Exploring and Enhancing the Spectral and Energy-Efficiency of Non-Orthogonal Multiple Access in Next Generation IoT Networks

Abstract

The proliferation of technologies like Internet of Things (IoT) and Industrial IoT (IIoT) has led to rapid growth in the number of connected devices and the volume of data associated with IoT applications. It is expected that more than 125 billion IoT devices will be connected to the Internet by 2030. With the plethora of wireless IoT devices, we are moving towards the connected world which is the guiding principle for the IoT. The next generation of IoT network should be capable of interconnecting heterogeneous IoT sensor or devices for effective Device-to-Device (D2D), Machine-to-Machine (M2M) communications as well as facilitating various IoT services and applications. Therefore, the next generation of IoT networks is expected to meet the capacity demand of such a network of billions of IoT devices. The current underlying wireless network is based on Orthogonal Multiple Access (OMA) by assigning orthogonal resources to multiple users. OMA cannot serve multiple IoT devices simultaneously and hence cannot maximize the resource efficiency. Therefore, OMA is considered spectrally inefficient for the design and optimization of the next-generation wireless systems. In this context, to provide massive connectivity requirements of IoT sensor and devices and to ameliorate their capacity demands, Non-Orthogonal Multiple Access (NOMA) has been considered as a potential candidate for the Fifth-Generation (5G) and the next-generation networks. Fundamentally, in NOMA, multiple signals or messages for users with distinct channel conditions are multiplexed in power domain. Specifically, multiple signals can overlap in same time, frequency and code in order to achieve a balanced trade-off between system throughput and user fairness. Moreover, in addition to improving the Spectral Efficiency (SE), which is the main motivation of NOMA, another key objective of the next-generation wireless IoT networks is to maximize the energy-efficiency so as to support massive IoT device communication and data transmission. To this end, Simultaneous Wireless Information and Power Transfer (SWIPT) has been contemplated as an energy efficient viable solution to self-sustainable communication in IoT networks.

In this dissertation, different from the state-of-the-art methods and architectures, we investigate and propose several spectral and energy-efficient NOMA architectures for next-generation IoT networks. This dissertation first proposed an architecture to demonstrate how bi-directional communications can be achieved in a NOMA-SWIPT enabled IoT relay networks. Then pairing issues in NOMA are discussed, since efficient user pairing between multiple users is needed to enhance the capacity of NOMA systems. Thus, a new adaptive user pairing strategy that enhances the capacities of a cell for NOMA systems is proposed and thoroughly examined. Then this dissertation sheds lights on the issue of distributed localization in the IoT, since accurate and precise localization can help the IoT sensor nodes for efficient user pairing and energy harvesting. Therefore, we propose Social Learning based Particle Swarm Optimization (SL- PSO), which is a new distributed localization algorithm inspired from nature. Following this, several architectures for cooperative NOMA-SWIPT are proposed where outage probability, throughput, sum-throughput, Ergodic capacity and Ergodic sum capacity is investigated for a delay limited and delay tolerant transmission mode. Their analytical derivations are mathematically derived and corroborated with the simulation results under both perfect Signal-to-Interference Cancellation (SIC) and imperfect SIC scenarios. Moreover, NOMA is based on the principle of SIC, which is known to be very fragile to interference, as the decoding failure propagates in the SIC chain to weaker users. Therefore, we propose and investigate a simple and energy-efficient distributed power control in downlink NOMA using Reinforcement Learning (RL) based Game Theoretic approach. Finally, this dissertation proposes and investigates different models by consolidating direct links in a way that significantly enhances the performance of the cooperative NOMA-SWIPT systems. We believe that our works proposed in this dissertation will be useful for designing spectral and energy-efficient NOMA in next-generation IoT networks. We further believe that the study and results presented in this dissertation might be potentially useful to network operators, researchers and scientists in the wireless networking community from both academia and industry who want to assess the characteristics of NOMA to design next-generation IoT networks.