Distributed learning in wireless networks: Recent progress and future challenges
The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …
applications to efficiently analyze various types of data collected by edge devices for …
[HTML][HTML] Backscatter communications: Inception of the battery-free era—A comprehensive survey
The ever increasing proliferation of wireless objects and consistent connectivity demands
are creating significant challenges for battery-constrained wireless devices. The vision of …
are creating significant challenges for battery-constrained wireless devices. The vision of …
Model-free training of end-to-end communication systems
The idea of end-to-end learning of communication systems through neural network (NN)-
based autoencoders has the shortcoming that it requires a differentiable channel model. We …
based autoencoders has the shortcoming that it requires a differentiable channel model. We …
End-to-end learning of communications systems without a channel model
The idea of end-to-end learning of communications systems through neural network (NN)-
based autoencoders has the shortcoming that it requires a differentiable channel model. We …
based autoencoders has the shortcoming that it requires a differentiable channel model. We …
Challenges and countermeasures for adversarial attacks on deep reinforcement learning
Deep reinforcement learning (DRL) has numerous applications in the real world, thanks to
its ability to achieve high performance in a range of environments with little manual …
its ability to achieve high performance in a range of environments with little manual …
Distributed artificial intelligence solution for D2D communication in 5G networks
Device-to-device (D2D) communication, a core technology component of the evolving fifth-
generation (5G) architecture, promises improvements in energy efficiency, spectral …
generation (5G) architecture, promises improvements in energy efficiency, spectral …
多Agent 深度强化学习综述
梁星星， 冯旸赫， 马扬， 程光权， 黄金才， 王琦， 周玉珍… - 自动化学报, 2020 - aas.net.cn
多Agent深度强化学习综述 E-mail Alert RSS 2.765 2022影响因子 (CJCR) 中文核心 EI 中国科技
核心 Scopus CSCD 英国科学文摘 首页 期刊介绍 1.基本信息 2.收录与获奖 3.近年指标 期刊在线 …
核心 Scopus CSCD 英国科学文摘 首页 期刊介绍 1.基本信息 2.收录与获奖 3.近年指标 期刊在线 …
On the robustness of cooperative multi-agent reinforcement learning
In cooperative multi-agent reinforcement learning (c-MARL), agents learn to cooperatively
take actions as a team to maximize a total team reward. We analyze the robustness of c …
take actions as a team to maximize a total team reward. We analyze the robustness of c …
A novel Distributed AI framework with ML for D2D communication in 5G/6G networks
Inspired by the adoption of Artificial Intelligence (AI) and Machine Learning (ML) approaches
in 5G and 6G networks, in this paper we propose a novel ML based Distributed AI (DAI) …
in 5G and 6G networks, in this paper we propose a novel ML based Distributed AI (DAI) …
Certified policy smoothing for cooperative multi-agent reinforcement learning
Cooperative multi-agent reinforcement learning (c-MARL) is widely applied in safety-critical
scenarios, thus the analysis of robustness for c-MARL models is profoundly important …
scenarios, thus the analysis of robustness for c-MARL models is profoundly important …