基于终端直通通信的多用户计算卸载资源优化决策

随着计算密集和时延敏感类应用的激增，移动边缘计算（MEC）被提出应用在网络边缘为用户提供计算服务。针对基站（BS）端边缘服务器计算资源有限以及网络边缘用户远距离计算卸载的时延较长等问题，提出了基于终端直通（D2D）通信的多用户计算卸载资源优化决策，将D2D融入MEC网络使用户以D2D方式直接卸载任务到相邻用户处执行，从而能够进一步降低卸载时延和能耗。首先，以最小化包括时延和能耗的系统计算总开销为优化目标，建模多用户计算卸载和多用户计算资源分配的联合优化问题；然后，将求解该问题看作是一个D2D配对过程，并提出基于稳定匹配的低复杂度的多用户计算卸载资源优化决策算法；最后，迭代求解D2D卸载的优化分配决策。通过理论证明分析了所提算法的稳定性、最优性和复杂度等特性。仿真结果表明，所提算法相较于随机匹配算法能够有效降低10%~33%的系统计算总开销，并且其性能非常接近最优的穷举搜索算法。可见，所提基于D2D卸载的决策有利于改善时延和能耗开销性能。

Multi-user computation offloading and resource optimization policy based on device-to-device communication

With the significant increase of computation-intensive and latency-intensive applications， Mobile-Edge Computing （MEC） was proposed to provide computing services for users at the network edge. In view of the limited computing resources of edge servers at the Base Stations （BSs） and the long latency of long-distance computation offloading of users at the network edge， a multi-user computation offloading and resource optimization policy based on Device-to-Device （D2D） communication was proposed. The D2D was integrated into MEC network to directly offload tasks to neighbor users for executing in D2D mode， which was able to further reduce offloading latency and energy consumption. Firstly， the joint optimization problem of multi-user computation offloading and multi-user computing resource allocation was modelled with the optimization objective of minimizing the total system computing cost including latency and energy consumption. Then， the solution of this problem was considered as a D2D pairing process， and the multi-user computation offloading and resource optimization policy algorithm was proposed based on stable matching. Finally， the optimization allocation policy of D2D offloading was solved iteratively. The characteristics such as stability， optimality and complexity of the proposed algorithm were analyzed by theoretical proof. Simulation results show that， the proposed algorithm can effectively reduce the total system computing cost by 10%-30% compared with the random matching algorithm， and the performance of the proposed algorithm is very close to the optimal exhaustive search algorithm， indicating that the proposed policy based on D2D offloading is helpful to improve latency and energy consumption performance.