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
Cluster formation in cloud-radio access networks: Performance analysis and algorithms design
TL;DR: Simulation results show that the proposed approaches can achieve better performance with smaller cluster settings, and two distributed algorithms based on the merge and split approach are obtained as efficient solutions for the cases with and without cluster size constraints.
Journal ArticleDOI
Interference Coordination in Heterogeneous Small-Cell Networks: A Coalition Formation Game Approach
TL;DR: Simulation results have verified that the proposed algorithm can significantly improve the individual SAP throughput for both modes when compared with the conventional and the non-cooperative schemes.
Proceedings ArticleDOI
Classification-based approach for cell outage detection in self-healing heterogeneous networks
TL;DR: This work employs a classification algorithm called K-nearest neighbor (KNN) to achieve automatic anomaly detection in a two-tier macro-pico network based on observation of performance metrics in time domain.
Journal ArticleDOI
Direct Acyclic Graph-Based Ledger for Internet of Things: Performance and Security Analysis
TL;DR: The impact of network load on the performance and security of the DAG-based ledger, based on one of the most typical DAG consensuses, Tangle, is investigated and a stochastic model is proposed to capture the behavior of DAG consensus process under dynamic load conditions.
Journal ArticleDOI
Learning Based Joint Cache and Power Allocation in Fog Radio Access Networks
TL;DR: A deep reinforcement learning (DRL) based joint proactive cache placement and power allocation strategy is proposed in this paper to solve the latency optimization problem for F-RANs and enhance the content serving capability at the edge.