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Active User Detection and Channel Estimation for Massive Machine-Type Communication: Deep Learning Approach
Cited 12 time in
Web of Science
Cited 21 time in Scopus
- Authors
- Issue Date
- 2022-07
- Citation
- IEEE Internet of Things Journal, Vol.9 No.14, pp.11904-11917
- Abstract
- Recently, massive machine-type communications (mMTCs) have become one of key use cases for 5G. In order to support massive users transmitting small data packets at low rates, grant-free (GF) access and nonorthogonal multiple access (NOMA) have been suggested. Since each device transmits information without scheduling in the GF-NOMA systems, the device identification process, called active user detection (AUD), is required at the base station (BS). For the NOMA-based systems, the channel estimation (CE), an operation after the AUD, is a challenging task since multiple devices' transmit signals and channels are superimposed in the same wireless resources. In this article, we propose a deep learning (DL)-based AUD and CE in the GF-NOMA systems. In our work, DL figures out the direct mapping between the received NOMA signal and the indices of active devices and associated channels using the long short-term memory (LSTM). From numerical experiments, we show that the proposed scheme is effective in handling the AUD and CE in the mMTC environments.
- ISSN
- 2327-4662
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