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
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Journal ArticleDOI
Cross-layer design for tree-type routing and level-based centralised scheduling in IEEE 802.16 based wireless mesh networks
Mugen Peng,Yuexin Wang,Wei Wang +2 more
TL;DR: Simulation results show that the proposed cross-layer design strategy can effectively improve the network throughput performance, decrease the power consumption and achieve better performances.
Proceedings ArticleDOI
Reinforcement Learning-Based Interference Control for Ultra-Dense Small Cells
TL;DR: A reinforcement learning based downlink power control algorithm to manage interference for the ultra-dense small cell networks and significantly improves the network throughput and saves the energy consumption compared with the benchmark, a data-driven based transmission power adaptation scheme.
Journal ArticleDOI
Near-Optimal Modulo-and-Forward Scheme for the Untrusted Relay Channel
TL;DR: In this article, the authors proposed a modulo-and-forward (MF) operation at the relay with nested lattice encoding at the source, which achieves the secrecy capacity within 1/2 bit for all channel realizations, and hence achieves full generalized security degrees of freedom (G-SDoF).
Journal ArticleDOI
Training Design for Channel Estimation in Uplink Cloud Radio Access Networks
TL;DR: Segment training based individual channel estimation for C-RANs is proposed, in which channel state information acquisition is performed through two consecutive segments, and the sequential minimum mean-square-error (SMMSE) estimator is developed.
Journal ArticleDOI
Superimposed Training Based Channel Estimation for Uplink Multiple Access Relay Networks
TL;DR: Simulation results show that the presented ST scheme can effectively improve the performance of multi-user detection in MARNs, and the proposed CBCE algorithm significantly outperforms the existing channel estimation methods.