Performance analysis of underlay two-way relay cooperation in cognitive radio networks with energy harvesting

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Non-Orthogonal Multiple Access for 5G: Design and Performance Enhancement

PhDSpectrum scarcity is one of the most important challenges in wireless communications networks due to the sky-rocketing growth of multimedia applications. As the latest member of the multiple access family, non-orthogonal multiple access (NOMA) has been recently proposed for 3GPP Long Term Evolution (LTE) and envisioned to be a key component of the 5th generation (5G) mobile networks for its potential ability on spectrum enhancement. The feature of NOMA is to serve multiple users at the same time/frequency/code, but with di erent power levels, which yields a signi cant spectral e ciency gain over conventional orthogonal multiple access (OMA). This thesis provides a systematic treatment of this newly emerging technology, from the basic principles of NOMA, to its combination with simultaneously information and wireless power transfer (SWIPT) technology, to apply in cognitive radio (CR) networks and Heterogeneous networks (HetNets), as well as enhancing the physical layer security and addressing the fairness issue. First, this thesis examines the application of SWIPT to NOMA networks with spatially randomly located users. A new cooperative SWIPT NOMA protocol is proposed, in which near NOMA users that are close to the source act as energy harvesting relays in the aid of far NOMA users. Three user selection schemes are proposed to investigate the e ect of locations on the performance. Besides the closed-form expressions in terms of outage probability and throughput, the diversity gain of the considered networks is determined. Second, when considering NOMA in CR networks, stochastic geometry tools are used to evaluate the outage performance of the considered network. New closed-form expressions are derived for the outage probability. Diversity order of NOMA users has been analyzed based on the derived outage probability, which reveals important design insights regarding the interplay between two power constraints scenarios. Third, a new promising transmission framework is proposed, in which massive multipleinput multiple-output (MIMO) is employed in macro cells and NOMA is adopted in small cells. For maximizing the biased average received power at mobile users, a massive MIMO and NOMA based user association scheme is developed. Analytical expressions for the spectrum e ciency of each tier are derived using stochastic geometry. It is con rmed that NOMA is capable of enhancing the spectrum e ciency of the network compared to the OMA based HetNets. Fourth, this thesis investigates the physical layer security of NOMA in large-scale networks with invoking stochastic geometry. Both single-antenna and multiple-antenna aided transmission scenarios are considered, where the base station (BS) communicates with randomly distributed NOMA users. In addition to the derived exact analytical expressions for each scenario, some important insights such as secrecy diversity order and large antenna array property are obtained by carrying the asymptotic analysis. Fifth and last, the fundamental issues of fairness surrounding the joint power allocation and dynamic user clustering are addressed in MIMO-NOMA systems in this thesis. A two-step optimization approach is proposed to solve the formulated problem. Three e cient suboptimal algorithms are proposed to reduce the computational complexity. To further improve the performance of the worst user in each cluster, power allocation coe cients are optimized by using bi-section search. Important insights are concluded from the generated simulate results

Nonorthogonal Multiple Access for 5G and Beyond

This work was supported in part by the U.K. Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/N029720/1 and Grant EP/N029720/2. The work of L. Hanzo was supported by the ERC Advanced Fellow Grant Beam-me-up

Optimizing resource allocation in eh-enabled internet of things

Internet of Things (IoT) aims to bridge everyday physical objects via the Internet. Traditional energy-constrained wireless devices are powered by fixed energy sources like batteries, but they may require frequent battery replacements or recharging. Wireless Energy Harvesting (EH), as a promising solution, can potentially eliminate the need of recharging or replacing the batteries. Unlike other types of green energy sources, wireless EH does not depend on nature and is thus a reliable source of energy for charging devices. Meanwhile, the rapid growth of IoT devices and wireless applications is likely to demand for more operating frequency bands. Although the frequency spectrum is currently scarce, owing to inefficient conventional regulatory policies, a considerable amount of the radio spectrum is greatly underutilized. Cognitive radio (CR) can be exploited to mitigate the spectrum scarcity problem of IoT applications by leveraging the spectrum holes. Therefore, transforming the IoT network into a cognitive based IoT network is essential to utilizing the available spectrum opportunistically. To address the two aforementioned issues, a novel model is proposed to leverage wireless EH and CR for IoT. In particular, the sum rate of users is maximized for a CR-based IoT network enabled with wireless EH. Users operate in a time switching fashion, and each time slot is partitioned into three non-overlapping parts devoted for EH, spectrum sensing and data transmission. There is a trade-off among the lengths of these three operations and thus the time slot structure is to be optimized. The general problem of joint resource allocation and EH optimization is formulated as a mixed integer nonlinear programming task which is NP-hard and intractable. Therefore, a sub-channel allocation scheme is first proposed to approximately satisfy users rate requirements and remove the integer constraints. In the second step, the general optimization problem is reduced to a convex optimization task. Another optimization framework is also designed to capture a fundamental tradeoff between energy efficiency (EE) and spectral efficiency for an EH-enabled IoT network. In particular, an EE maximization problem is formulated by taking into consideration of user buffer occupancy, data rate fairness, energy causality constraints and interference constraints. Then, a low complexity heuristic algorithm is proposed to solve the resource allocation and EE optimization problem. The proposed algorithm is shown to be capable of achieving a near optimal solution with polynomial complexity. To support Machine Type Communications (MTC) in next generation mobile networks, NarrowBand-IoT (NB-IoT) has emerged as a promising solution to provide extended coverage and low energy consumption for low cost MTC devices. However, the existing orthogonal multiple access scheme in NB-IoT cannot provide connectivity for a massive number of MTC devices. In parallel with the development of NB-IoT, Non-Orthogonal Multiple Access (NOMA), introduced for the fifth generation wireless networks, is deemed to significantly improve the network capacity by providing massive connectivity through sharing the same spectral resources. To leverage NOMA in the context of NB-IoT, a power domain NOMA scheme is proposed with user clustering for an NB-IoT system. In particular, the MTC devices are assigned to different ranks within the NOMA clusters where they transmit over the same frequency resources. Then, an optimization problem is formulated to maximize the total throughput of the network by optimizing the resource allocation of MTC devices and NOMA clustering while satisfying the transmission power and quality of service requirements. Furthermore, an efficient heuristic algorithm is designed to solve the proposed optimization problem by jointly optimizing NOMA clustering and resource allocation of MTC devices