works on

From the 1 of 8 linked papers with an AI index.

most citedA Survey on Reconfigurable and Movable Antennas for Wireless Communications and Sensing

10 citations · 10 across the 3 of their papers we have counts for

collaborators

8 papers

eess.SP2026

Multi-User Localization via Active Sensing with Electromagnetically Reconfigurable Antennas

Ruizhi Zhang, Yuchen Zhang, Ying Zhang +1

The paper proposes an active‑sensing framework for multi‑user uplink localization that adaptively configures shared electromagnetically reconfigurable antennas using past pilot obs…

eess.SP2026

Tri-Hybrid Beamforming Design for ISAC Systems with Reconfigurable Antennas

Jiangong Chen, Xia Lei, Yuchen Zhang +2

Integrated Sensing and Communication (ISAC) systems require efficient beamforming architectures to jointly support communication and sensing functionalities. To reduce hardware ove…

eess.SP202610 cited

A Survey on Reconfigurable and Movable Antennas for Wireless Communications and Sensing

Wenyan Ma, Lipeng Zhu, Yanhua Tan +10

Reconfigurable antennas (RAs) and movable antennas (MAs) have been recognized as promising technologies to enhance the performance of wireless communication and sensing systems by…

eess.SP2026

User Localization via Active Sensing with Electromagnetically Reconfigurable Antennas

Ruizhi Zhang, Yuchen Zhang, Ying Zhang

This paper presents an end-to-end deep learning framework for electromagnetically reconfigurable antenna (ERA)-aided user localization with active sensing, where ERAs provide addit…

cs.IT2026

Precoding Matrix Indicator in the 5G NR Protocol: A Tutorial on 3GPP Beamforming Codebooks

Boyu Ning, Haifan Yin, Sixu Liu +8

This paper bridges this critical gap by providing a systematic examination of the beamforming codebook technology, i.e., precoding matrix indicator (PMI), in the 5G NR from theoret…

cs.IT2025

A Deep Learning Framework for Joint Channel Acquisition and Communication Optimization in Movable Antenna Systems

Ruizhi Zhang, Yuchen Zhang, Lipeng Zhu +2

This paper presents an end-to-end deep learning framework in a movable antenna (MA)-enabled multiuser communication system. In contrast to the conventional works assuming perfect c…