Coverage optimization and power reduction in SFN using simulated annealing

An approach that predicts the propagation, models the terrestrial receivers and optimizes the performance of single frequency networks (SFN) for digital video broadcasting in terms of the final coverage achieved over any geographical region, enhancing the most populated areas, is proposed in this paper. The effective coverage improvement and thus, the self-interference reduction in the SFN is accomplished by optimizing the internal static delays, sector antenna gain, and both azimuth and elevation orientation for every transmitter within the network using the heuristic simulated annealing (SA) algorithm. Decimation and elevation filtering techniques have been considered and applied to reduce the computational cost of the SA-based approach, including results that demonstrate the improvements achieved. Further representative results for two SFN in different scenarios considering the effect on the final coverage of optimizing any of the transmitter parameters previously outlined or a combination of some of them are reported and discussed in order to show both, the performance of the method and how increasing gradually the complexity of the model for the transmitters leads to more realistic and accurate results.This work was supported by the Spanish Ministry of Science and Innovation under Projects TEC2008-02730 and TEC2012-33321. The work of M. Lanza and Á. L. Gutiérrez was supported by a Pre-Doctoral Grant from the University of Cantabria

Deterministic diffraction loss modelling for novel broadband communication in rural environments

This paper presents a deterministic modelling approach to predict diffraction loss for an innovative Multi-User-Single-Antenna (MUSA) MIMO technology, proposed for rural Australian environments. In order to calculate diffraction loss, six receivers have been considered around an access point in a selected rural environment. Generated terrain profiles for six receivers are presented in this paper. Simulation results using classical diffraction models and diffraction theory are also presented by accounting the rural Australian terrain data. Results show that in an area of 900 m by 900 m surrounding the receivers, path loss due to diffraction can range between 5 dB and 35 dB. Diffraction loss maps can contribute to determine the optimal location for receivers of MUSA-MIMO systems in rural areas

Modelling and coverage improvement of DVB-T networks

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe necessity of accurate point-to-area and point-to-point prediction tools arises from the enormous demand in designing broadcasting systems for digital TV and cellular communications. Up to now, a considerable number of coverage prediction models for radio coverage has been developed. In electromagnetic wave propagation theory, there are three types of propagation models. Empirical models that are based on a large quantity of measurement data are elementary but not very accurate. Semi-deterministic models that are based on measurement data and electromagnetic theory of propagation, which are more precise. Finally, deterministic models based on theoretical physics, like diffraction theory and Fresnel theory, that require a significant amount of geometrical data about the propagation terrain profile but are the most accurate. The primary outcomes of this research are the comparative study and improvement of several propagation models, using a significant quantity of measurements and simulations and the deduction of useful conclusions to be used by engineers to improve propagation predictions further. In this research, the Longley-Rice (ITM) Irregular Terrain Model model was used, a classic model used for TV coverage prediction, which model is to date the preferred model of the FCC (Federal Communications Commission) in the US for FM-TV coverage calculations. To run the model, the Radio Mobile program (Radio Propagation and Virtual Mapping Freeware) was used based on the Longley-Rice Model ITM, including the 3-arc-second Satellite Radar Terrain Mission (SRTM) maps and the SPLAT! program (an RF Signal Propagation, Loss, And Terrain analysis tool), which also relies on the Longley-Rice ITM model and makes use of SRTM maps. Both programs work in Windows operating system (Windows7 Professional, 64 bits). Another model used in this research was SPLAT! with ITWOM (Irregular Terrain with Obstructions Model) which combines empirical data from the ITU-R P.1546 model and other ITU recommendations in conjunction with Beer's and Snell's laws. The ITU-R Recommendation P.1546 model and the empirical Hata-Davidson model using HAAT were also utilized in this research. The Single Knife-Edge (SKE) model was coded in MATLAB and utilized in this research as a simple reference model, where only one main obstacle is considered. Other well-known multiple knife-edge diffraction models employed in this study are the Epstein-Peterson, Deygout, and Giovaneli models. For these deterministic models, individual MATLAB programs were written. Simulations produced by the models were limited to the main two knife-edges of the propagation path for immediate comparison with the Longley-Rice model which uses the “double knife-edge” approach. All measurement campaigns took place in Northern Greece and Southern (F.Y.R.O.M) Former Yugoslav Republic of Macedonia using a Rohde & Schwarz FSH-3 portable spectrum analyser and precision calibrated antennas

Optimisation of a propagation model for last mile connectivity with low altitude platforms using machine learning

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonOur related research review on propagation models reveals six factors that are significant in last mile connectivity via LAP: path loss, elevation angle, LAP altitude, coverage area, power consumption, operation frequency, interference, and antenna type. These factors can help with monitoring system performance, network planning, coverage footprint, receivers’ line-of-sight, quality of service requirements, and data rates which may all vary in response to geomorphology characteristics. Several competing propagation models have been proposed over the years but whilst they collectively raise many shortcomings such as limited altitude up to few tens of meters, lack of cover across different environments, low perdition accuracy they also exhibit several advantages. Four propagation models, which are representatives of their types, have been selected since they exhibit advantages in relation to high altitude, wide coverage range, adaption across different terrains. In addition, all four have been extensively deployed in the past and as a result their correction factors have evolved over the years to yield extremely accurate results which makes the development and evaluation aspects of this research very precise. The four models are: ITU-R P.529-3, Okumura, Hata-Davidson, and ATG. The aim of this doctoral research is to design a new propagation model for last-mile connectivity using LAPs technology as an alternative to aerial base station that includes all six factors but does not exhibit any of the shortcomings of existing models. The new propagation model evolves from existing models using machine learning. The four models are first adapted to include the elevation angle alongside the multiple-input multiple-output diversity gain, our first novelty in propagation modelling. The four adapted models are then used as input in a Neural Network framework and their parameters are clustered in a Self-Organizing-Map using a minimax technique. The framework evolves an optimal propagation model that represents the main research contribution of this research. The optimal propagation model is deployed in two proof-of-concept applications, a wireless sensor network, and a cellular structure. The performance of the optimal model is evaluated and then validated against that of the four adapted models first in relation to predictions reported in the literature and then in the context of the two proof-of-concept applications. The predictions of the optimised model are significantly improved in comparison to those of the four adapted propagation models. Each of the two proof-of-concept applications also represent a research novelty.The Royal Saudi Embassy and the Saudi Cultural Bureau in London, and Taif University in the Kingdom of Saudi Arabia