Abstract
As a means to support the access of massive machine-type communication devices, grant-free access and nonorthogonal multiple access (NOMA) have received a lot of attention recently. In the grant-free environment, each device transmits information without scheduling. Hence, the device identification process called active user detection (AUD) is indispensable at the base station. After the AUD process, the channel estimation for active devices is performed in the base station before detecting the data. These processes are challenging problems in the NOMA-based systems since it is difficult to detect the active devices and estimate the channel of those devices from the superimposed received signal. In this paper, we propose a deep neural network (DNN)-based joint AUD and CE scheme for the practical mMTC systems. Specifically, the proposed scheme consists of long short term memory (LSTM)-based AUD (L-AUD) and DNN-based CE (D-CE). In L-AUD, by feeding the training data in the designed network, the proposed LSTM network is trained to exploit the extracted features when mapping the received NOMA signal to the indices of active devices. After the AUD process, by using the deeply stacked hidden layers, D-CE extracts the channel features and the codebook features of the active devices to map the received NOMA signal to the corresponding channel. As a result, the trained DNN can jointly handle the whole AUD and CE processes, achieving an accurate detection of the active devices and the small channel estimation error.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.