Deep Learning for Synchronization and Channel Estimation in NB-IoT Random Access Channel

Mads H. Jespersen, Milutin Pajovic, Toshiaki Koike-Akino, Ye Wang, Petar Popovski, Philip V. Orlik

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review


The central challenge in supporting massive IoT connectivity is the uncoordinated, random access by sporadically active devices. The random access protocol and activity detection have been widely studied, while the auxiliary procedures, such as synchronization, channel estimation and equalization, have received much less attention. However, once the protocol is fixed, the access performance can only be improved by a more effective receiver, through more accurate execution of the auxiliary procedures. This motivates the pursuit of joint synchronization and channel estimation, rather than the traditional approach of handling them separately. The prohibitive complexity of the conventional analytical solutions leads us to employ the tools of deep learning in this paper. Specifically, the proposed method is applied to the random access protocol of Narrowband IoT (NB-IoT), preserving its standard preamble structure. We obtain excellent performance in estimating Time-of-Arrival (ToA), Carrier-Frequency Offset (CFO), channel gain and collision multiplicity from a received mixture of transmissions. The proposed estimator achieves a ToA Root-Mean-Square Error (RMSE) of 0.99 us and a CFO RMSE of 1.61 Hz at 10 dB Signal-to-Noise Ratio (SNR), whereas a conventional estimator using two cascaded stages have RMSEs of 15.85 us and 8.05 Hz, respectively.

Original languageEnglish
Title of host publication2019 IEEE Global Communications Conference (GLOBECOM)
Publication date27 Feb 2020
ISBN (Print)978-1-7281-0963-3
ISBN (Electronic)978-1-7281-0962-6
Publication statusPublished - 27 Feb 2020
EventIEEE Global Communications Conference (GLOBECOM) 2019 - Waikoloa, Hawaii, Waikoloa, United States
Duration: 9 Dec 201913 Dec 2019


ConferenceIEEE Global Communications Conference (GLOBECOM) 2019
LocationWaikoloa, Hawaii
CountryUnited States
Internet address
SeriesIEEE Global Communications Conference (GLOBECOM)


Cite this

Jespersen, M. H., Pajovic, M., Koike-Akino, T., Wang, Y., Popovski, P., & V. Orlik, P. (2020). Deep Learning for Synchronization and Channel Estimation in NB-IoT Random Access Channel. In 2019 IEEE Global Communications Conference (GLOBECOM) IEEE. IEEE Global Communications Conference (GLOBECOM)