Doubling the Number of Connected Devices in Narrow-band Internet of Things while Maintaining System Performance: An STC-based Approach (2208.10112v1)

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 connection 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 in order 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) and beyond. This study paves the way for 5G networks and beyond to have increased capacity and data rate 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 compresses the occupied bandwidth of each device without increasing complexity, losing data throughput or bit error rate (BER) performance. The STC approach is proposed in the literature to work with the conventional orthogonal frequency division multiplexing (OFDM) to reduce bandwidth usage by 50% and improve the peak-to-average power ratio (PAPR). Specifically, An STC-based method is proposed that exploits the unused bandwidth to double the number of connected devices while keeping system performance and complexity. Furthermore, the {\mu}-law companding technique is leveraged to reduce the PAPR of the transmitted signals. The obtained simulation results reveal that the proposed approach using the {\mu}-law companding technique increases the transmitted data by twice and reduces the PAPR by 3.22 dB while maintaining the same complexity and BER.