Impact of Femtocell backhaul limitation on performance of Macro-Femto HetNet

This thesis is a techno-economical study which focuses on addressing the exponentially rising data capacity demand through network densification. The study is based on the two popular deployment strategies; Macrocellular networks and Macro-Femto heterogeneous networks, deployed in a suburban type environment with modern houses. The main aim of the dissertation is to investigate the impact of network densification on capacity, energy- and cost-efficiency of the network, while considering different femtocell backhaul connectivity limitations. The network performance is evaluated for both indoor and outdoor scenarios. A comparative analysis between the macrocellular and macro-femto network is done by increasing the density of the macrocells, femtocells and the operating frequency spectrum. The capacity is enhanced by increasing the density of the cell sites in the network but operators want to generate profit and want to adopt a cost effective solution to cater the problems. The results show that increasing the density of low-cost, low-powered femtocell access points (FAPs) in the network can solve the problem of 1000x future data capacity demand while keeping the CAPEX and OPEX of the network relatively lower than legacy pure macrocellular deployments. The deployment of the FAPs both in indoor and outdoor environments enhances the network capacity. This study helped in providing results, understanding and insight of both technical and techno-economical aspects of different mobile network deployment and densification solutions. Furthermore, the outcome of the thesis will give some guidelines for network vendors and mobile operators in evolving their network in future

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Interference management in wireless cellular networks

In wireless networks, there is an ever-increasing demand for higher system throughputs, along with growing expectation for all users to be available to multimedia and Internet services. This is especially difficult to maintain at the cell-edge. Therefore, a key challenge for future orthogonal frequency division multiple access (OFDMA)-based networks is inter-cell interference coordination (ICIC). With full frequency reuse, small inter-site distances (ISDs), and heterogeneous architectures, coping with co-channel interference (CCI) in such networks has become paramount. Further, the needs for more energy efficient, or “green,” technologies is growing. In this light, Uplink Interference Protection (ULIP), a technique to combat CCI via power reduction, is investigated. By reducing the transmit power on a subset of resource blocks (RBs), the uplink interference to neighbouring cells can be controlled. Utilisation of existing reference signals limits additional signalling. Furthermore, cell-edge performance can be significantly improved through a priority class scheduler, enhancing the throughput fairness of the system. Finally, analytic derivations reveal ULIP guarantees enhanced energy efficiency for all mobile stations (MSs), with the added benefit that overall system throughput gains are also achievable. Following this, a novel scheduler that enhances both network spectral and energy efficiency is proposed. In order to facilitate the application of Pareto optimal power control (POPC) in cellular networks, a simple feasibility condition based on path gains and signal-to-noise-plus- interference ratio (SINR) targets is derived. Power Control Scheduling (PCS) maximises the number of concurrently transmitting MSs and minimises their transmit powers. In addition, cell/link removal is extended to OFDMA operation. Subsequently, an SINR variation technique, Power SINR Scheduling (PSS), is employed in femto-cell networks where full bandwidth users prohibit orthogonal resource allocation. Extensive simulation results show substantial gains in system throughput and energy efficiency over conventional power control schemes. Finally, the evolution of future systems to heterogeneous networks (HetNets), and the consequently enhanced network management difficulties necessitate the need for a distributed and autonomous ICIC approach. Using a fuzzy logic system, locally available information is utilised to allocate time-frequency resources and transmit powers such that requested rates are satisfied. An empirical investigation indicates close-to-optimal system performance at significantly reduced complexity (and signalling). Additionally, base station (BS) reference signals are appropriated to provide autonomous cell association amongst multiple co-located BSs. Detailed analytical signal modelling of the femto-cell and macro/pico-cell layouts reveal high correlation to experimentally gathered statistics. Further, superior performance to benchmarks in terms of system throughput, energy efficiency, availability and fairness indicate enormous potential for future wireless networks

