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20242026
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eess.SP2026

Toward Alias-Free Channel Extrapolation in Upper Mid-Band Systems: A Spatial-Frequency-Temporal Tensor Learning Approach

Jiawei Zhuang, Hongwei Hou, Yafei Wang +4

Upper mid-band massive multiple-input multiple-output (MIMO) offers a favorable capacity-coverage trade-off for next-generation wireless systems, but its large antenna arrays, wide…

eess.SP2026

Learning to Unfold Fractional Programming for Multi-Cell MU-MIMO Beamforming with Graph Neural Networks

Zihan Jiao, Xinping Yi, Shi Jin

In the multi-cell multiuser multi-input multi-output (MU-MIMO) systems, fractional programming (FP) has demonstrated considerable effectiveness in optimizing beamforming vectors, y…

eess.SP2025

Unlocking Symbol-Level Precoding Efficiency Through Tensor Equivariant Neural Network

Jinshuo Zhang, Yafei Wang, Xinping Yi +4

Although symbol-level precoding (SLP) based on constructive interference (CI) exploitation offers performance gains, its high complexity remains a bottleneck. This paper addresses…

eess.SP2025

Tensor-Structured Bayesian Channel Prediction for Upper Mid-Band XL-MIMO Systems

Hongwei Hou, Yafei Wang, Xinping Yi +3

The upper mid-band balances coverage and capacity for the future cellular systems and also embraces XL-MIMO systems, offering enhanced spectral and energy efficiency. However, thes…

eess.SP2025

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.SP2025

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…