.Non-orthogonal multiple access for 5G and beyond[J].Proceedings of the IEEE, 2018, 105(12):2347-2381.
[7] YAKOU K, HIGUCHI K.Downlink NOMA with SIC using unified user grouping for non-orthogonal user multiplexing and decoding order[C]//Proceedings of International Symposium on Intelligent Signal Processing & Communication Systems.Washington D.C., USA:IEEE Press, 2015:508-513.
[8] ALEMAISHAT S, SARAEREH O A, KHAN I, et al.Efficient resource allocation algorithm for D2D communications based on NOMA[J].IEEE Access, 2019, 7:120238-120247.
[9] CHENG R, ZHOU X T, ZHANG H X, et al.Non-orthogonal multiple access in SWIPT enabled cooperative D2D network[C]//Proceedings of 2020 IEEE/CIC International Conference on Communications in China.Washington D.C., USA:IEEE Press, 2020:62-67.
[10] ZHAO J, LIU Y, CHAI K K, et al.Joint subchannel and power allocation for NOMA enhanced D2D communications[J].IEEE Transactions on Communications, 2017, 65(11):5081-5094.
[11] YOON T, NGUYEN T H, NGUYEN X T, et al.Resource allocation for NOMA-based D2D systems coexisting with cellular networks[J].IEEE Access, 2018, 6:66293-66304.
[12] DAI Y P, SHENG M, LIU J Y, et al.Joint mode selection and resource allocation for D2D-enabled NOMA cellular networks[J].IEEE Transactions on Vehicular Technology, 2019, 68(7):6721-6733.
[13] PAN Y J, PAN C H, YANG Z H, et al.Resource allocation for D2D communications underlaying a NOMA-based cellular network[J].IEEE Wireless Communication Letters, 2018, 7(1):130-133.
[14] BUDHIRAJA I, KUMAR N, TYAGI S.Energy-delay tradeoff scheme for NOMA-based D2D groups with WPCNs[J].IEEE Systems Journal, 2020, 8:1-12.
[15] ZHENG H Y, HOU S J, LI H, et al.Power allocation and user clustering for uplink MC-NOMA in D2D underlaid cellular networks[J].IEEE Wireless Communications Letters, 2018, 7(6):1030-1033.
[16] CHEN J, JIA J, LIU Y W, et al.Optimal resource block assignment and power allocation for D2D-enabled NOMA communication[J].IEEE Access, 2019, 7:90023-90035.
[17] LEE S, KIM J, CHO S.Resource allocation for NOMA based D2D system using genetic algorithm with continuous pool[C]//Proceedings of 2019 International Conference on Information and Communication Technology Convergence.Washington D.C., USA:IEEE Press, 2019:705-707.
[18] 蔡海鸾, 郭学萍.一种新的自适应惩罚函数在遗传算法中的应用[J].华东师范大学学报(自然科学版), 2015(6):36-45, 52. CAI H L, GUO X P.Application of a new adaptive penalty function in genetic algorithm[J].Journal of East China Normal University(Natural Science), 2015(6):36-45, 52.(in Chinese)
[19] SRINIVAS M, PATNAIK L M.Adaptive probabilities of crossover and mutation in genetic algorithms[J].IEEE Transactions on Systems Man & Cybernetics, 2002, 24(4):656-667.
[20] XU H Y, ZHOU Z.Hill-climbing genetic algorithm optimization in cognitive radio decision engine[C]//Proceedings of 2013 IEEE International Conference on Communication Technology.Washington D.C., USA:IEEE Press, 2013:115-119.

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