Comparative study of Radio Mobile and ICS Telecom propagation prediction models for DVB-T

In this paper, a comparative study between the results of a measurement campaign conducted in northern Greece and simulations performed with Radio Mobile and ICS Telecom radio planning tools is performed. The DVB-T coverage of a transmitting station located near the city of Thessaloniki is estimated using three empirical propagation models (NTIA-ITS Longley Rice, ITU-R P.1546 and Okumura-Hata-Davidson) and one deterministic model (ITU-R 525/526). The best results in terms of minimum average error and standard deviation are obtained using the deterministic model and the NTIA-ITS Longley Rice empirical model. In order to improve the results, the tuning options available in the ICS Telecom software are used on the Okumura-Hata-Davidson model, leading to a significant increase in accuracy

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

Evaluation of prediction accuracy for the Longley-Rice model in the FM and TV bands

Accurate geographical coverage predictions maps for FM and TV are needed for channel and frequency allocations and in order to avoid unwanted interferences. The Longley-Rice model has been used for this purpose over the last four decades and still being used almost exclusively by the FCC in the United States. In this work a comparison is presented between the relative accuracy of this model in the VHF-FM and UHF-TV frequency bands. Simulations were made with accurate and up to date input data (antenna height, location, gain, transmit power, etc.) for the FM-TV stations provided by the ERT S.A. public broadcaster in the region of Thessaloniki – Greece. Finally, the calculated – simulated results were confronted to field measurements using a Rohde & Schwarz FSH3 portable spectrum analyzer and high precision calibrated biconical and log-periodic antennas, and the errors between predictions and measurements were statistically analyzed in the two frequency bands. It has been found in this study that the Longley-Rice model, in general, overestimates field-strength values, but this overestimation is much higher in the VHF – FM radio band (88-108 MHz) than in the UHF-TV band (470-790 MHz)

Coverage Determination of Incumbent System and Available TV White Space Channels for Secondary Use in Ethiopia

Different path loss modelsare used to analyze the behavior of terrestrial television signals. The path loss calculated by one model differs from the other depending on different factors they consider. Frequency is one of the main factors included in each model. The frequency variation in the electromagnetic spectrum causes different response for each model. In terrestrial TV signal representation, since it is operating under VHF and UHF spectrum range, the propagation model used to model the signal must be less invariant when the transmitter is operating in VHF and UHF. If the pathloss model used is very variant it is difficult to define the coverage of the transmitters. This causes interference among transmitters and between the digital terrestrial TV transmitters and TV white space devices. Different propagation models are analyzed by their sensitivity to frequency variation from very-high and ultra-high frequency spectrums. After the best model is selected, we have used this model to find the coverage of the incumbent transmitter, which then is used to analyze free channels for secondary use. First the pathloss at VHF and then for UHF is calculated. This difference is then compared and the result indicates that ITU-R P.1546–5, which incorporate terrain data is best of others. Using this model and further analyze the coverage and free channels, we have found a minimum of 408 MHz free contiguous bandwidth, by considering a worst-case scenario, which is placing a WSD at the incumbent transmitter

Weather-Based Nonlinear Regressions for Digital TV Received Signal Strength Prediction

In this research, the impact of various weather conditions on digital television signals is investigated. Machine learning and nonlinear regression models were used to estimate the strength of the received signal. The received signal strength might vary significantly depending on the weather condition, especially in higher frequency ranges or millimetre wavelengths. Predictive analysis was performed for the radio-relay link Aval Tower-Vršac Hill, which is used for the distribution of television and radio programmes by the public company Broadcasting Technology and Connections in Serbia. The prediction was made using temperature, temperature index, relative humidity, and received signal strength data for the months of June, July, and August in 2022. The best results were obtained using the RandomForest model. Extreme variations in the strength of the received signal can be predicted by using the model mentioned above. More effective management of the broadcasting infrastructure can be done with the ability to predict sudden falls and fluctuations in received signal strength

