most citedDeep Unsupervised Learning for Joint Antenna Selection and Hybrid Beamforming

70 citations · 78 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SP2021★ 1 cited

Federated Dynamic Neural Network for Deep MIMO Detection

Yuwen Yang, Feifei Gao, Jiang Xue +2

In this paper, we develop a dynamic detection network (DDNet) based detector for multiple-input multiple-output (MIMO) systems. By constructing an improved DetNet (IDetNet) detecto…

eess.SP2021

Joint Channel Estimation and Mixed-ADCs Allocation for Massive MIMO via Deep Learning

Liangyuan Xu, Feifei Gao, Ting Zhou +2

Millimeter wave (mmWave) multi-user massive multi-input multi-output (MIMO) is a promising technique for the next generation communication systems. However, the hardware cost and p…

eess.SP2021★ 70 cited

Deep Unsupervised Learning for Joint Antenna Selection and Hybrid Beamforming

Zhiyan Liu, Yuwen Yang, Feifei Gao +2

In this paper, we propose a novel deep unsupervised learning-based approach that jointly optimizes antenna selection and hybrid beamforming to improve the hardware and spectral eff…

eess.SP2021★ 6 cited

Deep Learning based Antenna Selection and CSI Extrapolation in Massive MIMO Systems

Bo Lin, Feifei Gao, Shun Zhang +2

A critical bottleneck of massive multiple-input multiple-output (MIMO) system is the huge training overhead caused by downlink transmission, like channel estimation, downlink beamf…

eess.SP2020★ 1 cited

FusionNet: Enhanced Beam Prediction for mmWave Communications Using Sub-6GHz Channel and A Few Pilots

Chenghong Bian, Yuwen Yang, Feifei Gao +1

In this paper, we propose a new downlink beamforming strategy for mmWave communications using uplink sub-6GHz channel information and a very few mmWave pilots. Specifically, we des…