Doctor of Philosophy / Department of Electrical and Computer Engineering / Balasubramaniam Natarajan / The number of wireless devices is predicted to skyrocket from about 5 billion in 2015 to 25 billion by 2020. Therefore, traffic volume demand is envisioned to explode in the very near future. The proposed fifth generation (5G) of mobile networks is expected to be a mixture of network components with different sizes, transmit powers, back-haul connections and radio access technologies. While there are many interesting problems within the 5G framework, we address the challenges of energy-related management in a heterogeneous 5G networks. Based on the 5G architecture, in this dissertation, we present some fundamental methodologies to analyze and improve the energy efficiency of 5G network components using mathematical tools from optimization, control theory and stochastic geometry. Specifically, the main contributions of this research include: • We design power-saving modes in small cells to maximize energy efficiency. We first derive performance metrics for heterogeneous cellular networks with sleep modes based on stochastic geometry. Then we quantify the energy efficiency and maximize it with quality-of-service constraint based on an analytical model. We also develop a simple sleep strategy to further improve the energy efficiency according to traffic conditions. • We conduct a techno-economic analysis of heterogeneous cellular networks powered by both on-grid electricity and renewable energy. We propose a scheme to minimize the electricity cost based on a real-time pricing model. • We provide a framework to uncover desirable system design parameters that offer the best gains in terms of ergodic capacity and average achievable throughput for device-to-device underlay cellular networks. We also suggest a two-phase scheme to optimize the ergodic capacity while minimizing the total power consumption. • We investigate the modeling and analysis of simultaneous information and energy transfer in Internet of things and evaluate both transmission outage probability and power outage probability. Then we try to balance the trade-off between the outage performances by careful design of the power splitting ratio. This research provides valuable insights related to the trade-offs between energy-conservation and system performance in 5G networks. Theoretical and simulation results help verify the performance of the proposed algorithms.

The deployment of heterogeneous networks (HetNets), in which low power nodes (LPNs) and high power nodes (HPNs) coexist, has become a promising solution for extending coverage and increasing capacity in wireless networks. Meanwhile, several advanced technologies such as massive multi-input multi-output (MIMO), cloud radio access networks (C-RAN) and device-to-device (D2D) communications have been proposed as competent candidates for supporting the next generation (5G) network. Since single technology cannot solely achieve the envisioned 5G requirements, the e ect of integrating multiple technologies in one system is worth to be investigated. In this thesis, a thoroughly theoretical analysis is conducted to evaluate the network performance in di erent scenarios, where two or more 5G techniques are employed. First, the downlink performance of massive MIMO enabled HetNets is fully evaluated. The exact and asymptotic expressions for the probability of a user being associated with a macro cell or a small cell are presented. The analytical expressions for the spectrum e ciency (SE) and energy e ciency (EE) in the K-tier network are also derived. The analysis reveals that the implementation of massive MIMO in the macro cell can considerably improve the network performance and decrease the demands for small cells in HetNets, which simpli es the network deployment. Then, the downlink performance of a massive MIMO enabled heterogeneous C-RAN is investigated. The exact expressions for the SE and EE of the remote radio heads (RRHs) tier and a tractable approximation approach for evaluating the SE and EE of the macrocell tier are obtained. Numerical results collaborate the analysis and prove that massive MIMO with dense deployment of RRHs can signi cantly enhance the performance of heterogeneous C-RAN theoretically. Next, the uplink performance of massive MIMO enabled HetNets is exploited with interference management via derived SE and EE expressions. The numerical results show that the uplink performance in the massive MIMO macrocells can be signi cantly improved through uplink power control in the small cells, while more uplink transmissions in the macrocells have mild adverse e ect on the uplink performance of the small cells. In addition, the SE and EE of the massive MIMO macrocells with heavier load can be improved by expanding the small cell range. Lastly, the uplink performance of the D2D underlaid massive MIMO network is investigated and a novel D2D power control scheme is proposed. The average uplink achievable SE and EE expressions for the cellular and D2D are derived and results demonstrate that the proposed power control can e ciently mitigate the interference from the D2D. Moreover, the D2D scale properties are obtained, which provide the su cient conditions for achieving the anticipated SE. The results demonstrate that there exists the optimal D2D density for maximizing the area SE of D2D tier. In addition, the achievable EE of a cellular user can be comparable to that of a D2D user. Stochastic geometry is applied to model all of the systems mentioned above. Monte Carlo simulations are also developed and conducted to validate the derived expressions and the theoretical analysis.

