Performance Modeling of Cognitive NOMA-aided IoT Networks with Energy Harvesting

Abstract

In an attempt to address rocketed connectivity and bandwidth demands in 5G wireless networks, Non-Orthogonal Multiple Access (NOMA) and Cognitive Radio (CR) concepts have been proposed. The former addresses increased connectivity requirements by allowing multiple users in the same NOMA group to utilise the same channel resources. The latter enhances spectrum efficiency by intelligently al- lowing spectrum sharing between primary and secondary networks, if secondary to primary network interference is properly managed. To prolong connectivity/ser- vice life-time of battery capacity constrained Internet of Things (IoT) devices, Energy Harvesting (EH) technique has been identified as the technology that can enable such devices to harvest energy from ambient sources present in the envi- ronment. This research work is motivated by the observed surge in adoption of IoT devices around the globe. The resulting adoption has brought about the need to investigate performance of different IoT system models and hence, understand potential applicability and optimization options for different services. The focus of this dissertation is to model and analyse the performance of an EH Cognitive Radio Non-Orthogonal Multiple Access (CR-NOMA) IoT network. To accomplish this, a simplified energy harvesting CR-NOMA IoT network is considered. The considered network consists of primary and secondary network components. The primary network contains Macro Base-Station (MBS) and Pri- mary Network users (PUs), while the secondary network is made up of Secondary Base-Station (SBS) and multiple CR-NOMA groups containing two Secondary Users (SUs) each. To analytically capture the stochastic nature of energy harvest- ing process and cater for residual energy from one transmission frame to the next, each SU’s energy level in the battery is discretized to represent the state of each SU during each transmission frame; with this, we derive a complete Markovian model for the considered system model using queueing theory and Markovian analysis. Two Markovian models are developed for the considered system model, with one assuming that the SUs are harvesting energy from the SBS (one energy source) and the other, adopting an assumption that energy is harvested from both the SBS and MBS (two energy sources). The considered system performance is analysed in terms of up-link system outage probability and mean capacity. To provide detailed insights, closed-form analytical expressions for up-link outage probability and mean capacity for each user in the CR-NOMA group are derived using the Markovian models as the ba- sis. Produced analytical results are confirmed through simulations using Matlab. Simulation results matched the analytical results, this confirmed the validity of the derived analytical expressions for SUs outage probability and mean capacity. ii Both performance metrics are studied and the impact of varying different network parameters on outage probability and mean capacity is investigated. For out- age probability, results are generated which demonstrate SUs outage performance as we vary Signal-to-Interference-plus-Noise Ratio (SINR), interference threshold, and battery power level. Similarly, mean capacity results are generated to illus- trate each SU mean capacity performance while varying their battery levels, this is done for different values of primary transmit power and interference threshold. Performance results observed as different parameters are varied for outage prob- ability and mean capacity align with the theoretical performance expected when those parameters are changed. The significance of this work lies in providing ana- lytical tools to assess the performance of the CR-NOMA IoT system with energy harvesting (EH). These tools enable easy computation of system performance in- dicators such as outage performance and mean capacity. Attempting the same assessment through simulation would be a cumbersome process.