Name
Title | ||
|---|---|---|
Active User Detection and Channel Estimation for Massive Machine-Type Communication: Deep Learning Approach |
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/JIOT.2021.3132329 | IEEE Internet of Things Journal |
Keywords | DocType | Volume |
Active user detection (AUD),channel estimation (CE),deep learning (DL),grant free (GF),massive machine-type communication (mMTC),nonorthogonal multiple access (NOMA) | Journal | 9 |
Issue | Citations | PageRank |
14 | 1 | 0.38 |
References | Authors | |
22 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongjun Ahn | 1 | 1 | 0.38 |
| Wonjun Kim | 2 | 301 | 26.50 |
| Byonghyo Shim | 3 | 937 | 88.51 |