| 多小区蜂窝网络中计算卸载与资源分配联合优化算法 |
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| 引用本文： | 朱科宇,朱琦.多小区蜂窝网络中计算卸载与资源分配联合优化算法[J].信号处理,2021,37(6):1055-1065. |
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| 作者姓名： | 朱科宇 朱琦 |
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| 作者单位： | 南京邮电大学江苏省无线通信重点实验室 |
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| 基金项目： | 国家自然科学基金（61971239，92067201） |
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| 摘 要： | 本文在多基站和远端云构成的多层计算卸载场景中,提出了一种多小区蜂窝网络中基站选择、计算卸载与资源分配联合优化算法。该算法考虑多基站重叠覆盖用户的基站选择,在边缘服务器计算资源约束条件下,构建了能耗与时延加权和的最小化问题,这是NP-hard问题。本文首先对单用户多基站计算卸载问题,采用拉格朗日乘子法对其进行求解;然后针对多用户多基站场景,考虑用户的基站选择以及边缘服务器计算资源的竞争,基于定义的选择函数对接入基站进行选择,采用次优的迭代启发式算法对单用户场景下的卸载决策做出动态修正,获得卸载决策和边缘服务器资源分配。仿真结果表明,提出的计算卸载及资源分配算法能有效的降低任务完成的时延与终端的能耗。
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| 关 键 词： | 移动边缘计算 计算卸载 基站选择 资源分配 终端时延与能耗 |
| 收稿时间： | 2020-11-23 |
Joint Optimization Algorithm of Computation Offloading and Resource Allocation in Multi-cell Cellular Networks |
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| Affiliation: | Jiangsu Key Lab of Wireless Communications, Nanjing University of Posts and Telecommunications, NanjingKey Lab on Wideband Wireless Communications and Sensor Network Technology of Ministry of Education, Nanjing University of Posts and Telecommunications |
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| Abstract: | In this paper, a joint optimization algorithm of base station selection, computation offloading and resource allocation in multi-cell cellular networks is proposed in the multi-layer computation offloading scenario composed of the multi-base station and remote cloud. The algorithm considers the base station selection of multi-base station overlapping coverage users and constructs the minimization problem of the weighted sum of energy consumption and delay under the constraint of edge server computing resources, which is NP-hard. We start from the single user multi-cell computation offloading problem solved by the Lagrange multiplier method. Secondly, for the multi-user multi-base station scenario, considering the user's selection of base station and the competition of computing resources of edge server, the access base station is selected based on the defined selection function, and the suboptimal iterative heuristic algorithm is adopted to make the dynamic correction for the offloading decision in the single-user scenario, so as to obtain the offloading decision and resource allocation of the edge server. Simulation results show that the proposed algorithm can effectively reduce the task completion delay and terminal energy consumption. |
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