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 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。在这项研究中，我们开发了一种由认知无人机（CUAV）辅助的MEC系统，配备MEC服务器的CUAV可以作为中继节点和计算节点。在此类网络中，考虑采用非正交多址方案为增强型移动宽带通信 (eMBB) 和海量机器类通信 (mMTC) 用户提供服务，其中推导出两个用户的传输延迟。为了优化该系统中的延迟，我们制定了一个优化问题，旨在通过联合优化用户信息的传输功率来最小化 eMBB 和 mMTC 用户的处理延迟，考虑二次网络发射功率的约束。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。