Interference Aware Cognitive Femtocell Networks

Femtocells Access Points (FAP) are low power, plug and play home base stations which are designed to extend the cellular radio range in indoor environments where macrocell coverage is generally poor. They offer significant increases in data rates over a short range, enabling high speed wireless and mobile broadband services, with the femtocell network overlaid onto the macrocell in a dual-tier arrangement. In contrast to conventional cellular systems which are well planned, FAP are arbitrarily installed by the end users and this can create harmful interference to both collocated femtocell and macrocell users. The interference becomes particularly serious in high FAP density scenarios and compromises the ensuing data rate. Consequently, effective management of both cross and co-tier interference is a major design challenge in dual-tier networks. Since traditional radio resource management techniques and architectures for single-tier systems are either not applicable or operate inefficiently, innovative dual-tier approaches to intelligently manage interference are required. This thesis presents a number of original contributions to fulfill this objective including, a new hybrid cross-tier spectrum sharing model which builds upon an existing fractional frequency reuse technique to ensure minimal impact on the macro-tier resource allocation. A new flexible and adaptive virtual clustering framework is then formulated to alleviate co-tier interference in high FAP densities situations and finally, an intelligent coverage extension algorithm is developed to mitigate excessive femto-macrocell handovers, while upholding the required quality of service provision. This thesis contends that to exploit the undoubted potential of dual-tier, macro-femtocell architectures an interference awareness solution is necessary. Rigorous evidence confirms that noteworthy performance improvements can be achieved in the quality of the received signal and throughput by applying cognitive methods to manage interference

Partially-Distributed Resource Allocation in Small-Cell Networks

We propose a four-stage hierarchical resource allocation scheme for the downlink of a large-scale small-cell network in the context of orthogonal frequency-division multiple access (OFDMA). Since interference limits the capabilities of such networks, resource allocation and interference management are crucial. However, obtaining the globally optimum resource allocation is exponentially complex and mathematically intractable. Here, we develop a partially decentralized algorithm to obtain an effective solution. The three major advantages of our work are: 1) as opposed to a fixed resource allocation, we consider load demand at each access point (AP) when allocating spectrum; 2) to prevent overloaded APs, our scheme is dynamic in the sense that as the users move from one AP to the other, so do the allocated resources, if necessary, and such considerations generally result in huge computational complexity, which brings us to the third advantage: 3) we tackle complexity by introducing a hierarchical scheme comprising four phases: user association, load estimation, interference management via graph coloring, and scheduling. We provide mathematical analysis for the first three steps modeling the user and AP locations as Poisson point processes. Finally, we provide results of numerical simulations to illustrate the efficacy of our scheme.Comment: Accepted on May 15, 2014 for publication in the IEEE Transactions on Wireless Communication

Integration of TV White Space and Femtocell Networks.

PhDFemtocell is an effective approach to increase system capacity in cellular networks. Since traditional Femtocells use the same frequency band as the cellular network, cross-tier and co-tier interference exist in such Femtocell networks and have a major impact on deteriorating the system throughput. In order to tackle these challenges, interference mitigation has drawn attentions from both academia and industry. TV White Space (TVWS) is a newly opened portion of spectrum, which comes from the spare spectrum created by the transition from analogue TV to digital TV. It can be utilized by using cognitive radio technology according to the policies from telecommunications regulators. This thesis considers using locally available TVWS to reduce the interference in Femtocell networks. The objective of this research is to mitigate the downlink cross-tier and co-tier interference in different Femtocell deployment scenarios, and increase the throughput of the overall system. A Geo-location database model to obtain locally available TVWS information in UK is developed in this research. The database is designed using power control method to calculate available TVWS channels and maximum allowable transmit power based on digital TV transmitter information in UK and regulations on unlicensed use of TVWS. The proposed database model is firstly combined with a grid-based resource allocation scheme and investigated in a simplified Femtocell network to demonstrate the gains of using TVWS in Femtocell networks. Furthermore, two Femtocell deployment scenarios are studied in this research. In the suburban Femtocell deployment scenario, a novel system architecture that consists of the Geo-location database and a resource allocation scheme using TVWS is proposed to mitigate cross-tier interference between Macrocell and Femtocells. In the dense Femtocell deployment scenario, a power efficient resource allocation scheme is proposed to maximize the throughput of Femtocells while limiting the co-tier interference among Femtocells. The optimization problem in the power efficient scheme is solved by using sequential quadratic programming method. The simulation results show that the proposed schemes can effectively mitigate the interference in Femtocell networks in practical deployment scenarios

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