One important feature of the future communication network is that users in the network are required to experience a guaranteed high quality of service (QoS) due to the popularity of multimedia applications. This thesis studies QoS-aware radio resource management schemes in different OFDMA network scenarios. Motivated by the fact that in current 4G networks, the QoS provisioning is severely constrained by the availability of radio resources, especially the scarce spectrum as well as the unbalanced traffic distribution from cell to cell, a joint antenna and subcarrier management scheme is proposed to maximise user satisfaction with load balancing. Antenna pattern update mechanism is further investigated with moving users. Combining network densi fication with cloud computing technologies, cloud radio access network (C-RAN) has been proposed as the emerging 5G network architecture consisting of baseband unit (BBU) pool, remote radio heads (RRHs) and fronthaul links. With cloud based information sharing through the BBU pool, a joint resource block and power allocation scheme is proposed to maximise the number of satisfi ed users whose required QoS is achieved. In this scenario, users are served by high power nodes only. With spatial reuse of system bandwidth by network densi fication, users' QoS provisioning can be ensured but it introduces energy and operating effciency issue. Therefore two network energy optimisation schemes with QoS guarantee are further studied for C-RANs: an energy-effective network deployment scheme is designed for C-RAN based small cells; a joint RRH selection and user association scheme is investigated in heterogeneous C-RAN. Thorough theoretical analysis is conducted in the development of all proposed algorithms, and the effectiveness of all proposed algorithms is validated via comprehensive simulations.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Balasubramaniam Natarajan / The number of wireless devices is predicted to skyrocket from about 5 billion in 2015 to 25 billion by 2020. Therefore, traffic volume demand is envisioned to explode in the very near future. The proposed fifth generation (5G) of mobile networks is expected to be a mixture of network components with different sizes, transmit powers, back-haul connections and radio access technologies. While there are many interesting problems within the 5G framework, we address the challenges of energy-related management in a heterogeneous 5G networks. Based on the 5G architecture, in this dissertation, we present some fundamental methodologies to analyze and improve the energy efficiency of 5G network components using mathematical tools from optimization, control theory and stochastic geometry. Specifically, the main contributions of this research include: • We design power-saving modes in small cells to maximize energy efficiency. We first derive performance metrics for heterogeneous cellular networks with sleep modes based on stochastic geometry. Then we quantify the energy efficiency and maximize it with quality-of-service constraint based on an analytical model. We also develop a simple sleep strategy to further improve the energy efficiency according to traffic conditions. • We conduct a techno-economic analysis of heterogeneous cellular networks powered by both on-grid electricity and renewable energy. We propose a scheme to minimize the electricity cost based on a real-time pricing model. • We provide a framework to uncover desirable system design parameters that offer the best gains in terms of ergodic capacity and average achievable throughput for device-to-device underlay cellular networks. We also suggest a two-phase scheme to optimize the ergodic capacity while minimizing the total power consumption. • We investigate the modeling and analysis of simultaneous information and energy transfer in Internet of things and evaluate both transmission outage probability and power outage probability. Then we try to balance the trade-off between the outage performances by careful design of the power splitting ratio. This research provides valuable insights related to the trade-offs between energy-conservation and system performance in 5G networks. Theoretical and simulation results help verify the performance of the proposed algorithms.

