Abstract-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 a 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 express 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. Then, we reformulate the algorithm in 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.
Sardellitti et al. REF designed an iterative algorithm based on successive convex approximation techniques to minimize the overall users' energy consumption with latency constraints in a MIMO multicell system.
Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing
2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
Computer Science Mathematics