An Overview of Cell Zooming Algorithms and Power Saving Capabilities in Wireless Networks

Cell zooming has emerged as a potential strategy to develop a green communication system in our society and it has become an essential research area of wireless communication. Aiming to highlight the trend of existing cell zooming algorithms and their power saving capabilities, this paper reviews a number of cell zooming algorithms that have been proposed in the literature. Static cell zooming algorithms are effective for off-peak hours and their maximum power saving capability is 50% since off-peak duration is typically not more than 12 hours.Meanwhile dynamic cell zooming algorithms are applicable in full-day operation and they are useful not only for power saving but also for load balancing. However, on/off switching delay, signalling overhead due to traffic information exchange and how to attain information of traffic spatial distribution are existing challenges in dynamic cell zooming algorithms. One noticeable point is that relative power saving in dynamic cell zooming algorithm is less than 50% if traffic spatial distribution is considered. Since location management (LM) was designed for effectively servicing to customers, further researches could lead to work on location management (LM) based cell zooming algorithms for both effective servicing and energy saving

Traffic pattern prediction in cellular networks.

PhDIncreasing numbers of users together with a more use of high bit-rate services complicate radio resource management in 3G systems. In order to improve the system capacity and guarantee the QoS, a large amount of research had been carried out on radio resource management. One viable approach reported is to use semi-smart antennas to dynamically change the radiation pattern of target cells to reduce congestion. One key factor of the semi-smart antenna techniques is the algorithm to adjust the beam pattern to cooperatively control the size and shape of each radio cell. Methods described in the literature determine the optimum radiation patterns according to the current observed congestion. By using machine learning methods, it is possible to detect the upcoming change of the traffic patterns at an early stage and then carry out beamforming optimization to alleviate the reduction in network performance. Inspired from the research carried out in the vehicle mobility prediction field, this work learns the movement patterns of mobile users with three different learning models by analysing the movement patterns captured locally. Three different mobility models are introduced to mimic the real-life movement of mobile users and provide analysable data for learning. The simulation results shows that the error rates of predictions on the geographic distribution of mobile users are low and it is feasible to use the proposed learning models to predict future traffic patterns. Being able to predict these patterns mean that the optimized beam patterns could be calculated according to the predicted traffic patterns and loaded to the relevant base stations in advance

Localisation in wireless sensor networks for disaster recovery and rescuing in built environments

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyProgress in micro-electromechanical systems (MEMS) and radio frequency (RF) technology has fostered the development of wireless sensor networks (WSNs). Different from traditional networks, WSNs are data-centric, self-configuring and self-healing. Although WSNs have been successfully applied in built environments (e.g. security and services in smart homes), their applications and benefits have not been fully explored in areas such as disaster recovery and rescuing. There are issues related to self-localisation as well as practical constraints to be taken into account. The current state-of-the art communication technologies used in disaster scenarios are challenged by various limitations (e.g. the uncertainty of RSS). Localisation in WSNs (location sensing) is a challenging problem, especially in disaster environments and there is a need for technological developments in order to cater to disaster conditions. This research seeks to design and develop novel localisation algorithms using WSNs to overcome the limitations in existing techniques. A novel probabilistic fuzzy logic based range-free localisation algorithm (PFRL) is devised to solve localisation problems for WSNs. Simulation results show that the proposed algorithm performs better than other range free localisation algorithms (namely DVhop localisation, Centroid localisation and Amorphous localisation) in terms of localisation accuracy by 15-30% with various numbers of anchors and degrees of radio propagation irregularity. In disaster scenarios, for example, if WSNs are applied to sense fire hazards in building, wireless sensor nodes will be equipped on different floors. To this end, PFRL has been extended to solve sensor localisation problems in 3D space. Computational results show that the 3D localisation algorithm provides better localisation accuracy when varying the system parameters with different communication/deployment models. PFRL is further developed by applying dynamic distance measurement updates among the moving sensors in a disaster environment. Simulation results indicate that the new method scales very well

Hybrid Satellite-Terrestrial Communication Networks for the Maritime Internet of Things: Key Technologies, Opportunities, and Challenges

With the rapid development of marine activities, there has been an increasing number of maritime mobile terminals, as well as a growing demand for high-speed and ultra-reliable maritime communications to keep them connected. Traditionally, the maritime Internet of Things (IoT) is enabled by maritime satellites. However, satellites are seriously restricted by their high latency and relatively low data rate. As an alternative, shore & island-based base stations (BSs) can be built to extend the coverage of terrestrial networks using fourth-generation (4G), fifth-generation (5G), and beyond 5G services. Unmanned aerial vehicles can also be exploited to serve as aerial maritime BSs. Despite of all these approaches, there are still open issues for an efficient maritime communication network (MCN). For example, due to the complicated electromagnetic propagation environment, the limited geometrically available BS sites, and rigorous service demands from mission-critical applications, conventional communication and networking theories and methods should be tailored for maritime scenarios. Towards this end, we provide a survey on the demand for maritime communications, the state-of-the-art MCNs, and key technologies for enhancing transmission efficiency, extending network coverage, and provisioning maritime-specific services. Future challenges in developing an environment-aware, service-driven, and integrated satellite-air-ground MCN to be smart enough to utilize external auxiliary information, e.g., sea state and atmosphere conditions, are also discussed

UE-Centric Clustering and Resource Allocation for Practical Two-Tier Heterogeneous Cellular Networks

Heterogeneous cellular Network (HetNet) has emerged as a promising technology for the 5th generation mobile networks (5G) that can be used to meet the high demand of data rate and better quality of service (QoS) performance. However, the performance of HetNet will depend on how scarce resources such as frequency, time, power and spatial resource are shared among user equipments (UEs) in the system and also how interference is controlled. In this work, we utilize UE-centric clustering as a tool to effectively determine the interfering BSs that cause significant interference to each UE in the network. These interfering BSs together with the serving BSs of these interfered UEs will coordinate and make resource allocation decision together to allocate spatial directions to each UE in the network in order to manage interference in the network. We formulate the resource allocation problem as maximizing the weighted sum-rate of HetNet while fulfilling some power, QoS and interference constraints. This optimization problem is non-convex. We readily split the RA problem into two sub-problems: the spatial direction allocation problem and the power allocation problem respectively. We are able to solve these problems efficiently using SeDumi, which provides a general purpose implementation of interior point methods. Simulation results of our proposed method, when compared with the other existing methods, show significant improvement