S. Thushan, S. Ali, N. H. Mahmood, N. Rajatheva and M. Latva-Aho, "Deep Learning-Based Blind Multiple User Detection for Grant-Free SCMA and MUSA Systems," in IEEE Transactions on Machine Learning in Communications and Networking, vol. 1, pp. 61-77, 2023, doi: 10.1109/TMLCN.2023.3283350
Deep learning-based blind multiple user detection for grant-free SCMA and MUSA systems
|Author:||Thushan, Sivalingam1; Ali, Samad1; Mahmood, Nurul Huda1;|
1Centre for Wireless Communications (CWC), University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20231018140542
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-10-18
Massive machine-type communications (mMTC) in 6G requires supporting a massive number of devices with limited resources, posing challenges in efficient random access. Grant-free random access and uplink non-orthogonal multiple access (NOMA) are introduced to increase the overload factor and reduce transmission latency with signaling overhead in mMTC. Sparse code multiple access (SCMA) and Multi-user shared access (MUSA) are introduced as advanced code domain NOMA schemes. In grant-free NOMA, machine-type devices (MTD) transmit information to the base station (BS) without a grant, creating a challenging task for the BS to identify the active MTD among all potential active devices. In this paper, a novel pre-activated residual neural network-based multi-user detection (MUD) scheme for the grant-free SCMA and MUSA system in an mMTC uplink framework is proposed to jointly identify the number of active MTDs and their respective messages in the received signal’s sparsity and the active MTDs in the absence of channel state information. A novel residual unit designed to learn the properties of multi-dimensional SCMA codebooks, MUSA spreading sequences, and corresponding combinations of active devices with diverse settings. The proposed scheme learns from the labeled dataset of the received signal and identifies the active MTDs from the received signal without any prior knowledge of the device sparsity level. A calibration curve is evaluated to verify the model’s calibration. The application of the proposed MUD scheme is investigated in an indoor factory setting using four different mmWave channel models. Numerical results show that when the number of active MTDs in the system is large, the proposed MUD has a significantly higher probability of detection compared to existing approaches over the signal-to-noise ratio range of interest.
IEEE transactions on machine learning in communications and networking
|Pages:||61 - 77|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the Academy of Finland, 6G Flagship Program, under Grant 346208; and in part by the Hexa-X-II Project through the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe Research and Innovation Programme under Grant 101095759.
|EU Grant Number:||
(101095759) Hexa-X-II - A holistic flagship towards the 6G network platform and system, to inspire digital transformation, for the world to act together in meeting needs in society and ecosystems with novel 6G services
|Academy of Finland Grant Number:||
346208 (Academy of Finland Funding decision)
© The Author(s) 2023. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.