Grant-free random access protocols are among the enabling techniques for mMTC, where a large number of devices activate sporadically and transmit short packets, typically containing a preamble (or a pilot sequence), without any resource allocation from the BS. One of the critical tasks to be accomplished by the BS is thus the preamble-based detection of the transmitted packets. This letter proposes a DL-based solution for detecting preambles in an asynchronous grant-free random access uplink scenario, assuming multiple antennas at the BS. The DL-based approach outperforms the classical correlator-based approach.
Preamble Detection in Asynchronous Random Access Using Deep Learning / Khan M.U.; Testi E.; Paolini E.; Chiani M.. - In: IEEE WIRELESS COMMUNICATIONS LETTERS. - ISSN 2162-2337. - STAMPA. - 13:2(2023), pp. 10288135.279-10288135.283. [10.1109/LWC.2023.3325918]
Preamble Detection in Asynchronous Random Access Using Deep Learning
Khan M. U.;Testi E.;Paolini E.;Chiani M.
2023
Abstract
Grant-free random access protocols are among the enabling techniques for mMTC, where a large number of devices activate sporadically and transmit short packets, typically containing a preamble (or a pilot sequence), without any resource allocation from the BS. One of the critical tasks to be accomplished by the BS is thus the preamble-based detection of the transmitted packets. This letter proposes a DL-based solution for detecting preambles in an asynchronous grant-free random access uplink scenario, assuming multiple antennas at the BS. The DL-based approach outperforms the classical correlator-based approach.File | Dimensione | Formato | |
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