Noncoherent Massive Random Access for Inhomogeneous Networks: From Message Passing to Deep Learning-Reference-Cited by-同舟云学术

Noncoherent Massive Random Access for Inhomogeneous Networks: From Message Passing to Deep Learning

Author:

Huang Jianhao¹^ORCID,Zhang Han²^ORCID,Huang Chuan¹^ORCID,Yang Lian³,Zhang Wei⁴^ORCID

Affiliation:

1. Future Network of Intelligence Institute (FNii) and the School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China

2. Institute for Communication Systems, University of Surrey, Surrey, U.K.

3. Information and Communication Engineering College, University of Electronic Science and Technology of China, Sichuan, China

4. School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Special Project for Research and Development in Key areas of Guangdong Province

Shenzhen Outstanding Talents Training Fund

Guangdong Research Project

Basic Research Project of Hetao Shenzhen-HK Science and Technology Cooperation Zone

Shenzhen Science and Innovation Fund

Australian Research Council’s Project Funding Scheme

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Link

Reference53 articles.

3. Model-based machine learning for communications;shlezinger;arXiv 2101 04726,2021

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