Xinyu Gao
Queen Mary University of London
8 Papers
31 Citations
Xinyu Gao is an academic researcher from Queen Mary University of London. The author has contributed to research in topics: Resource allocation & Computer science. The author has an hindex of 2, co-authored 8 publications.
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Papers
•Posted Content
Robotic Communications for 5G and Beyond: Challenges and Research Opportunities
TL;DR: Signal and spatial modeling for robotic communications are presented, and a novel simultaneous localization and radio mapping (SLARM) framework is proposed for integrating localization and communications into robotic networks.
19
Resource Allocation In IRSs Aided MISO-NOMA Networks: A Machine Learning Approach
Xinyu Gao,Yuanwei Liu,Xiao Liu,Zhijin Qin +3 more
- 01 Dec 2020
TL;DR: In this paper, a novel IRS-aided multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) network is proposed, where a base station (BS) serves multiple clusters with unfixed number of users in each cluster.
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•Posted Content
Resource Allocation in IRSs Aided MISO-NOMA Networks: A Machine Learning Approach
TL;DR: A novel framework of intelligent reflecting surface (IRS)-aided multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) network is proposed, where a base station serves multiple clusters with unfixed number of users in each cluster.
9
Trajectory and Passive Beamforming Design for IRS-aided Multi-Robot NOMA Indoor Networks
Xinyu Gao,Yuanwei Liu,Xidong Mu +2 more
- 14 Jun 2021
TL;DR: In this paper, a novel intelligent reflecting surface (IRS)-aided multi-robot network is proposed, where multiple mobile wheeled robots are served by an access point (AP) through non-orthogonal multiple access (NOMA).
8
SLARM: Simultaneous Localization and Radio Mapping for Communication-aware Connected Robot
Xinyu Gao,Yuanwei Liu,Xidong Mu +2 more
- 14 Jun 2021
TL;DR: In this paper, a novel simultaneous localization and radio mapping (SLARM) framework for communication-aware connected robots in the unknown indoor environment is proposed, where the SLAM algorithm and the global geographic map recovery (GGMR) algorithm are leveraged to simultaneously construct a geographic map and a radio map named a channel power gain map.
8