As the complexity of modern cellular networks continuously increases along with the evolution of technologies and the quick explosion of mobile data traffic, conventional large scale system level simulations and analytical tools become either too complicated or less tractable and accurate. Therefore, novel analytical models are actively pursued. In recent years, stochastic geometry models have been recognized as powerful tools to analyze the key performance metrics of cellular networks. In this dissertation, stochastic geometry based analytical models are developed to analyze the performance of some key technologies proposed for 5G mobile networks. Particularly, Device-to-Device (D2D) communication, Non-orthogonal multiple access (NOMA), and ultra-dense networks (UDNs) are investigated and analyzed by stochastic geometry models, more specifically, Poisson Point Process (PPP) models. D2D communication enables direct communication between mobile users in proximity to each other bypassing base station (BS). Embedding D2D communication into existing cellular networks brings many benefits such as improving spectrum efficiency, decreasing power energy consumption, and enabling novel location-based services. However, these benefits may not be fully exploited if the co-channel interference among D2D users and cellular users is not properly tackled. In this dissertation, various frequency reuse and power control schemes are proposed, aiming at mitigating the interference between D2D users and conventional cellular users. The performance gain of proposed schemes is analyzed on a system modeled by a 2-tier PPP and validated by numerical simulations. NOMA is a promising radio access technology for 5G cellular networks. Different with widely applied orthogonal multiple access (OMA) such as orthogonal frequency division multiple access (OFDMA) and single carrier frequency division multiple access (SC-FDMA), NOMA allows multiple users to use the same frequency/time resource and offers many advantages such as improving spectral efficiency, enhancing connectivity, providing higher cell-edge throughput, and reducing transmission latency. Although some initial performance analysis has been done on NOMA with single cell scenario, the system level performance of NOMA in a multi-cell scenario is not investigated in existing work. In this dissertation, analytical frameworks are developed to evaluate the performance of a wireless network with NOMA on both downlink and uplink. Distinguished from existing publications on NOMA, the framework developed in this dissertation is the first one that takes inter-cell interference into consideration. UDN is another key technology for 5G wireless networks to achieve high capacity and coverage. Due to the existence of line-of-sight (LoS)/non-line-of-sight (NLoS) propagation and bounded path loss behavior in UDN networks, the tractability of the original PPP model diminishes when analyzing the performance of UDNs. Therefore, a dominant BS (base station)-based approximation model is developed in this dissertation. By applying reasonable mathematical approximations, the tractability of the PPP model is preserved and the closed form solution can be derived. The numerical results demonstrate that the developed analytical model is accurate in a wide range of network densities. The analysis conducted in this dissertation demonstrates that stochastic geometry models can serve as powerful tools to analyze the performance of 5G technologies in a dense wireless network deployment. The frameworks developed in this dissertation provide general yet powerful analytical tools that can be readily extended to facilitate other research in wireless networks.

Emerging 5G mobile networks are envisioned to become multi-service environments, enabling the dynamic deployment of services with a diverse set of performance requirements, accommodating the needs of mobile network operators, verticals and over-the-top service providers. The Radio Access Network (RAN) part of mobile networks is expected to play a very significant role towards this evolution. Unfortunately, such a vision cannot be efficiently supported by the conventional RAN architecture, which adopts a fixed and rigid design. For the network to evolve, flexibility in the creation, management and control of the RAN components is of paramount importance. The key elements that can allow us to attain this flexibility are the programmability and the virtualization of the network functions. While in the case of the mobile core, these issues have been extensively studied due to the advent of technologies like Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) and the similarities that the core shares with other wired networks like data centers, research in the domain of the RAN is still in its infancy. The contributions made in this thesis significantly advance the state of the art in the domain of RAN programmability and virtualization in three dimensions. First, we design and implement a software-defined RAN (SD-RAN) platform called FlexRAN, that provides a flexible control plane designed with support for real-time RAN control applications, flexibility to realize various degrees of coordination among RAN infrastructure entities, and programmability to adapt control over time and easier evolution to the future following SDN/NFV principles. Second, we leverage the capabilities of the FlexRAN platform to design and implement Orion, which is a novel RAN slicing system that enables the dynamic on-the-fly virtualization of base stations, the flexible customization of slices to meet their respective service needs and which can be used in an end-to-end network slicing setting. Third, we focus on the use case of multi-tenancy in a neutral-host indoors small-cell environment, where we design Iris, a system that builds on the capabilities of FlexRAN and Orion and introduces a dynamic pricing mechanism for the efficient and flexible allocation of shared spectrum to the tenants. A number of additional use cases that highlight the benefits of the developed systems are also presented. The lessons learned through this research are summarized and a discussion is made on interesting topics for future work in this domain. The prototype systems presented in this thesis have been made publicly available and are being used by various research groups worldwide in the context of 5G research.

