42 citations · 107 across the 21 of their papers we have counts for
7 papers · 1 filter
Uplink Transmission Design for Crowded Correlated Cell-Free Massive MIMO-OFDM Systems
Junyuan Gao, Yongpeng Wu, Yongjian Wang +2
In cell-free massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, user equipments (UEs) are served by many distributed access po…
Massive Unsourced Random Access for Massive MIMO Correlated Channels
Xinyu Xie, Yongpeng Wu, Junyuan Gao +1
This paper investigates the massive random access for a huge amount of user devices served by a base station (BS) equipped with a massive number of antennas. We consider a grant-fr…
SPARC-LDPC Coding for MIMO Massive Unsourced Random Access
Tianya Li, Yongpeng Wu, Mengfan Zheng +2
A joint sparse-regression-code (SPARC) and low-density-parity-check (LDPC) coding scheme for multiple-input multiple-output (MIMO) massive unsourced random access (URA) is proposed…
Energy-efficiency of Massive Random Access with Individual Codebook
Junyuan Gao, Yongpeng Wu, Wenjun Zhang
The massive machine-type communication has been one of the most representative services for future wireless networks. It aims to support massive connectivity of user equipments (UE…
Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation
Hang Wang, Minghao Xu, Bingbing Ni +1
Transferring knowledges learned from multiple source domains to target domain is a more practical and challenging task than conventional single-source domain adaptation. Furthermor…
Polar Coding and Sparse Spreading for Massive Unsourced Random Access
Mengfan Zheng, Yongpeng Wu, Wenjun Zhang
In this paper, we propose a new polar coding scheme for the unsourced, uncoordinated Gaussian random access channel. Our scheme is based on sparse spreading, treat interference as…
Cross-domain Detection via Graph-induced Prototype Alignment
Minghao Xu, Hang Wang, Bingbing Ni +2
Applying the knowledge of an object detector trained on a specific domain directly onto a new domain is risky, as the gap between two domains can severely degrade model's performan…