Massive Machine Type Communication using Non-Orthogonal Multiple Access with Convolutional Neural Network Approach

Abstract

: The 5G cellular network supports massive Machine Type Communication (mMTC) for Wireless Sensor Network (WSN) application. In this paper, High Altitude Platforms (HAPs) is used as a replacement for Base Station (BS). So that the cluster head (CH) from every cluster will send information owned to the HAPs by using the Power Domain Non-Orthogonal Multiple Access (PD NOMA) as a multiple access technique. PD NOMA uses the Successive Interference Cancellation (SIC) technique on the receiver side. SIC process is proven effective for detecting PD NOMA signal by sorting the received signal strength and then decoding it. However, error from the prioritized signal that has high decoding has a tremendous impact on the prioritized signal that has a way lower decoding, and this error can then further spread with the SIC process. In this paper, we propose a Convolutional Neural Network (CNN) approach to decode information from multiple CH without performing traditional communication signal processing. The simulation is already done by the Rician channel with 11 CH that is connected to the HAP. From the series of simulations that have been done, we can see that the CNN used to replace the conventional SIC on the uplink PD NOMA can detect NOMA signals without the use of conventional signal processing. The CH node nearest to the HAP requires a lower SNR than the CH node farthest from the HAP to achieve BER = 10-4 in both conventional uplink PD NOMA and uplink PD NOMA with CNN. Uplink PD NOMA with CNN has a lower complexity than conventional uplink PD NOMA.