In order to support the new paradigm shift in fifth generation (5G) mobile communication, radically different network architectures, associated technologies and network operation algorithms, need to be developed compared to existing fourth generation (4G) cellular solutions. The evolution toward 5G mobile networks will be characterized by an increasing number of wireless devices, increasing device and service complexity, and the requirement to access mobile services ubiquitously. To realise the dramatic increase in data rates in particular, research is focused on improving the capacity of current, Long Term Evolution (LTE)-based, 4G network standards, before radical changes are exploited which could include acquiring additional spectrum. The LTE network has a reuse factor of one; hence neighbouring cells/sectors use the same spectrum, therefore making the cell-edge users vulnerable to heavy inter cell interference in addition to the other factors such as fading and path-loss. In this direction, this thesis focuses on improving the performance of cell-edge users in LTE and LTE-Advanced networks by initially implementing a new Coordinated Multi-Point (CoMP) technique to support future 5G networks using smart antennas to mitigate cell-edge user interference in uplink. Successively a novel cooperative uplink inter-cell interference mitigation algorithm based on joint reception at the base station using receiver adaptive beamforming is investigated. Subsequently interference mitigation in a heterogeneous environment for inter Device-to-Device (D2D) communication underlaying cellular network is investigated as the enabling technology for maximising resource block (RB) utilisation in emerging 5G networks. The proximity of users in a network, achieving higher data rates with maximum RB utilisation (as the technology reuses the cellular RB simultaneously), while taking some load off the evolved Node B (eNodeB) i.e. by direct communication between User Equipment (UE), has been explored. Simulation results show that the proximity and transmission power of D2D transmission yields high performance gains for D2D receivers, which was demonstrated to be better than that of cellular UEs with better channel conditions or in close proximity to the eNodeB in the network. It is finally demonstrated that the application, as an extension to the above, of a novel receiver beamforming technique to reduce interference from D2D users, can further enhance network performance. To be able to develop the aforementioned technologies and evaluate the performance of new algorithms in emerging network scenarios, a beyond the-state-of-the-art LTE system-level-simulator (SLS) was implemented. The new simulator includes Multiple-Input Multiple-Output (MIMO) antenna functionalities, comprehensive channel models (such as Wireless World initiative New Radio II i.e. WINNER II) and adaptive modulation and coding schemes to accurately emulate the LTE and LTE-A network standards.

La prolifération des appareils portables mène à une croissance du trafic mobile qui provoque une surcharge du cœur du réseau cellulaire. Pour faire face à un tel problème, plusieurs travaux conseillent de stocker les contenus (fichiers et vidéos) dans les small cells. Dans cette thèse, nous proposons d'utiliser les véhicules comme des small cells mobiles et de cacher les contenus à bord, motivés par le fait que la plupart d'entre eux pourra facilement être équipée avec de la connectivité et du stockage. L'adoption d'un tel cloud mobile réduit les coûts d'installation et de maintenance et présente des contraintes énergétiques moins strictes que pour les small cells fixes. Dans notre modèle, un utilisateur demande des morceaux d'un contenu aux véhicules voisins et est redirigé vers le réseau cellulaire après une deadline ou lorsque son playout buffer est vide. L'objectif du travail est de suggérer à un opérateur comment répliquer de manière optimale les contenus afin de minimiser le trafic mobile dans le cœur du réseau. Les principales contributions sont : (i) Modélisation. Nous modélisons le scénario ci-dessus en tenant compte de la taille des contenus, de la mobilité et d'un certain nombre d'autres paramètres. (ii) Optimisation. Nous formulons des problèmes d'optimisation pour calculer les politiques d'allocation sous différents modèles et contraintes. (iii) Analyse des performances. Nous développons un simulateur MATLAB pour valider les résultats théoriques. Nous montrons que les politiques de mise en cache proposées dans cette thèse sont capables de réduire de plus que 50% la charge sur le cœur du réseau cellulaire. / The large diffusion of handheld devices is leading to an exponential growth of the mobile traffic demand which is already overloading the core network. To deal with such a problem, several works suggest to store content (files or videos) in small cells or user equipments. In this thesis, we push the idea of caching at the edge a step further, and we propose to use public or private transportation as mobile small cells and caches. In fact, vehicles