Random-Access NOMA in URLL Energy-Harvesting IoT Networks with Short Packet and Diversity Transmissions

Abstract

Non-orthogonal multiple-access (NOMA) has recently been proposed to improve throughput and spectrum-efficiency of 5G cellular networks and beyond. It is also a key enabler for ultra-reliable and low-latency (URLL) communications. Moreover, the Internet-of-Things (IoT) paradigm has emerged to practically provide massive connectivity for smart devices and systems, which entails spectrum- and energy-efficient transmission schemes. To this aim, NOMA and energy-harvesting (EH) solutions have been put forth to address such demands, making the combination of such technologies inevitable in NOMA-based URLL-EH-IoT networks. On the other hand, random-access (RA) techniques are the key solution to enable massive URLL-EH-IoT networks, since they reduce signaling overhead, packet latency, and energy consumption when massive numbers of clustered IoT devices with sporadic traffic behavior are considered. In this paper, uplink RA-NOMA in URLL-EH-IoT networks with short packet and diversity transmissions is studied and analyzed. Network metrics - such as average packet latency, reliability, and GoodPut - are derived for the RA-NOMA scenario and compared with its RA-OMA counterpart to explore the advantages of RA-NOMA over RA-OMA. Moreover, the analytical derivations have been validated and shown to coincide with the simulation results. Furthermore, the effect of the transmission diversity and number of data bits per blocklength on the different network metrics are extensively evaluated. Lastly, the analytical derivations are utilized to find the optimum values of the IoT nodes' transmit power, number of packet replicas, and number of transmitted data bits per blocklength, such that the maximum sum-GoodPut of a RA-NOMA IoT cluster is achieved, subject to URLL constraints.