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20202025
most citedAn Orthogonal-SGD based Learning Approach for MIMO Detection under Multiple Channel Models

2 citations · 2 across the 2 of their papers we have counts for

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6 papers · 1 filter

eess.SP2025

Near-Field 3D Localization and MIMO Channel Estimation with Sub-Connected Planar Arrays

Kangda Zhi, Tianyu Yang, Songyan Xue +1

This paper investigates the design of channel estimation and 3D localization algorithms in a challenging scenario, where a sub-connected planar extremely large-scale multiple-input…

eess.SP2025

Holographic MIMO Multi-Cell Communications

Kangda Zhi, Tianyu Yang, Shuangyang Li +3

Metamaterial antennas are appealing for next-generation wireless networks due to their simplified hardware and much-reduced size, power, and cost. This paper investigates the holog…

eess.SP2021

End-to-End Learning for Uplink MU-SIMO Joint Transmitter and Non-Coherent Receiver Design in Fading Channels

Songyan Xue, Yi Ma, Na Yi

In this paper, a novel end-to-end learning approach, namely JTRD-Net, is proposed for uplink multiuser single-input multiple-output (MU-SIMO) joint transmitter and non-coherent rec…

eess.SP2020

On Deep Learning Solutions for Joint Transmitter and Noncoherent Receiver Design in MU-MIMO Systems

Songyan Xue, Yi Ma, Na Yi +1

This paper aims to handle the joint transmitter and noncoherent receiver design for multiuser multiple-input multiple-output (MU-MIMO) systems through deep learning. Given the deep…

eess.SP2020

A Modular Neural Network Based Deep Learning Approach for MIMO Signal Detection

Songyan Xue, Yi Ma, Na Yi +1

In this paper, we reveal that artificial neural network (ANN) assisted multiple-input multiple-output (MIMO) signal detection can be modeled as ANN-assisted lossy vector quantizati…

eess.SP20202 cited

An Orthogonal-SGD based Learning Approach for MIMO Detection under Multiple Channel Models

Songyan Xue, Yi Ma, Rahim Tafazolli

In this paper, an orthogonal stochastic gradient descent (O-SGD) based learning approach is proposed to tackle the wireless channel over-training problem inherent in artificial neu…