Cooperative Computation Offloading and Resource Management Based on Improved Genetic Algorithm in NOMA-MEC Systems
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摘要: 为平衡网络负载与充分利用网络资源,针对超密集异构的多用户和多任务边缘计算网络,在用户时延约束下,该文构造了协作式计算任务卸载与无线资源管理的联合优化问题以最小化系统能耗。问题建模时,为应对基站超密集部署导致的严重干扰问题,该文采用了频带划分机制,并引入了非正交多址技术(NOMA)以提升上行频谱利用率。鉴于该目标优化问题具备非线性混合整数的形式,根据多样性引导变异的自适应遗传算法(AGADGM),设计出了协作式计算卸载与资源分配算法。仿真结果表明,在严格满足时延约束条件下,该算法能获取较其他算法更低的系统能耗。Abstract: To balance the network loads and utilize fully the network resources, joint cooperative computation offloading and wireless resource management is considered for ultra-dense heterogeneous edge computing networks with multiple users and multiple tasks, which minimizes the system energy consumption under the constraints of users’ delay. During the problem modeling, a frequency spectrum partitioning mechanism is introduced to tackle serious network interference caused by ultra-dense deployment of base stations, and Non-Orthogonal Multiple Access (NOMA) technology is introduced to improve the uplink frequency spectrum efficiency. Considering that the optimization problem is a nonlinear mixed-integer form, according to Adaptive Genetic Algorithm with Diversity-Guided Mutation (AGADGM), an effective algorithm used for cooperative computation offloading and resource allocation is designed. The simulation results show that proposed algorithm could achieve lower system energy consumption than other existing algorithms under strict constraints of users’ delay.
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表 1 参数设置
参数 数值 参数 数值 系统带宽$W$ 20 MHz 用户计算能力$F_k^{{\text{UE}}}$ 1 GHz 子信道带宽$w$ 15 kHz 截止时延$T_k^{{\text{max}}}$ 5~10 s 噪声功率谱密度${\sigma ^2}$ –174 dBm/Hz 用户最大发射功率$p_k^{\max }$ 23 dBm 用户$k$的任务数${M_k}$ 3~7 个 基站计算能力${F^{{\text{MBS}}}}$, ${F^{{\text{SBS}}}}$ 20 GHz 单个任务数据大小${d_k}$ 200~500 kB 种群大小$I$ 64 单个任务执行时所需的CPU周期数${c_k}$ 50~100 cycles/bit -
[1] MAO Yuyi, YOU Changsheng, ZHANG Jun, et al. A survey on mobile edge computing: The communication perspective[J]. IEEE Communications Surveys & Tutorials, 2017, 19(4): 2322–2358. doi: 10.1109/COMST.2017.2745201 [2] GUO Hongzhi, ZHANG Jie, LIU Jiajia, et al. Energy-aware computation offloading and transmit power allocation in ultradense IoT networks[J]. IEEE Internet of Things Journal, 2019, 6(3): 4317–4329. doi: 10.1109/JIOT.2018.2875535 [3] ZHAO Junhui, SUN Xiaoke, LI Qiuping, et al. Edge caching and computation management for real-time internet of vehicles: An online and distributed approach[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(4): 2183–2197. doi: 10.1109/TITS.2020.3012966 [4] ZHOU Tianqing, JIANG Nan, LIU Zunxiong, et al. Joint cell activation and selection for green communications in ultra-dense heterogeneous networks[J]. IEEE Access, 2018, 6: 1894–1904. doi: 10.1109/ACCESS.2017.2780818 [5] DAI Yueyue, XU Du, MAHARJAN S, et al. Joint computation offloading and user association in multi-task mobile edge computing[J]. IEEE Transactions on Vehicular Technology, 2018, 67(12): 12313–12325. doi: 10.1109/TVT.2018.2876804 [6] 刘海燕. 