3 research outputs found
Resource Allocation for Next Generation Radio Access Networks
Driven by data hungry applications, the architecture of mobile networks is
moving towards that of densely deployed cells where each cell may use a different
access technology as well as a different frequency band. Next generation
networks (NGNs) are essentially identified by their dramatically increased data
rates and their sustainable deployment. Motivated by these requirements, in
this thesis we focus on (i) capacity maximisation, (ii) energy efficient configuration
of different classes of radio access networks (RANs). To fairly allocate
the available resources, we consider proportional fair rate allocations. We
first consider capacity maximisation in co-channel 4G (LTE) networks, then
we proceed to capacity maximisation in mixed LTE (including licensed LTE
small cells) and 802.11 (WiFi) networks. And finally we study energy efficient
capacity maximisation of dense 3G/4G co-channel small cell networks.
In each chapter we provide a network model and a scalable resource allocation
approach which may be implemented in a centralised or distributed manner
depending on the objective and network constraints
Architecture of a cognitive non-line-of-sight backhaul for 5G outdoor urban small cells
Densely deployed small cell networks will address the growing demand for broadband mobile connectivity, by increasing access network capacity and coverage. However, most potential small cell base station (SCBS) locations do not have existing telecommunication infrastructure. Providing backhaul connectivity to core networks is therefore a challenge. Millimeter wave (mmW) technologies operated at 30-90GHz are currently being considered to provide low-cost, flexible, high-capacity and reliable backhaul solutions using existing roof-mounted backhaul aggregation sites. Using intelligent mmW radio devices and massive multiple-input multiple-output (MIMO), for enabling point-to-multipoint (PtMP) operation, is considered in this research. The core aim of this research is to develop an architecture of an intelligent non-line-sight (NLOS) small cell backhaul (SCB) system based on mmW and massive MIMO technologies, and supporting intelligent algorithms to facilitate reliable NLOS street-to-rooftop NLOS SCB connectivity. In the proposed architecture, diffraction points are used as signal anchor points between backhaul radio devices. In the new architecture the integration of these technologies is considered. This involves the design of efficient artificial intelligence algorithms to enable backhaul radio devices to autonomously select suitable NLOS propagation paths, find an optimal number of links that meet the backhaul performance requirements and determine an optimal number of diffractions points capable of covering predetermined SCB locations. Throughout the thesis, a number of algorithms are developed and simulated using the MATLAB application. This thesis mainly investigates three key issues: First, a novel intelligent NLOS SCB architecture, termed the cognitive NLOS SCB (CNSCB) system is proposed to enable street-to-rooftop NLOS connectivity using predetermined diffraction points located on roof edges. Second, an algorithm to enable the autonomous creation of multiple-paths, evaluate the performance of each link and determine an optimal number of possible paths per backhaul link is developed. Third, an algorithm to determine the optimal number of diffraction points that can cover an identified SCBS location is also developed. Also, another investigated issue related to the operation of the proposed architecture is its energy efficiency, and its performance is compared to that of a point-to-point (PtP) architecture. The proposed solutions were examined using analytical models, simulations and experimental work to determine the strength of the street-to-rooftop backhaul links and their ability to meet current and future SCB requirements. The results obtained showed that reliable multiple NLOS links can be achieved using device intelligence to guide radio signals along the propagation path. Furthermore, the PtMP architecture is found to be more energy efficient than the PtP architecture. The proposed architecture and algorithms offer a novel backhaul solution for outdoor urban small cells. Finally, this research shows that traditional techniques of addressing the demand for connectivity, which consisted of improving or evolving existing solutions, may nolonger be applicable in emerging communication technologies. There is therefore need to consider new ways of solving the emerging challenges
Dynamic Idle Mode Control in Small Cell Networks
The deployment of both outdoor and indoor Small
Cell Base Stations (SCBSs) has attracted significant interest in
the wireless industry. However, a critical concern in a large scale
deployment is the efficient control of the small cell’s transmission
mode. In this paper we propose a load aware approach for
dynamic idle mode selection where the load distribution is
estimated by the “RF Fingerprints” of the users. Our approach
allows pilot signals and most of the processing power of a SCBS
to be completely switched off when no active user is in its
vicinity, or when the required Quality of Service (QoS) can
be provided by the underlying macro base stations. Such an
approach significantly reduces the energy consumption of the
small cells as well as reducing the pilot pollution and signalling
overhead. We evaluate the efficiency of our approach using field
measurements in central Dublin as well as with system-level
simulations. We show that the proposed method is capable of
identifying idle cells with an average prediction error rate of
1.9%. Moreover we show that it has the potential to achieve an
average reduction of 90% in kWh power consumption compared
to the concurrent operation of all SCBSs