T. Sivalingam, S. Ali, N. Huda Mahmood, N. Rajatheva and M. Latva-Aho, "Deep Neural Network-Based Blind Multiple User Detection for Grant-free Multi-User Shared Access," 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2021, pp. 1-7, doi: 10.1109/PIMRC50174.2021.9569446
Deep neural network-based blind multiple user detection for grant-free multi-user shared access
|Author:||Sivalingam, Thushan1; Ali, Samad1; Mahmood, Nurul Huda1;|
1Centre for Wireless Communications, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022020417648
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2022-02-04
Multi-user shared access (MUSA) is introduced as advanced code domain non-orthogonal complex spreading sequences to support a massive number of machine-type communications (MTC) devices. In this paper, we propose a novel deep neural network (DNN)-based multiple user detection (MUD) for grant-free MUSA systems. The DNN-based MUD model determines the structure of the sensing matrix, randomly distributed noise, and inter-device interference during the training phase of the model by several hidden nodes, neuron activation units, and a fit loss function. The thoroughly learned DNN model is capable of distinguishing the active devices of the received signal without any a priori knowledge of the device sparsity level and the channel state information. Our numerical evaluation shows that with a higher percentage of active devices, the DNN-MUD achieves a significantly increased probability of detection compared to the conventional approaches.
IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops
|Pages:||1 - 7|
32nd IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2021, 13-16 Sept. 2021, Helsinki, Finland
IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported by the Academy of Finland 6Genesis Flagship (grant no. 318927).
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
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