Learning-Based One-Bit Maximum Likelihood Detection for Massive MIMO Systems: Dithering-Aided Adaptive Approach-Reference-Cited by-同舟云学术

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Learning-Based One-Bit Maximum Likelihood Detection for Massive MIMO Systems: Dithering-Aided Adaptive Approach

Published:2024-08 Issue:8 Volume:73 Page:11680-11693
ISSN:0018-9545
Container-title:IEEE Transactions on Vehicular Technology
language:
Short-container-title:IEEE Trans. Veh. Technol.

Author:

Cho Yunseong¹^ORCID,Choi Jinseok²^ORCID,Evans Brian L.³^ORCID

Affiliation:

1. Samsung Research America, Plano, TX, USA

2. Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea

3. 6G@UT Research Center, Wireless Networking and Communication Group (WNCG), Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA

Funder

NVIDIA and AT&T Labs

BK21 FOUR

KAIST EE

National Research Foundation of Korea

Korea Government, MSIT

MSIT, Korea

Information Technology Research Center

Institute for Information & Communications Technology Planning & Evaluation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/25/10637911/10480278.pdf?arnumber=10480278

Reference43 articles.

1. Robust Learning-Based ML Detection for Massive MIMO Systems with One-Bit Quantized Signals

2. Energy and Spectral Efficiency of Very Large Multiuser MIMO Systems

3. Massive MIMO for next generation wireless systems

4. An introduction to millimeter-wave mobile broadband systems

5. What Will 5G Be?

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