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Title: ML-Based Feedback-Free Adaptive MCS Selection for Massive Multi-User MIMO
Abstract—As wireless communication systems strive to improve spectral efficiency, there has been a growing interest in employing machine learning (ML)-based approaches for adaptive modulation and coding scheme (MCS) selection. In this paper, we introduce a new adaptive MCS selection framework for massive MIMO systems that operates without any feedback from users by solely relying on instantaneous uplink channel estimates. Our proposed method can effectively operate in multi-user scenarios where user feedback imposes excessive delay and bandwidth overhead. To learn the mapping between the user channel matrices and the optimal MCS level of each user, we develop a Convolutional Neural Network (CNN)-Long Short-Term Memory Network (LSTM)-based model and compare the performance with the state-of-the-art methods. Finally, we validate the effectiveness of our algorithm by evaluating it experimentally using real-world datasets collected from the RENEW massive MIMO platform. Index Terms—Adaptive MCS Selection, Machine Learning, Convolutional Neural Network, Long Short-Term Memory Network, Channel State Information, Feedback Delay.  more » « less
Award ID(s):
2120363
PAR ID:
10542298
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
Institute of Electrical and Electronics Engineers
Date Published:
Journal Name:
Conference record
ISSN:
2576-2303
ISBN:
979-8-3503-2575-1
Subject(s) / Keyword(s):
Wireless communication, Adaptation models, Adaptive systems, Spectral efficiency, Modulation, Massive MIMO, Encoding
Format(s):
Medium: X
Location:
Pacific Grove, CA, USA
Sponsoring Org:
National Science Foundation
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