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In this paper, we investigate the beamformer design of the energy-efficient downlink multicast cloud radio access network (C-RAN) with the compression and data-sharing strategies, respectively. With the imperfect channel state information, we aim to maximize the worst-case energy efficiency (EE), which is defined as the ratio of the sum of worst-case group rates to the total power consumption of the network. The formulated problems for both strategies are non-convex and challenging to solve. In order to find the solutions, we develop a two-layer iterative algorithm that recast the formulated problems as a series of convex quadratic matrix inequality problems which can be solved by the generated Augmented Lagrangian method. The convergence of the proposed algorithms is proved via both analytical and simulated ways, and the effectiveness of them on improving EE is also validated. Moreover, through the comparison of the two transmission strategies considered, we find that the compression strategy can achieve higher worst-case EE than the data-sharing strategy in most scenarios, except for the case with low rate requirement in small-size network. Our conclusions can be used as meaningful references for the practical system design. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) is a promising architecture to reduce the energy consumption of mobile devices and provide satisfactory quality-of-service to time-sensitive services. How to jointly optimize task offloading and resource allocation to minimize the energy consumption subject to the latency requirement remains an open problem, which motivates this paper. When the latency constraint is taken into account, the optimization variables, including offloading ratio, transmission power, and subcarrier and computing resource allocation, are strongly coupled. To address this issue, we first decompose the original problem into three subproblems named as offloading ratio selection, transmission power optimization, and subcarrier and computing resource allocation. Then, we propose an iterative algorithm to deal with them in a sequence. To be specific, we derive the closed-form solution of offloading ratios, employ the equivalent parametric convex programming to obtain the optimal power allocation policy, and deal with subcarrier and computing resource allocation by the primal-dual method. Simulation results demonstrate that the proposed algorithm can save 20%–40% energy compared with the reference schemes, and can converge to local optimal solutions. [ABSTRACT FROM AUTHOR]

Based on a hierarchical cloud-fog computing-enabled paradigm, fog radio access networks (F-RANs) can provide abundant resource to support the future mobile artificial intelligent services. However, due to the differences of computation and communication capabilities at the cloud computing center, the fog computing based access points (F-APs), and the user devices, it is challenging to propose efficient computation offloading strategies to fully explore the potential of F-RANs. In this paper, we study the design of computation offloading in F-RANs to minimize the total cost with respect to the energy consumption and the offloading latency. In particular, a joint optimization problem is formulated to optimize the offloading decision, the computation and the radio resources allocation. To solve this non-linear and non-convex problem, an iterative algorithm is designed, which can be proved to converge a stationary optimal solution with polynomial computational complexity. Finally, the simulation results are provided to show the performance gains of our proposed joint optimization algorithm. [ABSTRACT FROM AUTHOR]