Model For Propagation Loss Prediction Research Articles

An Accurate Maritime Radio Propagation Loss Prediction Approach Employing Neural Networks

The radio propagation loss prediction model is essential for maritime communication. The oceanic tropospheric duct is much more complicated than the atmospheric structure on land due to the rough sea surface influence, and it leads to difficulties in loss prediction. Classical radio wave propagation loss prediction models are either based on complicated electromagnetic wave theories or rely on empirical data. Consequently, they suffer from low accuracy and a limited range of application. To address this issue, a novel maritime propagation loss prediction approach is proposed, which fully exploits the data for training. In this new approach, 3D sea surface contour profiles are generated based on the Pierson–Moskowitz spectrum theory and the direction expansion formula Stereo Wave Observation Program (SWOP), by sweeping the parameters. The full-wave propagation procedure of radio signals over the sea surface is simulated by commercial EM analysis software CST Studio Suite. Based on the large quantity of simulated data, the BP neural network is employed to fit the radio propagation loss and obtain the sea surface radio wave propagation prediction model. Other classical machine learning methods are also compared to validate the proposed approach. Traditional empirical model construction relies on observation data. This approach, for the first time, proposed an automatic scheme which covers the whole procedure from the data generation to prediction model training. It avoids the requirements for on-site observation, as well as significantly decreases the cost of experiments. The application scope of the prediction model such as propagation distance range and working frequency range could be adaptive through adjustments for simulated sea size and simulated working frequency. This approach is validated to save 99% of prediction time in comparison with the full-wave simulations. The prediction model trained via our proposed method could obtain the coefficient of determination R2 which is over 0.92, demonstrating the superiority of this method.

Characterisation of Propagation Loss for a 3G Cellular Network in a Crowded Market Area Using CCIR Model

In this paper, the propagation loss for 1800 MHz cellular network in a crowded market is studied and characterized using the Comit´e International des Radio-Communication, (CCIR) propagation loss model. Empirical measurement of the received signal strength in the market was conducted using CellMapper android app installed on Samsung Galaxy S4 phone. The CCIR model was configured with three different percentages of covered areas (PB). The model was optimized using the root means square error (RMSE) method and also by tuning the PB value. The un-tuned CCIR model gave an RMSE value of 9.23 dB which is above the acceptable upper limit of 6 dB for propagation loss prediction models. On the other hand, the PB-tuned CCIR model gave the best prediction result with an RMSE value of 2.177 dB and prediction accuracy of 98.11 % which is better than the performance of all the RMSE-tuned CCIR models. The results showed that apart from using an RMSE value to tune the CCIR propagation loss model, adjustment of some other key parameters of the model can as well provide a better prediction performance. However, the choice of the parameter to be tuned depends on the specific nature of the case study area.

Measurement of UHF Signal Propagation Loss under Different Altitude in Hilly Environment

In this paper, the effect of environment and altitude on ultrahigh frequency (UHF) band has been studied. Both theoretical study and experimental investigations are conducted in order to model and characterize such complex communication medium. The experimental study is primarily aimed at critically analyzing the detrimental impact of channel condition on received power in hilly environment under different altitudes. The propagated signal strength varies from one place to another due to time varying channel condition as a result of obstacles between the transmitter and receiver. There is dramatic need to experimentally investigate such scenario in order to provide an avenue through which service providers can strategies their policies for effective wireless communication. Developing such channel models is extremely useful in communication systems design and simulation. The proposed model has been compared with ITU-R model for verification of the develop model for propagation loss prediction in hilly environment. The results obtained are compared with different measurement at various altitudes in hilly terrain environment.

STUDY OF PROPAGATION LOSS PREDICTION IN FOREST ENVIRONMENT

A comprehensive review of radio wave attenuation in forest environments is presented in this paper. The classic analytical methods of propagation loss modeling and prediction are described flrst. This provides information on the physical processes that the radio waves undergo while propagating through a forest. The focus of this paper is on the review and summary of the experimental work done in this area and the development of empirical propagation loss prediction models. The propagation loss variation due to external factors such as antenna height-gain, depolarization, humidity efiect etc. are examined and discussed individually. In view of current research work done in this area, some possible future work is proposed to improve the performance of radio links in forest environment.

Comments on the hybrid model for propagation loss prediction in tunnels

The hybrid model for propagation-loss prediction in tunnels is discussed and its limitations are given. The values from the waveguide model with /spl gamma//sub 2/<2 are applicable for only short distances and for tunnels with low specific attenuation factor (/spl alpha/). In general, /spl gamma//sub 2/ can take any value higher than two.

Novel model for propagation loss prediction in tunnels

Radio signal propagation in a tunnel exhibits distinct near and far regions with quite different propagation characteristics. This paper proposes a model that can distinguish these propagation regions and predict their respective propagation losses in the tunnel. The model relies on a break point to separate the propagation regions and a hybrid technique to calculate the propagation losses. The location of the break point is determined with the solution of a novel tunnel-propagation equation for the first time. The solution shows that the location of the break point depends strongly upon frequency, antenna position, and tunnel transversal dimensions. The model is compared with data measured in various tunnels at different frequencies (900 MHz, 1.8 GHz, and 2.448 GHz). The results show reasonable agreement between predictions and measurements.

Enhancement of waveguide model for propagation‐loss prediction in tunnels

AbstractFor microcellular system design, we have enhanced a well‐known waveguide model for propagation‐loss prediction along an LOS propagation path in a tunnel. It is reported in this letter. The enhancement involves a more accurate calculation of the tilt loss, the distinction of the two propagation regions, and the suggestion of using the free‐space model for one region and the waveguide model for the other. The enhancement is demonstrated with examples. © 2001 John Wiley &amp; Sons, Inc. Microwave Opt Technol Lett 30: 10–12, 2001.