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Xi Han 0001

dblp:25/459-1 · DBLP profile ↗
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2ranked-venue papers
2as first author
1since 2021 · last 2026
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 2 · 2 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Physical-layer communications · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Physical-layer communications
channel estimation
1.012026
Tensor-Based Joint Channel and Symbol Estimation With Subspace-Based Parameter Extraction for Multi-RIS Uplink MIMO Systems · IEEE Trans. Commun. 2026
Physical-layer communications › channel estimation
joint channel estimation and data detection
1.012026
Tensor-Based Joint Channel and Symbol Estimation With Subspace-Based Parameter Extraction for Multi-RIS Uplink MIMO Systems · IEEE Trans. Commun. 2026
Physical-layer communications
MIMO
0.312026
Tensor-Based Joint Channel and Symbol Estimation With Subspace-Based Parameter Extraction for Multi-RIS Uplink MIMO Systems · IEEE Trans. Commun. 2026
Physical-layer communications
reconfigurable intelligent surface
0.312026
Tensor-Based Joint Channel and Symbol Estimation With Subspace-Based Parameter Extraction for Multi-RIS Uplink MIMO Systems · IEEE Trans. Commun. 2026

Methods — techniques the papers use, named apart from their topics

tensor decomposition · 1.0subspace-based parameter estimation · 1.0
YearPublicationVenuePosition
2026 Tensor-Based Joint Channel and Symbol Estimation With Subspace-Based Parameter Extraction for Multi-RIS Uplink MIMO Systems
Xi Han 0001, Jiaxi Ying, Feifei Gao 0001, Chau Yuen
IEEE Trans. Commun.1
2019 Semi-blind receiver for two-way MIMO relaying systems based on joint channel and symbol estimation
abstract
This study proposes a semi‐blind receivers for two‐way multiple‐input multiple‐output (MIMO) relaying systems capable of jointly estimating the channels and symbols in a direct‐data approach. Resorting to a Khatri‐Rao coding scheme applied at both uplink and downlink transmission phases, the authors show that the signals received at the relay and user node are third‐order tensors satisfying the parallel factor (PARAFAC) and PARATUCK2 models, respectively. Combining these two tensor models, a semi‐blind receiver based on an integrated alternating least squares algorithm is proposed to estimate the channels and symbols transmitted by the user nodes without training sequences. The effectiveness of the proposed semi‐blind receiver is corroborated with numerical results, which show that the proposed semi‐blind receiver outperforms state‐of‐the‐art two‐stage training sequence estimator, while operating close to the tensor‐based channel estimator.
Xi Han 0001, André Lima Férrer de Almeida, An Liu 0002, Wenle Bai
IET Commun.1