Quanxin Zhao

TL;DR: An optimization problem is formulated to minimize the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration, and an EECO scheme is designed, which jointly optimizes offloading and radio resource allocation to obtain the minimal energy consumption under the latency constraints.

TL;DR: A novel solution to jointly optimize the allocation of the communication, computing and energy resources in IoT, with the aid of some advanced wireless communication technologies including Wireless Energy Transfer, Mobile-Edge Computing and Full-Duplex is provided.

TL;DR: Simulation results show that the proposed hierarchical RA algorithm and the two-stage game can achieve a higher EE and a lower computation complexity.

TL;DR: This paper forms a stochastic optimization problem to achieve the EE-delay tradeoff, which optimizes the network energy efficiency subject to the network stability, Central Processing Unit-cycle frequency, peak transmission power, and energy causality constraints and proposes a joint computation allocation and resource management algorithm.

TL;DR: This paper develops an Overlapping-Reduced Augmented-Reality Multi-view-video (ORARM) architecture for ARM, and proposes a novel network architecture named Hybrid-Unicasting-Multicasting Device-to-device (HUMD), and a low-complexity resource scheduling scheme named Offloading Traffic Scheduling (OTS).