Deep neural network-based blind multiple user detection for grant-free multi-user shared access
Sivalingam, Thushan; Ali, Samad; Mahmood, Nurul Huda; Rajatheva, Nandana; Latva-Aho, Matti (2021-10-22)
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
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe2022020417648
Tiivistelmä
Abstract
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.
Kokoelmat
- Avoin saatavuus [38865]