452 citations · 530 across the 11 of their papers we have counts for
11 papers
Edge Learning Based Collaborative Automatic Modulation Classification for Hierarchical Cognitive Radio Networks
Peihao Dong, Chaowei He, Shen Gao +2
In hierarchical cognitive radio networks, edge or cloud servers utilize the data collected by edge devices for modulation classification, which, however, is faced with problems of…
Lightweight Deep Learning Based Channel Estimation for Extremely Large-Scale Massive MIMO Systems
Shen Gao, Peihao Dong, Zhiwen Pan +1
Extremely large-scale massive multiple-input multiple-output (XL-MIMO) systems introduce the much higher channel dimensionality and incur the additional near-field propagation effe…
Joint Resource Allocation and Trajectory Design for Resilient Multi-UAV Communication Networks
Linghui Ge, Xiao Liang, Hua Zhang +3
In contrast to terrestrial wireless networks, dynamic Unmanned Aerial Vehicle (UAV) networks are susceptible to unexpected link failures arising from UAV breakdowns or the depletio…
Edge Semantic Cognitive Intelligence for 6G Networks: Novel Theoretical Models, Enabling Framework, and Typical Applications
Peihao Dong, Qihui Wu, Xiaofei Zhang +1
Edge intelligence is anticipated to underlay the pathway to connected intelligence for 6G networks, but the organic confluence of edge computing and artificial intelligence still n…
Deep Multi-Stage CSI Acquisition for Reconfigurable Intelligent Surface Aided MIMO Systems
Shen Gao, Peihao Dong, Zhiwen Pan +1
This article aims to reduce huge pilot overhead when estimating the reconfigurable intelligent surface (RIS) relayed wireless channel. Motivated by the compelling grasp of deep lea…
Framework on Deep Learning Based Joint Hybrid Processing for mmWave Massive MIMO Systems
Peihao Dong, Hua Zhang, Geoffrey Ye Li
For millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, hybrid processing architecture is essential to significantly reduce the complexity and cost but…
Acquisition of Channel State Information for mmWave Massive MIMO: Traditional and Machine Learning-based Approaches
Chenhao Qi, Peihao Dong, Wenyan Ma +3
The accuracy of channel state information (CSI) acquisition directly affects the performance of millimeter wave (mmWave) communications. In this article, we provide an overview on…
Spatially Correlated Massive MIMO Relay Systems with Low-Resolution ADCs
Peihao Dong, Hua Zhang, Qihui Wu +1
In this paper, we investigate the massive MIMO relay system, where the relay station (RS) forwards the signals from multiple remote users to the base station (BS). Large-scale ante…
Reinforcement Learning Based Cooperative Coded Caching under Dynamic Popularities in Ultra-Dense Networks
Shen Gao, Peihao Dong, Zhiwen Pan +1
For ultra-dense networks with wireless backhaul, caching strategy at small base stations (SBSs), usually with limited storage, is critical to meet massive high data rate requests.…
Deep Learning based Channel Estimation for Massive MIMO with Mixed-Resolution ADCs
Shen Gao, Peihao Dong, Zhiwen Pan +1
In this article, deep learning is applied to estimate the uplink channels for mixed analog-to-digital converters (ADCs) massive multiple-input multiple-output (MIMO) systems, where…
Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems
Peihao Dong, Hua Zhang, Geoffrey Ye Li +2
For millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, hybrid processing architecture is usually used to reduce the complexity and cost, which poses a…