NOMA物联网下多UAV辅助MEC系统的资源分配

为解决偏远地区或突发灾害等场景中的物联网(Internet of Things, IoT)设备的任务计算问题，构建了一个非正交多址接入(Non-orthogonal Multiple Access, NOMA)-IoT(NOMA-IoT)下多无人机(Unmanned Aerial Vehicle, UAV)辅助的NOMA多址边缘计算(Multiple Access Edge Computing, MEC)系统。该系统中设备的计算能耗、卸载能耗和MEC服务器计算能耗直接受同信道干扰、计算资源和发射功率的影响，可通过联合优化卸载策略、计算资源和发射功率最小化系统加权总能耗。根据优化问题的非凸性和复杂性，提出了一种有效的迭代算法解决：首先，对固定卸载策略，计算资源和发射功率分配问题可通过连续凸逼近转化为可解的凸问题；其次，对固定计算资源和发射功率，利用联盟形成博弈解决卸载策略问题，以最小化IoT设备之间的同信道干扰。仿真结果表明，较OMA接入方式，NOMA接入方式减少本地计算能耗、卸载能耗及计算能耗约20%;较无卸载策略方法，包含卸载策略方法在减少系统加权总能耗方面效果较为明显。

Resource allocation in multi-UAV-enabled MEC system for NOMA-IoT

In order to solve the task computing problem of Internet of Things(IoT) devices in remote or emergency disaster scenarios,a multi-unmanned aerial vehicle(UAV)-enabled multiple access edge computing(MEC) system for non-orthogonal multiple access(NOMA)-IoT(NOMA-IoT) is built,where IoT devices offload the part of computing tasks to the MEC servers using NOMA for calculation.The computing energy consumption,offloading energy consumption and computing energy consumption of MEC server in the system are directly affected by the co-channel interference,computing resources and transmitted power,and the weighted total energy consumption of the system can be minimized by jointly optimizing offloading strategy,computing resources and transmitting power.As the formulated problem suffers non-convexity and complication,an efficient iterative algorithm is proposed to address it.Firstly,for the fixed offloading strategy,the optimization problem of computation resources and transmit power is converted to convex ones via successive convex approximation.Then,for the fixed computation resources and transmit power,the offloading strategy is solved by means of coalitional formation game to minimize co-channel interference between IoT devices.Experimental results show that compared with OMA mode,the NOMA technology reduces local computing energy consumption,offloading energy consumption and computing energy consumption by about 20%.Compared with no offloading strategy,the method including device offloading strategy has a more obvious effect in reducing the total weighted energy consumption of the system.