T. Sivalingam, S. Ali, N. H. Mahmood, N. Rajatheva and M. Latva-Aho, "Deep Learning-Based Active User Detection for Grant-free SCMA Systems," 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2021, pp. 635-641, doi: 10.1109/PIMRC50174.2021.9569538
Deep learning-based active user detection for grant-free SCMA systems
|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-fe2022020417653
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2022-02-04
Grant-free random access and uplink non- orthogonal multiple access (NOMA) have been introduced to reduce transmission latency and signaling overhead in massive machine-type communication (mMTC). In this paper, we propose two novel group-based deep neural network active user detection (AUD) schemes for the grant-free sparse code multiple access (SCMA) system in mMTC uplink framework. The proposed AUD schemes learn the nonlinear mapping, i.e., multi-dimensional codebook structure and the channel characteristic. This is accomplished through the received signal which incorporates the sparse structure of device activity with the training dataset. Moreover, the offline pre-trained model is able to detect the active devices without any channel state information and prior knowledge of the device sparsity level. Simulation results show that with several active devices, the proposed schemes obtain more than twice the probability of detection compared to the conventional AUD schemes over the signal to noise ratio range of interest.
IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops
|Pages:||635 - 641|
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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