Doubling the Number of Connected Devices in Narrow-band Internet of Things while Maintaining System Performance: An STC-based Approach

Abstract

Narrow-band Internet of Things (NB-IoT) is a low-power wide-area network (LPWAN) method that was first launched by the 3rd generation partnership project (3GPP) Rel-13 with the purpose of enabling low-cost, low-power, and wide-area cellular connections for the Internet of Things (IoT). As the demand for over-the-air services grows and with the number of linked wireless devices reaching 100 billion, wireless spectrum is becoming scarce, necessitating creative techniques that can increase the number of connected devices within a restricted spectral resource to satisfy service needs. Consequently, it is vital that academics develop efficient solutions to fulfill the quality of service (QoS) criteria of the NB-IoT in the context of 5th generation (5G). This study paves the way for 5G networks and beyond to have increased capacity and data rates for NB-IoT. Whereas, this article suggests a method for increasing the number of connected devices by using a technique known as symbol time compression (STC). The suggested method reduces the occupied bandwidth of each device without increasing complexity, losing data throughput, or affecting bit error rate (BER) performance. The STC approach is proposed in the literature to work with conventional orthogonal frequency-division multiplexing (OFDM) to reduce bandwidth usage by 50% and enhance the peak-to-average power ratio (PAPR). Specifically, an STC-based technique is suggested that exploits the available bandwidth to increase the number of linked devices by double while keeping the complexity and performance of the system. Furthermore, the µ-law companding technique is leveraged to reduce the PAPR of the transmitted signals. The obtained simulation results indicate that the suggested method using the µ-law companding technique doubles the amount of transferred data and lowers the PAPR by 3.22 dB while keeping the same complexity and BER.