We consider a MIMO multicell system wherein several Mobile Users (MUs) ask for computation offloading to a common cloud server through their femto-access points. We formulate the computation offloading problem as a 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 the latency constraints imposed by the applications running on the MUs. The resulting optimization problem is nonconvex (in the objective function and the constraints), and there are constraints coupling all the optimization variables. To cope with the nonconvexity, we hinge on successive convex approximation techniques and propose an iterative algorithm converging to a local optimal solution of the original nonconvex problem. The algorithm is also suitable for a parallel implementation across the access point, with limited coordination/signaling with the cloud. Numerical results show that the proposed joint optimization yields significant energy savings with respect to more traditional schemes performing a separate optimization of the radio and computational resources.

Joint optimization of radio and computational resources for multicell mobile cloud computing / Sardellitti, S.; Scutari, G.; Barbarossa, S.. - 2014(2014), pp. 354-358. ((Intervento presentato al convegno 2014 15th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2014 tenutosi a Toronto; Canada [10.1109/SPAWC.2014.6941749].

Joint optimization of radio and computational resources for multicell mobile cloud computing

Sardellitti S.;Barbarossa S.
2014

Abstract

We consider a MIMO multicell system wherein several Mobile Users (MUs) ask for computation offloading to a common cloud server through their femto-access points. We formulate the computation offloading problem as a 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 the latency constraints imposed by the applications running on the MUs. The resulting optimization problem is nonconvex (in the objective function and the constraints), and there are constraints coupling all the optimization variables. To cope with the nonconvexity, we hinge on successive convex approximation techniques and propose an iterative algorithm converging to a local optimal solution of the original nonconvex problem. The algorithm is also suitable for a parallel implementation across the access point, with limited coordination/signaling with the cloud. Numerical results show that the proposed joint optimization yields significant energy savings with respect to more traditional schemes performing a separate optimization of the radio and computational resources.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/1395462
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