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20162021
most citedFederated Dynamic Spectrum Access

9 citations · 17 across the 8 of their papers we have counts for

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7 papers · 1 filter

eess.SP2021

Learning to Equalize OTFS

Zhou Zhou, Lingjia Liu, Jiarui Xu +1

Orthogonal Time Frequency Space (OTFS) is a novel framework that processes modulation symbols via a time-independent channel characterized by the delay-Doppler domain. The conventi…

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.SP2021

Making Intelligent Reflecting Surfaces More Intelligent: A Roadmap Through Reservoir Computing

Zhou Zhou, Kangjun Bai, Nima Mohammadi +2

This article introduces a neural network-based signal processing framework for intelligent reflecting surface (IRS) aided wireless communications systems. By modeling radio-frequen…

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…

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…

eess.SP2019

CogRF: A New Frontier for Machine Learning and Artificial Intelligence for 6G RF Systems

Tarun Cousik, Rubayet Shafin, Zhou Zhou +3

The concept of CogRF, a novel tunable radio frequency (RF) frontend that uses artificial intelligence (AI) to meet mission requirements for beyond 5G and 6G systems, is introduced.…