Abstract:
The potential of Artificial Intelligence (AI) techniques, such as autoencoders, for customizing the wireless physical layer has been demonstrated in previous works. In th...View moreMetadata
Abstract:
The potential of Artificial Intelligence (AI) techniques, such as autoencoders, for customizing the wireless physical layer has been demonstrated in previous works. In the current paper, we move up the protocol stack and explore the frontiers of Machine Learning (ML) on the wireless Medium Access Control (MAC) layer. Unlike the Physical Layer (PHY), the MAC aggregates multiple independent features, which require a separate ML treatment. Considering this, this survey paper navigates recent research on AI-driven MAC functions such as resource allocation, random access, Adaptive Modulation and Coding (AMC), power control, protocol learning, Channel State Information (CSI) reporting, Hybrid Automatic Repeat Request (HARQ), and Multi-RAT Spectrum Sharing (MRSS).
Published in: 2024 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)
Date of Conference: 03-06 June 2024
Date Added to IEEE Xplore: 19 July 2024
ISBN Information: