Gaofeng Nie

TL;DR: This paper forms the energy consumption minimization problem as a mixed interger nonlinear programming (MINLP) problem, which is subject to specific application latency constraints, and proposes a reformulation-linearization-technique-based Branch-and-Bound (RLTBB) method, which can obtain the optimal result or a suboptimal result by setting the solving accuracy.

TL;DR: An integration architecture of wireless powered MEC for IIoT is proposed with joint consideration of energy, communication, and computing resources, enabled by an efficient system schedule mechanism to achieve age-aware data update, green and sustainable energy supply, as well as hierarchical and resilient computation.

TL;DR: A model-free online TDD configuration scheme is proposed based on context analysis and multi-armed bandit (MAB) optimization to efficiently exploit the networking and computing functionalities for TDD orthogonal frequency division multiple access (TDD-OFDMA) technology supporting multiple services.

TL;DR: Simulation results show that the proposed model to minimize total power consumption by jointly optimizing the user association and cache deployment outperforms the other existing multicast and caching algorithms in terms of power consumption, while keeping the load among base stations balanced.

TL;DR: This paper aims to minimize the total energy consumed by the UAV during the region detection mission through jointly optimizing the collected data size, transmission time, and flying trajectory, and proposes a model-free reinforcement learning-based algorithm for training the Uav to plan its trajectory without knowing the environment information in advance.