Abstract: Mobile Edge Computing (MEC) is an emerging computational paradigm, it can enhance the computing capacity of the device terminals, reduce the delay and energy consumption greatly, and extend battery life while ensuring user's service experience. Ultra-dense network (UDN) is regarded as the key technology of 5G wireless communication, it reduces system computing costs and improves network throughput to provide low-latency computing communication services. Aiming at MEC combine UDN, the ultra-dense deployment of networks infrastructures causes channel interference and both time delay and energy consumption is considered, the joint optimization problem of resource allocation and task offloading is studied, in order to minimize the total system cost including time delay and energy consumption. Due to coupling of decision variables, the original problem is decomposed into resource optimization problem and task offloading problem. A CF-APSO algorithm is proposed to obtain the suboptimal solution and the effectiveness is validated. The results show that, in ultra-dense deployment environment of micro base stations, compared with other three algorithms, the algorithm proposed can reduce the delay and energy consumption and improve system performance effectively.

Key words: Mobile edge computing, Ultra-dense networks, Resource allocation, Task offloading