M
Mugen Peng
Researcher at Beijing University of Posts and Telecommunications
Publications - 554
Citations - 16681
Mugen Peng is an academic researcher from Beijing University of Posts and Telecommunications. The author has contributed to research in topics: Relay & Resource allocation. The author has an hindex of 51, co-authored 501 publications receiving 12800 citations. Previous affiliations of Mugen Peng include Peking University & Chinese Ministry of Education.
Papers
More filters
Proceedings ArticleDOI
Learning-Based Defense against Malicious Unmanned Aerial Vehicles
TL;DR: A reinforcement learning (RL) based defense framework to address malicious UAVs close to a target estate such as a company or an institute using Q-learning to choose the defense policy such as jamming the global positioning system signals and hacking, and laser shooting.
Journal ArticleDOI
Relay Mode Selection and Power Allocation for Hybrid One-Way/Two-Way Half-Duplex/Full-Duplex Relaying
TL;DR: In this letter, a classic three-node relay network with two end nodes and one relay is studied to conveniently formulate the sum data rate of a multicarrier relaynetwork with hybrid relay modes on a per subcarrier basis.
Journal ArticleDOI
A Multidimensional Resource-Allocation Optimization Algorithm for the Network-Coding-Based Multiple-Access Relay Channels in OFDM Systems
TL;DR: This paper investigates the resource allocation for OFDM-based multiuser multiple-access relay channels (MARCs) with network coding by forming a joint optimization problem considering source-node pairing, subcarrier assignment, sub carrier pairing, and power allocation to maximize the sum rate under per-user power constraints.
Journal ArticleDOI
Economical Energy Efficiency: An Advanced Performance Metric for 5G Systems
TL;DR: In this article, economical energy efficiency (E3) is proposed, whose core idea is to take SE/EE and cost into account to evaluate comprehensive gains when different kinds of advanced technologies are used in 5G systems.
Journal ArticleDOI
On the Design of Federated Learning in the Mobile Edge Computing Systems
TL;DR: The performance of the proposed optimization scheme is evaluated by numerical simulation and experiment results, which show that both the accuracy loss and cost of federated learning in the MEC systems can be reduced significantly by employing the proposed algorithm.