Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing
Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing
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DOI：
10.1109/tsipn.2015.2448520
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发表时间：
2015-06-01
影响因子：
3.2
通讯作者：
Barbarossa, Sergio
中科院分区：
文献类型：
--
作者：
Sardellitti, Stefania;Scutari, Gesualdo;Barbarossa, Sergio
将计算密集型任务从移动的设备迁移到资源更丰富的云服务器是一种很有前途的技术，可以增加移动的设备的计算能力，同时节省它们的电池能量。在本文中，我们考虑一个MIMO多小区系统，多个移动的用户（MU）要求计算卸载到一个共同的云服务器。我们制定的卸载问题的无线电资源的联合优化的MU的发射预编码矩阵和计算资源的CPU周期/秒分配的云到每个MU，以最大限度地减少整体用户的能量消耗，同时满足延迟约束。由此产生的优化问题是非凸的（在目标函数和约束中）。然而，在单用户的情况下，我们能够计算封闭形式的全局最优解。在更具挑战性的多用户场景中，我们提出了一种迭代算法，基于一种新的连续凸逼近技术，收敛到原来的非凸问题的局部最优解。然后，我们表明，所提出的算法框架自然会导致一个分布式和并行的无线电接入点的实现，只需要有限的协调/信令与云。数值结果表明，所提出的方案优于不相交优化算法。
Migrating computational intensive tasks from mobile devices to more resourceful cloud servers is a promising technique to increase the computational capacity of mobile devices while saving their battery energy. In this paper, we consider an MIMO multicell system where multiple mobile users (MUs) ask for computation offloading to a common cloud server. We formulate the offloading problem as the joint optimization of the radio resources-the transmit precoding matrices of the MUs-and the computational resources-the CPU cycles/second assigned by the cloud to each MU-in order to minimize the overall users' energy consumption, while meeting latency constraints. The resulting optimization problem is nonconvex (in the objective function and constraints). Nevertheless, in the single-user case, we are able to compute the global optimal solution in closed form. In the more challenging multiuser scenario, we propose an iterative algorithm, based on a novel successive convex approximation technique, converging to a local optimal solution of the original nonconvex problem. We then show that the proposed algorithmic framework naturally leads to a distributed and parallel implementation across the radio access points, requiring only a limited coordination/signaling with the cloud. Numerical results show that the proposed schemes outperform disjoint optimization algorithms.