Search Results for author: Shiwen Mao

Found 6 papers, 1 papers with code

Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning

no code implementations19 Sep 2022 Xianfu Chen, Zhifeng Zhao, Shiwen Mao, Celimuge Wu, Honggang Zhang, Mehdi Bennis

We then put forward a novel offline DAC scheme, which estimates the optimal control policy from a previously collected dataset without any further interactions with the system.

reinforcement-learning

Multi-Agent Collaborative Inference via DNN Decoupling: Intermediate Feature Compression and Edge Learning

1 code implementation24 May 2022 Zhiwei Hao, Guanyu Xu, Yong Luo, Han Hu, Jianping An, Shiwen Mao

In this paper, we study the multi-agent collaborative inference scenario, where a single edge server coordinates the inference of multiple UEs.

A view synthesis-based 360° VR caching system over MEC-Enabled C-RAN

no code implementations1 Oct 2020 Jianmei Dai, Zhilong Zhang, Shiwen Mao, Danpu Liu

If the requested content of a specific view is cached in the BBU pool or RRHs, or can be synthesized with the aid of the cached adjacent views, it is unnecessary to request the content from the remote VR video source server.

Edge-computing

Dealing with Limited Backhaul Capacity in Millimeter Wave Systems: A Deep Reinforcement Learning Approach

no code implementations27 Dec 2018 Mingjie Feng, Shiwen Mao

In this article, we present a deep reinforcement learning (DRL) approach to address this challenge.

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

no code implementations24 Sep 2018 Yaohua Sun, Mugen Peng, Yangcheng Zhou, Yuzhe Huang, Shiwen Mao

As a key technique for enabling artificial intelligence, machine learning (ML) is capable of solving complex problems without explicit programming.

BIG-bench Machine Learning Management +1

Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning

no code implementations16 May 2018 Xianfu Chen, Honggang Zhang, Celimuge Wu, Shiwen Mao, Yusheng Ji, Mehdi Bennis

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services.

Edge-computing reinforcement-learning

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