2 citations · 7 across the 8 of their papers we have counts for
8 papers
Multi-Functional RIS-enabled Radar and Communication Coexistence: Channel Modeling and a Sub-6 GHz Indoor Measurement Campaign
Anton Tishchenko, Demos Serghiou, Hamidreza Taghvaee +5
In this work, we analyze a multi-functional reconfigurable intelligent surface (MF-RIS)-enabled radar and communication coexistence (RCC) system, detailing the key aspects of its p…
Advanced Closed-Loop Method with Limited Feedback for ISAC
Ashkan Jafari Fesharaki, Yasser Mestrah, Yi Ma +6
6G wireless networks are poised to seamlessly integrate communication, computing, localization, and sensing functionalities, ensuring high reliability and trustworthiness. This pap…
ISAC Channel Modelling -- Perspectives from ETSI
Mohammad Heggo, Arman Shojaeifard, Alain Mourad +3
Integrated Sensing and Communications (ISAC) is defined as one of six usage scenarios in the ITU-R International Mobile Telecommunications (IMT) 2030 framework for 6G. ISAC is envi…
Low-Complexity Channel Estimation for RIS-Assisted Multi-User Wireless Communications
Qingchao Li, Mohammed El-Hajjar, Ibrahim Hemadeh +2
Reconfigurable intelligent surfaces (RISs) are eminently suitable for improving the reliability of wireless communications by jointly designing the active beamforming at the base s…
Statistical CSI-based Beamforming for RIS-Aided Multiuser MISO Systems using Deep Reinforcement Learning
Mahdi Eskandari, Huiling Zhu, Arman Shojaeifard +1
The paper presents a joint beamforming algorithm using statistical channel state information (S-CSI) for reconfigurable intelligent surfaces (RIS) for multiuser MISO wireless commu…
Sim2real for Reinforcement Learning Driven Next Generation Networks
Peizheng Li, Jonathan Thomas, Xiaoyang Wang +8
The next generation of networks will actively embrace artificial intelligence (AI) and machine learning (ML) technologies for automation networks and optimal network operation stra…
Smart Interference Management xApp using Deep Reinforcement Learning
Mahdi Eskandari, Shipra Kapoor, Keith Briggs +3
Interference continues to be a key limiting factor in cellular radio access network (RAN) deployments. Effective, data-driven, self-adapting radio resource management (RRM) solutio…
RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN
Peizheng Li, Jonathan Thomas, Xiaoyang Wang +8
Radio access network (RAN) technologies continue to evolve, with Open RAN gaining the most recent momentum. In the O-RAN specifications, the RAN intelligent controllers (RICs) are…