Jienan Chen

TL;DR: The proposed intelligent resource allocation framework (iRAF) is a multitask deep reinforcement learning algorithm for making resource allocation decisions based on network states and task characteristics, such as the computing capability of edge servers and devices, communication channel quality, resource utilization, and latency requirement of the services, etc.

TL;DR: This paper proposes a new method, called joint RNS-RPR (JRR), which is the combination of RNS and the reduced precision redundancy (RPR) technique, which inherits the properties of R NS, including shorter critical path, low complexity, and low power.

TL;DR: The GHR combines 2-D and 1-D factorization techniques and improves the throughput by a factor of two to four with comparable hardware cost compared with the previous designs, which is nearly two times better than that of previous FFT processors.

TL;DR: A self-supervised learning based Monte Carlo Tree Search (MCTS) method to solve the antenna selection problem for a massive MIMO system and exhibits a high searching efficiency with near-optimal performance.

TL;DR: An intelligent Task Offloading Algorithm (iTOA) for UAV edge computing network that is able to perceive the network’s environment intelligently to decide the offloading action based on deep Monte Calor Tree Search (MCTS), the core algorithm of Alpha Go.