most citedFederated Dynamic Spectrum Access

9 citations · 24 across the 4 of their papers we have counts for

collaborators

5 papers

eess.SP20219 cited

Federated Dynamic Spectrum Access

Yifei Song, Hao-Hsuan Chang, Zhou Zhou +2

Due to the growing volume of data traffic produced by the surge of Internet of Things (IoT) devices, the demand for radio spectrum resources is approaching their limitation defined…

eess.SP2020

Learning with Knowledge of Structure: A Neural Network-Based Approach for MIMO-OFDM Detection

Zhou Zhou, Shashank Jere, Lizhong Zheng +1

In this paper, we explore neural network-based strategies for performing symbol detection in a MIMO-OFDM system. Building on a reservoir computing (RC)-based approach towards symbo…

cs.LG20207 cited

Learning for Integer-Constrained Optimization through Neural Networks with Limited Training

Zhou Zhou, Shashank Jere, Lizhong Zheng +1

In this paper, we investigate a neural network-based learning approach towards solving an integer-constrained programming problem using very limited training. To be specific, we in…

eess.SP20208 cited

Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G

Shashank Jere, Qiang Fan, Bodong Shang +2

Owing to the large volume of sensed data from the enormous number of IoT devices in operation today, centralized machine learning algorithms operating on such data incur an unbeara…

eess.SP2020

RCNet: Incorporating Structural Information into Deep RNN for MIMO-OFDM Symbol Detection with Limited Training

Zhou Zhou, Lingjia Liu, Shashank Jere +3

In this paper, we investigate learning-based MIMO-OFDM symbol detection strategies focusing on a special recurrent neural network (RNN) -- reservoir computing (RC). We first introd…