Small Cells for Broadband Internet Access in Low-Income Suburban Areas in Emerging Market Environments

Mobile broadband technologies are providing the best and most commonly used broadband connectivity in many emerging markets. In some regions such as Africa, mobile networks provide the only feasible ways for extending the socio-economic benefits of broadband Internet access to the masses. The use of small cell technologies, like femtocells provide an attractive solution for such areas as femtocells are most cost – effective option for coverage and capacity expansion. Furthermore, femtocells are operator managed access points which can be easily deployed and operated by the end user. It is well known that increased densification of cell sites is the most effective means for broadband mobile network capacity and coverage enhancements. However, cell densification through adding new macrocell sites by operators is usually a costly option. Therefore, this thesis will investigate methods to achieve mobile broadband capacity and coverage enhancements in low – income informal settlements or slum area, through more cost – effective cell densification using femtocells. Moreover this thesis will validate the performance gains of small cell concept for the case study through extensive simulations. The impacts of femtocell in the network, the performance gain from femtocell and gain provided by different deployment strategies have been studied. Simulation results highlight the potential benefits of using femtocells in the network for extended broadband connectivity. With the femto increment the network performance increases up to a great extent

Performance analysis of biological resource allocation algorithms for next generation networks.

Masters Degree. University of KwaZulu-Natal, Durban.Abstract available in PDF.Publications listed on page iii

Radio network management in cognitive LTE-Femtocell Systems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.There is a strong uptake of femtocell deployment as small cell application platforms in the upcoming LTE networks. In such two-tier networks of LTEfemtocell base stations, a large portion of the assigned spectrum is used sporadically leading to underutilisation of valuable frequency resources. Novel spectrum access techniques are necessary to solve these current spectrum inefficiency problems. Therefore, spectrum management solutions should have the features to improve spectrum access in both temporal and spatial manner. Cognitive Radio (CR) with the Dynamic Spectrum Access (DSA) is considered to be the key technology in this research in order to increase the spectrum efficiency. This is an effective solution to allow a group of Secondary Users (SUs) to share the radio spectrum initially allocated to the Primary User (PUs) at no interference. The core aim of this thesis is to develop new cognitive LTE-femtocell systems that offer a 4G vision, to facilitate the radio network management in order to increase the network capacity and further improve spectrum access probabilities. In this thesis, a new spectrum management model for cognitive radio networks is considered to enable a seamless integration of multi-access technology with existing networks. This involves the design of efficient resource allocation algorithms that are able to respond to the rapid changes in the dynamic wireless environment and primary users activities. Throughout this thesis a variety of network upgraded functions are developed using application simulation scenarios. Therefore, the proposed algorithms, mechanisms, methods, and system models are not restricted in the considered networks, but rather have a wider applicability to be used in other technologies. This thesis mainly investigates three aspects of research issues relating to the efficient management of cognitive networks: First, novel spectrum resource management modules are proposed to maximise the spectrum access by rapidly detecting the available transmission opportunities. Secondly, a developed pilot power controlling algorithm is introduced to minimise the power consumption by considering mobile position and application requirements. Also, there is investigation on the impact of deploying different numbers of femtocell base stations in LTE domain to identify the optimum cell size for future networks. Finally, a novel call admission control mechanism for mobility management is proposed to support seamless handover between LTE and femtocell domains. This is performed by assigning high speed mobile users to the LTE system to avoid unnecessary handovers. The proposed solutions were examined by simulation and numerical analysis to show the strength of cognitive femtocell deployment for the required applications. The results show that the new system design based on cognitive radio configuration enable an efficient resource management in terms of spectrum allocation, adaptive pilot power control, and mobile handover. The proposed framework and algorithms offer a novel spectrum management for self organised LTE-femtocell architecture. Eventually, this research shows that certain architectures fulfilling spectrum management requirements are implementable in practice and display good performance in dynamic wireless environments which recommends the consideration of CR systems in LTE and femtocell networks