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Distributed Resource Allocation and Computation Offloading Scheme for Cognitive Mobile Edge Computing Networks with NOMA

机译:具有NOMA的认知移动边缘计算网络的分布式资源分配和计算分流方案

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Mobile edge computing (MEC) has become a key component of 5G, via bringing computation services to the edge of the mobile network to enhance the computational capacity of users. To improve spectrum efficiency, we adopt cognitive radio (CR) technology in the MEC system, which makes the resource allocation and computation offloading problem more complex. In this paper, we consider a multi-MEC and multi-user system combined with CR, where each user has a computation task to complete. To further improve the system efficiency, non-orthogonal multiple access (NOMA) is adopted by users. We formulate the resource allocation and computation offloading problem in our scenario as an optimization problem with multiple constraints which aims to maximize the system utility. Alternating direction method of multipliers (ADMM) is adopted to solve this problem in a distributed and efficient way. Simulation results show that our algorithm could achieve great performance comparing to state-of-art distributed and centralized approaches.
机译:通过将计算服务引入移动网络的边缘以增强用户的计算能力,移动边缘计算(MEC)已成为5G的关键组成部分。为了提高频谱效率,我们在MEC系统中采用认知无线电(CR)技术,这使资源分配和计算分流问题变得更加复杂。在本文中,我们考虑了结合了CR的多MEC和多用户系统,其中每个用户都有一个要完成的计算任务。为了进一步提高系统效率,用户采用了非正交多路访问(NOMA)。在我们的方案中,我们将资源分配和计算分流问题表述为具有多个约束条件的优化问题,旨在最大化系统效用。为了解决这个问题,采用了交替方向乘数法(ADMM)。仿真结果表明,与最新的分布式和集中式方法相比,我们的算法可以实现出色的性能。

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