are widespread in modern cities, and the majority of them could be readily equipped with network connectivity and storage. The adoption of such a mobile cloud, which does not suffer from energy constraints (compared to user equipments), reduces installation and maintenance costs (compared to small cells). In our work, a user can opportunistically download chunks of a requested content from nearby vehicles, and be redirected to the cellular network after a deadline (imposed by the operator) or when her playout buffer empties. The main goal of the work is to suggest to an operator how to optimally replicate content to minimize the load on the core network. The main contributions are: (i) Modelling. We model the above scenario considering heterogeneous content size, generic mobility and a number of other system parameters. (ii) Optimization. We formulate some optimization problems to calculate allocation policies under different models and constraints. (iii) Performance analysis. We build a MATLAB simulator to validate the theoretical findings through real trace-based simulations. We show that, even with low technology penetration, the proposed caching policies are able to offload more than 50 percent of the mobile traffic demand.

The adoption of 5G mobile networks is changing the way we connect our world. Now, it is not just phones that are connected to the network, it is everything - smart homes, self-driving cars, factory equipment, and anything in between. Because of this, there has been a large increase in the volume and complexity of mobile network traffic in recent years. As 5G becomes more widely adopted, this trend will continue moving forward. This presents a problem for mobile network operators. To account for this increase in traffic volume and complexity, the network must be optimized to handle it. However, the only way to do this is to better understand the traffic sent over the network. As such, the companies building and operating these networks rely on models that can define a set of traffic profiles from real-world network data. This thesis presents a novel method of identifying traffic profiles from 5G network data by analyzing the network traffic as unstructured time series data. Using two datasets containing TCP and UDP traffic data with 10 million time series apiece, clusters were defined for each using time series clustering techniques. Specifically, the ROCKET family of algorithms was adapted for clustering purposes, applying k-means clustering on top of the ROCKET feature transformations. The resulting clusters were analyzed and compared to another clustering model - one based on summary statistics from each time series. Overall, the ROCKET models appeared to produce more coherent traffic profiles compared to the baseline clustering model, and the proposed framework shows great promise - not just in network traffic clustering, but any analysis of unstructured time series data.

There is a prolific increase in the penetration of user devices such as smartphones and tablets. In addition, user expectations for higher Quality of Service (QoS), enhanced data rates and lower latencies are relentless. In this context, network densification through the dense deployment of small cell networks, underlaying the currently existing macrocell networks, is the most appealing approach to handle the aforementioned requirements. Small cell networks are capable of reusing the spectrum locally and providing most of the capacity while macrocell networks provide a blanket coverage for mobile user equipment (UEs). However, such setup imposes a lot of issues, among which, co-tier and cross-tier interference are the most challenging. To handle co-tier interference, I have proposed a semi-distributed (hierarchical) interference management scheme based on joint clustering and resource allocation (RA) for small cells. I have formulated the problem as a Mixed Integer Non-Linear Program (MINLP), whose solution was obtained by dividing the problem into two sub-problems, where the related tasks were shared between the Femto Gateway (FGW) and small cells. As for cross-tier interference, I have formulated RA problems for both the macrocell and small cells as optimization problems. In particular, I have introduced the idea of ``Tier-Awareness'' and studied the impact of the different RA policies in the macrocell tier on the small cells performance. I have shown that the RA policy in one tier should be carefully selected. In addition, I have formulated the RA problem for small cells as an optimization problem with an objective function that accounts for both RA and admission control (AC). Finally, I have studied cloud radio access network (C-RAN) of small cells which has been considered as a typical realization of a mobile network which is capable of supporting soft and green technologies in Fifth Generation (5G) networks, as well as a platform for the practical implementation of network multiple-input multiple-output (MIMO) and coordinated multi-point (CoMP) transmission concepts. / February 2016