Deep Neural Network-Based Blind Multiple User Detection for Grant-free Multi-User Shared Access
| Autor: | Matti Latva-aho, Samad Ali, Thushan Sivalingam, Nurul Huda Mahmood, Nandana Rajatheva |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Artificial neural network Computer science business.industry Computer Science - Information Theory Deep learning multi user detection Information Theory (cs.IT) Real-time computing Complex spreading deep learning MUSA Multi-user massive machine-type communication Interference (communication) Channel state information Code (cryptography) FOS: Electrical engineering electronic engineering information engineering A priori and a posteriori Noise (video) Artificial intelligence Electrical Engineering and Systems Science - Signal Processing grant-free business |
| Zdroj: | PIMRC |
| DOI: | 10.48550/arxiv.2106.11204 |
| Popis: | 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. Comment: Accepted for 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)-Workshop |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|