User Association in 5G Networks: A Survey and an Outlook

26 pages; accepted to appear in IEEE Communications Surveys and Tutorial

Energy aware management of 5G networks

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringBalasubramaniam NatarajanThe 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

Efficient energy management in ultra-dense wireless networks

The increase in demand for more network capacity has led to the evolution of wireless networks from being largely Heterogeneous (Het-Nets) to the now existing Ultra-dense (UDNs). In UDNs, small cells are densely deployed with the goal of shortening the physical distance between the base stations (BSs) and the UEs, so as to support more user equipment (UEs) at peak times while ensuring high data rates. Compared to Het-Nets, Ultra-dense networks (UDNs) have many advantages. These include, more network capacity, higher flexibility to routine configurations, and more suitability to achieve load-balancing, hence, fewer blind spots as well as lower call blocking probability. It should be noted that, in practice, due to the high density of deployed small cells in Ultra-Dense Networks, a number of issues, or rather concerns, come with this evolution from Het-Nets. Among these issues include problems with efficient radio resource management, user cell association, inter- and intra-cell interference management and, last but not least, efficient energy consumption. Some of these issues which impact the overall network efficiency are largely due to the use of obsolete algorithms, especially those whose resource allocation is based solely on received signal power (RSSP). In this paper, the focus is solely on the efficient energy management dilemma and how to optimally reduce the overall network energy consumption. Through an extensive literature review, a detailed report into the growing concern of efficient energy management in UDNs is provided in Chapter 2. The literature review report highlights the classification as well as the evolution of some of the Mobile Wireless Technologies and Mobile Wireless Networks in general. The literature review report provides reasons as to why the energy consumption issue has become a very serious concern in UltraDense networks as well as the various techniques and measures taken to mitigate this. It is shown that, due to the increasing Mobile Wireless Systems’ carbon footprint which carries serious negative environmental impact, and the general need to lower operating costs by the network operators, the management of energy consumption increases in priority. By using the architecture of a Fourth Generation Long Term Evolution (4G-LTE) UltraDense Network, the report further shows that more than 65% of the overall energy consumption is by the access network and base stations in particular. This phenomenon explains why most attention in energy efficiency management in UDNs is largely centred on reducing the energy consumption of the deployed base stations more than any other network components like the data servers or backhauling features used. Furthermore, the report also provides detailed information on the methods/techniques, their classification, implementation, as well as a critical analysis of the said implementations in literature. This study proposes a sub-optimal algorithm and Distributed Cell Resource Allocation with a Base Station On/Off scheme that aims at reducing the overall base station power consumption in UDNs, while ensuring that the overall Quality of Service (QoS) for each User Equipment (UE) as specified in its service class is met. The modeling of the system model used and hence formulation of the Network Energy Efficiency (NEE) optimization problem is done viii using stochastic geometry. The network model comprises both evolved Node B (eNB) type macro and small cells operating on different frequency bands as well as taking into account factors that impact NEE such as UE mobility, UE spatial distribution and small cells spatial distribution. The channel model takes into account signal interference from all base stations, path loss, fading, log normal shadowing, modulation and coding schemes used on each UE’s communication channels when computing throughout. The power consumption model used takes into account both static (site cooling, circuit power) and active (transmission or load based) base station power consumption. The formulation of the NEE optimization problem takes into consideration the user’s Quality-of-service (QoS), inter-cell interference, as well as each user’s spectral efficiency and coverage/success probability. The formulated NEE optimization problem is of type Nondeterministic Polynomial time (NP)-hard, due to the user-cell association. The proposed solution to the formulated optimization problem makes use of constraint relaxation to transform the NP-hard problem into a more solvable, convex and linear optimization one. This, combined with Lagrangian dual decomposition, is used to create a distributed solution. After cellassociation and resource allocation phases, the proposed solution in order to further reduce power consumption performs Cell On/Off. Then, by using the computer simulation tools/environments, the “Distributed Resource Allocation with Cell On/Off” scheme’s performance, in comparison to four other resource allocation schemes, is analysed and evaluated given a number of different network scenarios. Finally, the statistical and mathematical results generated through the simulations indicate that the proposed scheme is the closest in NEE performance to the Exhaustive Search algorithm, and hence superior to the other sub-optimal algorithms it is compared to

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig