6 papers · 1 filter
Multi-Branch DNN and CRLB-Ratio-Weight Fusion for Enhanced DOA Sensing via a Massive HAD MIMO Receiver
Feng Shu, Jiatong Bai, Di Wu +4
As a green MIMO structure, massive HAD is viewed as a potential technology for the future 6G wireless network. For such a structure, it is a challenging task to design a low-co…
DNN-based Enhanced DOA Sensing via Massive MIMO Receiver with Switches-based Hybrid Architecture
Yifan Li, Kang Wei, Linqiong Jia +4
Switches-based hybrid architecture has attracted much attention, especially in directional-of-arrival (DOA) sensing, due to its ability of significantly reducing the hardware cost…
Co-learning-aided Multi-modal-deep-learning Framework of Passive DOA Estimators for a Heterogeneous Hybrid Massive MIMO Receiver
Jiatong Bai, Feng Shu, Qinghe Zheng +5
Due to its excellent performance in rate and resolution, fully-digital (FD) massive multiple-input multiple-output (MIMO) antenna arrays has been widely applied in data transmissio…
Two Power Allocation and Beamforming Strategies for Active IRS-aided Wireless Network via Machine Learning
Qiankun Cheng, Jiatong Bai, Baihua Shi +2
This paper models an active intelligent reflecting surface (IRS) -assisted wireless communication network, which has the ability to adjust power between BS and IRS. We aim to maxim…
Machine Learning-based Near-field Emitter Location Sensing via Grouped Hybrid Analog and Digital XL-MIMO Receive Array
Jiatong Bai, Yifan Li, Feng Shu +5
As a green MIMO structure, the partially-connected hybrid analog and digital (PC-HAD) structure has been widely used in the far-field (FF) scenario for it can significantly reduce…
A New Heterogeneous Hybrid Massive MIMO Receiver with An Intrinsic Ability of Removing Phase Ambiguity of DOA Estimation via Machine Learning
Feng Shu, Baihua Shi, Yiwen Chen +5
Massive multiple input multiple output (MIMO) antenna arrays eventuate a huge amount of circuit costs and computational complexity. To satisfy the needs of high precision and low c…