Mobile edge computing (MEC) in cognitive radio networks is an optimistic technique for improving the computational capability and spectrum utilization efficiency. In this study, we developed an MEC system assisted by a cognitive unmanned aerial vehicle (CUAV), where a CUAV equipped with an MEC server can serve as a relay node and computing node. In such networks, a non-orthogonal multiple access scheme is considered to serve enhanced mobile broadband communication (eMBB) and massive machine-type communication (mMTC) users, in which the transmission delay for both users is derived. To optimize the delay in this system, we formulated an optimization problem aimed at minimizing the processing delay of eMBB and mMTC users by jointly optimizing the transmit power of the users’ information, considering the constraints of the transmit power of the secondary network. The numerical results demonstrate that the proposed Rosen’s gradient projection algorithm can considerably minimize the processing delay for a CUAV with a fixed position compared with a CUAV with a predetermined trajectory.

中文翻译：

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移动边缘计算中的延迟优化：认知无人机辅助的 eMBB 和 mMTC 服务


认知无线电网络中的移动边缘计算（MEC）是一种提高计算能力和频谱利用效率的乐观技术。在本研究中，我们开发了一种由认知无人机（CUAV）辅助的MEC系统，其中配备MEC服务器的CUAV可以充当中继节点和计算节点。在此类网络中，非正交多址方案被认为服务于增强型移动宽带通信（eMBB）和大规模机器类型通信（mMTC）用户，其中导出两个用户的传输延迟。为了优化该系统中的延迟，考虑到二次网络发射功率的约束，我们制定了一个优化问题，旨在通过联合优化用户信息的发射功率来最小化eMBB和mMTC用户的处理延迟。数值结果表明，与具有预定轨迹的 CUAV 相比，所提出的 Rosen 梯度投影算法可以大大减少具有固定位置的 CUAV 的处理延迟。