面向5G的超密集网络中分布式无线资源管理的研究[D]. [博士论文], 北京交通大学, 2016.LIU Haiyan. Research on distributed radio resource management in ultra dense networks for 5G communication systems[D]. [Ph. D. Dissertation]. Beijing Jiaotong University, 2016. [7] WU Yuhang, WANG Yuhao, ZHOU Fuhui, et al. Computation efficiency maximization in OFDMA-based mobile edge computing networks[J]. IEEE Communications Letters, 2020, 24(1): 159–163. doi: 10.1109/LCOMM.2019.2950013 [8] DENG Maofei, TIAN Hui, and LYU Xinchen. Adaptive sequential offloading game for multi-cell mobile edge computing[C]. 2016 23rd International Conference on Telecommunications (ICT), Thessaloniki, Greece, 2016: 1–5. [9] ZHOU Tianqing, QIN Donng, NIE Xuefang, et al. Energy-efficient computation offloading and resource management in ultradense heterogeneous networks[J]. IEEE Transactions on Vehicular Technology, 2021, 70(12): 13101–13114. doi: 10.1109/TVT.2021.3116955 [10] ZHOU Fuhui and HU R Q. Computation efficiency maximization in wireless-powered mobile edge computing networks[J]. IEEE Transactions on Wireless Communications, 2020, 19(5): 3170–3184. doi: 10.1109/TWC.2020.2970920 [11] 张海波, 刘香渝, 荆昆仑, 等. 车联网中基于NOMA-MEC的卸载策略研究[J]. 电子与信息学报, 2021, 43(4): 1072–1079. doi: 10.11999/JEIT200017ZHANG Haibo, LIU Xiangyu, JING Kunlun, et al. Research on NOMA-MEC-based offloading strategy in internet of vehicles[J]. Journal of Electronics &Information Technology, 2021, 43(4): 1072–1079. doi: 10.11999/JEIT200017 [12] CHENG Qianqian, LI Lixin, SUN Yan, et al. Efficient resource allocation for NOMA-MEC system in ultra-dense network: A mean field game approach[C]. 2020 IEEE International Conference on Communications Workshops (ICC Workshops), Dublin, Ireland, 2020: 1–6. [13] GUO Fengxian, ZHANG Heli, JI Hong, et al. An efficient computation offloading management scheme in the densely deployed small cell networks with mobile edge computing[J]. IEEE/ACM Transactions on Networking, 2018, 26(6): 2651–2664. doi: 10.1109/TNET.2018.2873002 [14] ZHOU Tianqing, ZHAO Junhui, QIN Dong, et al. Joint user association and time partitioning for load balancing in ultra-dense heterogeneous networks[J]. Mobile Networks and Applications, 2021, 26(2): 909–922. doi: 10.1007/s11036-019-01351-2 [15] YANG Zheng, DING Zhiguo, FAN Pingzhi, et al. A general power allocation scheme to guarantee quality of service in downlink and uplink NOMA systems[J]. IEEE Transactions on Wireless Communications, 2016, 15(11): 7244–7257. doi: 10.1109/TWC.2016.2599521 [16] PHAM Q V, NGUYEN H T, HAN Zhu, et al. Coalitional games for computation offloading in NOMA-enabled multi-access edge computing[J]. IEEE Transactions on Vehicular Technology, 2020, 69(2): 1982–1993. doi: 10.1109/TVT.2019.2956224 [17] ZHOU Tianqing, YUE Yali, QIN Dong, et al. Joint device association, resource allocation and computation offloading in ultra-dense multi-device and multi-task IoT networks[J]. IEEE Internet of Things Journal, To be published. [18] LI Meiyi, CAI Zixing, and SUN Guoyun. An adaptive genetic algorithm with diversity-guided mutation and its global convergence property[J]. Journal of Central South University of Technology, 2004, 11(3): 323–327. doi: 10.1007/s11771-004-0066-6 [19] ZHANG Ke, MAO Yuming, LENG Supeng, et al. Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks[J]. IEEE Access, 2016, 4: 5896–5907. doi: 10.1109/ACCESS.2016.2597169