Search Results for author: Zhifeng Zhao

Found 17 papers, 2 papers with code

Reinforcement Learning-powered Semantic Communication via Semantic Similarity

1 code implementation27 Aug 2021 Kun Lu, Rongpeng Li, Xianfu Chen, Zhifeng Zhao, Honggang Zhang

We introduce a new semantic communication mechanism, whose key idea is to preserve the semantic information instead of strictly securing the bit-level precision.

Semantic Similarity Semantic Textual Similarity

Semantic Communication with Adaptive Universal Transformer

no code implementations20 Aug 2021 Qingyang Zhou, Rongpeng Li, Zhifeng Zhao, Chenghui Peng, Honggang Zhang

With the development of deep learning (DL), natural language processing (NLP) makes it possible for us to analyze and understand a large amount of language texts.

The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication

no code implementations24 Mar 2021 Xing Xu, Rongpeng Li, Zhifeng Zhao, Honggang Zhang

Since the deep neural network models in federated learning are trained locally and aggregated iteratively through a central server, frequent information exchange incurs a large amount of communication overheads.

Decision Making Federated Learning +1

Learning to Prune in Training via Dynamic Channel Propagation

1 code implementation3 Jul 2020 Shibo Shen, Rongpeng Li, Zhifeng Zhao, Honggang Zhang, Yugeng Zhou

In this paper, we propose a novel network training mechanism called "dynamic channel propagation" to prune the neural networks during the training period.

Stigmergic Independent Reinforcement Learning for Multi-Agent Collaboration

no code implementations28 Nov 2019 Xing Xu, Rongpeng Li, Zhifeng Zhao, Honggang Zhang

With the rapid evolution of wireless mobile devices, there emerges an increased need to design effective collaboration mechanisms between intelligent agents, so as to gradually approach the final collective objective through continuously learning from the environment based on their individual observations.

Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicing

no code implementations10 Jun 2019 Chen Qi, Yuxiu Hua, Rongpeng Li, Zhifeng Zhao, Honggang Zhang

Furthermore, as DPGD only works in continuous action space, we embed a k-nearest neighbor algorithm into DQL to quickly find a valid action in the discrete space nearest to the DPGD output.


GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing

no code implementations10 May 2019 Yuxiu Hua, Rongpeng Li, Zhifeng Zhao, Xianfu Chen, Honggang Zhang

Moreover, we further develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to learn the action-value distribution by estimating the state-value distribution and the action advantage function.

Distributional Reinforcement Learning

Internet of Intelligence: The Collective Advantage for Advancing Communications and Intelligence

no code implementations26 Apr 2019 Rongpeng Li, Zhifeng Zhao, Xing Xu, Fei Ni, Honggang Zhang

Afterwards, we highlight the potential huge impact of CI on both communications and intelligence.

Evaluation Mechanism of Collective Intelligence for Heterogeneous Agents Group

no code implementations1 Mar 2019 Anna Dai, Zhifeng Zhao, Honggang Zhang, Rongpeng Li, Yugeng Zhou

Collective intelligence is manifested when multiple agents coherently work in observation, interaction, decision-making and action.

Decision Making

Brain-Inspired Stigmergy Learning

no code implementations20 Nov 2018 Xing Hsu, Zhifeng Zhao, Rongpeng Li, Honggang Zhang

Stigmergy has proved its great superiority in terms of distributed control, robustness and adaptability, thus being regarded as an ideal solution for large-scale swarm control problems.

Deep Learning with Long Short-Term Memory for Time Series Prediction

no code implementations24 Oct 2018 Yuxiu Hua, Zhifeng Zhao, Rongpeng Li, Xianfu Chen, Zhiming Liu, Honggang Zhang

Time series prediction can be generalized as a process that extracts useful information from historical records and then determines future values.

Time Series Time Series Prediction

AI-based Two-Stage Intrusion Detection for Software Defined IoT Networks

no code implementations7 Jun 2018 Jiaqi Li, Zhifeng Zhao, Rongpeng Li, Honggang Zhang

Software Defined Internet of Things (SD-IoT) Networks profits from centralized management and interactive resource sharing which enhances the efficiency and scalability of IoT applications.

Intrusion Detection

Deep Reinforcement Learning for Resource Management in Network Slicing

no code implementations17 May 2018 Rongpeng Li, Zhifeng Zhao, Qi Sun, Chi-Lin I, Chenyang Yang, Xianfu Chen, MinJian Zhao, Honggang Zhang

Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices.

Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory

no code implementations8 Nov 2017 Yuxiu Hua, Zhifeng Zhao, Rongpeng Li, Xianfu Chen, Zhiming Liu, Honggang Zhang

So, the RCLSTM, with certain intrinsic sparsity, have many neural connections absent (distinguished from the full connectivity) and which leads to the reduction of the parameters to be trained and the computational cost.

Traffic Prediction

A Machine Learning Based Intrusion Detection System for Software Defined 5G Network

no code implementations10 Jul 2017 Jiaqi Li, Zhifeng Zhao, Rongpeng Li

As an inevitable trend of future 5G networks, Software Defined architecture has many advantages in providing central- ized control and flexible resource management.

Intrusion Detection

The Learning and Prediction of Application-level Traffic Data in Cellular Networks

no code implementations15 Jun 2016 Rongpeng Li, Zhifeng Zhao, Jianchao Zheng, Chengli Mei, Yueming Cai, Honggang Zhang

Afterwards, with the aid of the traffic "big data", we make a comprehensive study over the modeling and prediction framework of cellular network traffic.

Dictionary Learning Traffic Prediction

TACT: A Transfer Actor-Critic Learning Framework for Energy Saving in Cellular Radio Access Networks

no code implementations28 Nov 2012 Rongpeng Li, Zhifeng Zhao, Xianfu Chen, Jacques Palicot, Honggang Zhang

Recent works have validated the possibility of improving energy efficiency in radio access networks (RANs), achieved by dynamically turning on/off some base stations (BSs).

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