most citedLearning Centric Power Allocation for Edge Intelligence

1 citations · 2 across the 5 of their papers we have counts for

collaborators

6 papers

cs.IT2021

Wireless Sensing With Deep Spectrogram Network and Primitive Based Autoregressive Hybrid Channel Model

Guoliang Li, Shuai Wang, Jie Li +3

Human motion recognition (HMR) based on wireless sensing is a low-cost technique for scene understanding. Current HMR systems adopt support vector machines (SVMs) and convolutional…

cs.IT2021

On Secure Degrees of Freedom of the MIMO Interference Channel with Local Output Feedback

Tong Zhang, Yinfei Xu, Shuai Wang +2

This paper studies the problem of sum-secure degrees of freedom (SDoF) of the (M,M,N,N) multiple-input multiple-output (MIMO) interference channel with local output feedback, so as…

cs.IT2021

Reconfigurable Intelligent Surface Assisted Edge Machine Learning

Shanfeng Huang, Shuai Wang, Rui Wang +2

The ever-growing popularity and rapid improving of artificial intelligence (AI) have raised rethinking on the evolution of wireless networks. Mobile edge computing (MEC) provides a…

cs.IT20201 cited

Deep Reinforcement Learning Based Dynamic Power and Beamforming Design for Time-Varying Wireless Downlink Interference Channel

Mengfan Liu, Rui Wang

With the high development of wireless communication techniques, it is widely used in various fields for convenient and efficient data transmission. Different from commonly used ass…

cs.IT2020

Learning Centric Wireless Resource Allocation for Edge Computing: Algorithm and Experiment

Liangkai Zhou, Yuncong Hong, Shuai Wang +4

Edge intelligence is an emerging network architecture that integrates sensing, communication, computing components, and supports various machine learning applications, where a fund…

cs.IT20201 cited

Learning Centric Power Allocation for Edge Intelligence

Shuai Wang, Rui Wang, Qi Hao +2

While machine-type communication (MTC) devices generate massive data, they often cannot process this data due to limited energy and computation power. To this end, edge intelligenc…