Adaptive averaging channel estimation for DVB-T2 systems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIn modern communication systems, the rate of transmitted data is growing rapidly. This leads to the need for more sophisticated methods and techniques of implementation in every block of the transmitter-receiver chain. The weakest link in radio communications is the transmission channel. The signal, which is passed through it, suffers from many degrading factors like noise, attenuation, diffraction, scattering etc. In the receiver side, the modulated signal has to be restored to its initial state in order to extract the useful information. Assuming that the channel acts like a filter with finite impulse, one has to know its coefficients in order to apply the inverse function, which will restore the signal back to its initial state. The techniques which deal with this problem are called channel estimation. Noise is one of the causes that degrade the quality of the received signal. If it could be discarded, then the process of channel estimation would be easier. Transmitting special symbols, called pilots with known amplitude, phase and position to the receiver and assuming that the noise has zero mean, an averaging process could reduce the noise impact to the pilot amplitudes and thus simplify the channel estimation process. In this thesis, a novel channel estimation method based on noise rejection is introduced. The estimator takes into account the time variations of the channel and adapts its buffer size in order to achieve the best performance. Many configurations of the estimator were tested and at the beginning of the research fixed size estimators were tested. The fixed estimator has a very good performance for channels which could be considered as stationary in the time domain, like Additive White Gaussian Noise (AWGN) channels or slowly time-varying channels. AWGN channel is a channel model where the only distorting factor is the noise, where noise is every unwanted signal interfering with the useful signal. The properties of the noise are that it is additive, which means that the noise is superimposed on the transmitted signal, it is white so the power density is constant for all frequencies, and it has a Gaussian distribution in the time domain with zero mean and variance σ2=N. A slowly time varying channel refers to channel with coherence time larger than the transmitted symbol duration. The performance of a fixed size averaging estimator in case of fast time-varying channels is subject to the buffering time. When the buffering time is smaller or equal to a portion of the coherence time the averaging process offers better performance than the conventional estimation, but when the buffering time exceeds this portion of the coherence time the performance of the averaging process degrades fast. So, an extension has been made to the averaging estimator that estimates the Doppler shift and thus the coherence time, where the channel could be assumed as stationary. The improved estimator called Adaptive Averaging Channel Estimator (AACE) is capable to adjust its buffer size and thus to average only successive Orthogonal Frequency Division Multiplexing (OFDM) symbols that have the same channel distortions. The OFDM is a transmission method where instead of transmitting the data stream using only on carrier, the stream is divided into parallel sub-streams where the subcarriers conveying the sub-streams are orthogonal to each other. The use of the OFDM increases the symbol duration making it more robust against Inter-Symbol Interference (ISI), which the interference among successive transmitted symbols, and also divides the channel bandwidth into small sub-bandwidths preventing frequency selectivity because of the multipath nature of the radio channel. Simulations using the Rayleigh channel model were performed and the results clearly demonstrate the benefits of the AACE in the channel estimation process. The performance of the combination of AACE with Least Square estimation (AACE-LS) is superior to the conventional Least Square estimation especially for low Doppler shifts and it is close to the Linear Minimum Mean Square Error (LMMSE) estimation performance. Consequently, if the receiver has low computational resources and/or the channel statistics are unknown, then the AACE-LS estimator is a valid choice for modern radio receivers. Moreover, the proposed adaptive averaging process could be used in any OFDM system based on pilot aided channel estimation. In order to verify the superiority of the AACE algorithm, quantitative results are provided in terms of BER vs SNR. It is demonstrated that AACE-LS is 7dB more sensitive than the LS estimator

Assessment of an Online RF Propagation Hybrid Architecture for Communication-Aware Small Unmanned Aircraft Systems

Small Unmanned Aircraft Systems (sUAS) are attracting significant attention for their use in a wide range applications. These applications can be categorized into two types based on their communication objective; communication-focused or communication-enabling. In both types it is beneficial to completing its mission if the sUAS is communicational-aware or it has information to assess the performance of a given communication channel. In the literature there is a lack of adaptable, robust, and online methods to provide the necessary information to be communication-aware. This thesis expands on a few authors' work to formally define and assess an online hybrid architecture that is also adaptable and robust. The architecture has a Bayesian approach combining an a-priori RF propagation model with a machine learning correction tool to provide an initial estimate and learn the deviation of that a-priori model to provide a combined prediction at any given point. The hybrid architecture is implemented and a series of assessments using simulation and flight data are performed on its capabilities and performance. The results from these assessments are that the online hybrid architecture provides a benefit over learning the field directly and when applied to a wireless airborne relaying application can perform as well as naive approaches