Mobile edge computing (MEC) as a new type of computing model can expand the computing power of cloud computing to the edge of radio access network (RAN), which brings a large number of applications close for end user. Compared to traditional cloud computing, computation tasks being offloaded to edge clouds nearby to execute can reduce transmission delay and energy consumption. However, how to select the best edge cloud in a dense cell to execute tasks remains challenging. To address this challenge, in this paper we propose joint user selection and resource allocation algorithm in MEC to maximize the user’s energy efficiency, defined as the ratio of user throughput to its energy consumption. We formulate the energy efficiency maximization problem as a mixed integer fractional nonlinear optimization problem, which involves both users’ offloading selection and uplink transmission power. To solve this non-convex optimization problem, we transform it into an equivalent subtractive convex optimization problem by relaxation transformation method, and furthermore provide the corresponding optimal solution of user selection and power allocation. Numerical results show that compared with other selection schemes, the proposed optimal scheme has a significant improvement in energy efficiency.

移动边缘计算（MEC）作为一种新型的计算模型，可以将云计算的计算能力扩展到无线接入网络（RAN）的边缘，从而使大量应用程序接近最终用户。与传统的云计算相比，将计算任务转移到附近的边缘云上执行可以减少传输延迟和能耗。然而，如何在密集的单元中选择最佳的边缘云来执行任务仍然具有挑战性。为了应对这一挑战，在本文中，我们提出了MEC中的联合用户选择和资源分配算法，以最大程度地提高用户的能源效率，定义为用户吞吐量与其能耗之间的比率。我们将能效最大化问题公式化为混合整数分数非线性优化问题，这既涉及用户的卸载选择，又涉及上行传输功率。为了解决这个非凸优化问题，我们通过松弛变换法将其转化为等效的减法凸优化问题，并进一步提供了相应的用户选择和功率分配的最优方案。数值结果表明，与其他选择方案相比，所提出的最优方案在能效上有显着提高。