Opportunistic Third-Party Backhaul for Cellular Wireless Networks

With high capacity air interfaces and large numbers of small cells, backhaul -- the wired connectivity to base stations -- is increasingly becoming the cost driver in cellular wireless networks. One reason for the high cost of backhaul is that capacity is often purchased on leased lines with guaranteed rates provisioned to peak loads. In this paper, we present an alternate \emph{opportunistic backhaul} model where third parties provide base stations and backhaul connections and lease out excess capacity in their networks to the cellular provider when available, presumably at significantly lower costs than guaranteed connections. We describe a scalable architecture for such deployments using open access femtocells, which are small plug-and-play base stations that operate in the carrier's spectrum but can connect directly into the third party provider's wired network. Within the proposed architecture, we present a general user association optimization algorithm that enables the cellular provider to dynamically determine which mobiles should be assigned to the third-party femtocells based on the traffic demands, interference and channel conditions and third-party access pricing. Although the optimization is non-convex, the algorithm uses a computationally efficient method for finding approximate solutions via dual decomposition. Simulations of the deployment model based on actual base station locations are presented that show that large capacity gains are achievable if adoption of third-party, open access femtocells can reach even a small fraction of the current market penetration of WiFi access points.Comment: 9 pages, 6 figure

Spectrum Leasing as an Incentive towards Uplink Macrocell and Femtocell Cooperation

The concept of femtocell access points underlaying existing communication infrastructure has recently emerged as a key technology that can significantly improve the coverage and performance of next-generation wireless networks. In this paper, we propose a framework for macrocell-femtocell cooperation under a closed access policy, in which a femtocell user may act as a relay for macrocell users. In return, each cooperative macrocell user grants the femtocell user a fraction of its superframe. We formulate a coalitional game with macrocell and femtocell users being the players, which can take individual and distributed decisions on whether to cooperate or not, while maximizing a utility function that captures the cooperative gains, in terms of throughput and delay.We show that the network can selforganize into a partition composed of disjoint coalitions which constitutes the recursive core of the game representing a key solution concept for coalition formation games in partition form. Simulation results show that the proposed coalition formation algorithm yields significant gains in terms of average rate per macrocell user, reaching up to 239%, relative to the non-cooperative case. Moreover, the proposed approach shows an improvement in terms of femtocell users' rate of up to 21% when compared to the traditional closed access policy.Comment: 29 pages, 11 figures, accepted at the IEEE JSAC on Femtocell Network

Low energy indoor network : deployment optimisation

This article considers what the minimum energy indoor access point deployment is in order to achieve a certain downlink quality-of-service. The article investigates two conventional multiple-access technologies, namely: LTE-femtocells and 802.11n Wi-Fi. This is done in a dynamic multi-user and multi-cell interference network. Our baseline results are reinforced by novel theoretical expressions. Furthermore, the work underlines the importance of considering optimisation when accounting for the capacity saturation of realistic modulation and coding schemes. The results in this article show that optimising the location of access points both within a building and within the individual rooms is critical to minimise the energy consumption

Self-organising network management for heterogeneous LTE-advanced networks

This thesis was submitted for the award of Doctor of Philosophy and awarded by Brunel University LondonSince 2004, when the Long Term Evolution (LTE) was first proposed to be publicly available in the year 2009, a plethora of new characteristics, techniques and applications have been constantly enhancing it since its first release, over the past decade. As a result, the research aims for LTE-Advanced (LTE-A) have been released to create a ubiquitous and supportive network for mobile users. The incorporation of heterogeneous networks (HetNets) has been proposed as one of the main enhancements of LTE-A systems over the existing LTE releases, by proposing the deployment of small-cell applications, such as femtocells, to provide more coverage and quality of service (QoS) within the network, whilst also reducing capital expenditure. These principal advantages can be obtained at the cost of new challenges such as inter-cell interference, which occurs when different network applications share the same frequency channel in the network. In this thesis, the main challenges of HetNets in LTE-A platform have been addressed and novel solutions are proposed by using self-organising network (SON) management approaches, which allows the cooperative cellular systems to observe, decide and amend their ongoing operation based on network conditions. The novel SON algorithms are modelled and simulated in OPNET modeler simulation software for the three processes of resource allocation, mobility management and interference coordination in multi-tier macro-femto networks. Different channel allocation methods based on cooperative transmission, frequency reuse and dynamic spectrum access are investigated and a novel SON sub-channel allocation method is proposed based on hybrid fractional frequency reuse (HFFR) scheme to provide dynamic resource allocation between macrocells and femtocells, while avoiding co-tier and cross-tier interference. Mobility management is also addressed as another important issue in HetNets, especially in hand-ins from macrocell to femtocell base stations. The existing research considers a limited number of methods for handover optimisation, such as signal strength and call admission control (CAC) to avoid unnecessary handovers, while our novel SON handover management method implements a comprehensive algorithm that performs sensing process, as well as resource availability and user residence checks to initiate the handover process at the optimal time. In addition to this, the novel femto over macro priority (FoMP) check in this process also gives the femtocell target nodes priority over the congested macrocells in order to improve the QoS at both the network tiers. Inter-cell interference, as the key challenge of HetNets, is also investigated by research on the existing time-domain, frequency-domain and power control methods. A novel SON interference mitigation algorithm is proposed, which is based on enhanced inter-cell interference coordination (eICIC) with power control process. The 3-phase power control algorithm contains signal to interference plus noise ratio (SINR) measurements, channel quality indicator (CQI) mapping and transmission power amendments to avoid the occurrence of interference due to the effects of high transmission power. The results of this research confirm that if heterogeneous systems are backed-up with SON management strategies, not only can improve the network capacity and QoS, but also the new network challenges such as inter-cell interference can also be mitigated in new releases of LTE-A network

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

Power adjustment and scheduling in OFDMA femtocell networks

Densely-deployed femtocell networks are used to enhance wireless coverage in public spaces like office buildings, subways, and academic buildings. These networks can increase throughput for users, but edge users can suffer from co-channel interference, leading to service outages. This paper introduces a distributed algorithm for network configuration, called Radius Reduction and Scheduling (RRS), to improve the performance and fairness of the network. RRS determines cell sizes using a Voronoi-Laguerre framework, then schedules users using a scheduling algorithm that includes vacancy requests to increase fairness in dense femtocell networks. We prove that our algorithm always terminate in a finite time, producing a configuration that guarantees user or area coverage. Simulation results show a decrease in outage probability of up to 50%, as well as an increase in Jain's fairness index of almost 200%

Coalitional Games with Overlapping Coalitions for Interference Management in Small Cell Networks

In this paper, we study the problem of cooperative interference management in an OFDMA two-tier small cell network. In particular, we propose a novel approach for allowing the small cells to cooperate, so as to optimize their sum-rate, while cooperatively satisfying their maximum transmit power constraints. Unlike existing work which assumes that only disjoint groups of cooperative small cells can emerge, we formulate the small cells' cooperation problem as a coalition formation game with overlapping coalitions. In this game, each small cell base station can choose to participate in one or more cooperative groups (or coalitions) simultaneously, so as to optimize the tradeoff between the benefits and costs associated with cooperation. We study the properties of the proposed overlapping coalition formation game and we show that it exhibits negative externalities due to interference. Then, we propose a novel decentralized algorithm that allows the small cell base stations to interact and self-organize into a stable overlapping coalitional structure. Simulation results show that the proposed algorithm results in a notable performance advantage in terms of the total system sum-rate, relative to the noncooperative case and the classical algorithms for coalitional games with non-overlapping coalitions