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20232026
most citedTowards Unified AI Models for MU-MIMO Communications: A Tensor Equivariance Framework

13 citations · 13 across the 13 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

eess.SP2024

A Tensor-Structured Approach to Dynamic Channel Prediction for Massive MIMO Systems with Temporal Non-Stationarity

Hongwei Hou, Yafei Wang, Yiming Zhu +4

In moderate- to high-mobility scenarios, CSI varies rapidly and becomes temporally non-stationary, leading to severe performance degradation in the massive MIMO transmissions. To a…

eess.SP2024

Polar-Coded Tensor-Based Unsourced Random Access with Soft Decoding

Jiaqi Fang, Yan Liang, Gangle Sun +4

The unsourced random access (URA) has emerged as a viable scheme for supporting the massive machine-type communications (mMTC) in the sixth generation (6G) wireless networks. Notab…

eess.SP2024

Joint Channel Estimation and Prediction for Massive MIMO with Frequency Hopping Sounding

Yiming Zhu, Jiawei Zhuang, Gangle Sun +3

In massive multiple-input multiple-output (MIMO) systems, the downlink transmission performance heavily relies on accurate channel state information (CSI). Constrained by the trans…

eess.SP2024★ 13 cited

Towards Unified AI Models for MU-MIMO Communications: A Tensor Equivariance Framework

Yafei Wang, Hongwei Hou, Xinping Yi +2

In this paper, we propose a unified framework based on equivariance for the design of artificial intelligence (AI)-assisted technologies in multi-user multiple-input-multiple-outpu…

eess.SP2024

Robust Symbol-Level Precoding for Massive MIMO Communication Under Channel Aging

Yafei Wang, Xinping Yi, Hongwei Hou +2

This paper investigates the robust design of symbol-level precoding (SLP) for multiuser multiple-input multiple-output (MIMO) downlink transmission with imperfect channel state inf…