There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

One of the most widely used statistical techniques is simple linear regression. This technique is used to relate a measured response variable, Y, to a single measured predictor (explanatory) variable, X, by means of a straight line. It uses the principle of least squares to come up with values of the “best” slope and intercept for a straight line that approximates the relationship. By means of the example below we motivate the technique, indicate the rationale underlying the calculations and come up with the formulas for the “best” slope and intercept based on the data.

Simple Linear Regression

The next generation (NG) optical technologies will unveil certain unique features, namely ultra-high data rate, broadband multiple services, scalable bandwidth, and flexible communications for manifold end-users. Among the optical technologies, free space optical (FSO) technology is a key element to achieve free space data transmission according to the requirements of the future technologies, which is due to its cost effective, easy deployment, high bandwidth enabler, and high secured. In this article, we give the overview of the recent progress on FSO technology and the factors that will lead the technology towards ubiquitous application. As part of the review, we provided fundamental concepts across all types of FSO system, including system architecture comprising of single beam and multiple beams. The review is further expanded into the investigation of rain and haze effects toward FSO signal propagation. The final objective that we cover is the scalability of an FSO network via the implementations of hybrid multi-beam FSO system with wavelength division multiplexing (WDM) technology.

/pdf/a-survey-of-free-space-optics-fso-communication-systems-oxtmcfz0h4.pdf

A Survey of Free Space Optics (FSO) Communication Systems, Links, and Networks

The most classical approach of determining rain attenuation for radio-wave frequency has been to theoretically determine the specific attenuation. At frequency over 10 GHz, rain and precipitation can influence the attenuation a lot; the effect of atmospheric attenuation between the source and destination over wireless communication is of major concern and a proper site visit and proper method are required to control the attenuation level so that the performance can be increased. In this paper exponential model has been used to determine the attenuation level for k-region (India) which can be used for region having similar condition. The analyzed predicted attenuation data have been compared with ITU-R measured rain attenuation, and the results will provide useful estimation of rainfall attenuation on microwave links in tropical regions that have similar conditions as (Almora) Uttarakhand region.

/pdf/prediction-of-rain-attenuation-and-impact-of-rain-in-wave-1rno84o3em.pdf

Prediction of Rain Attenuation and Impact of Rain in Wave Propagation at Microwave Frequency for Tropical Region (Uttarakhand, India)

It is very important to understand the input features and the neural network parameters required for optimal path loss prediction in wireless communication channels. In this paper, an extensive investigation was conducted to determine the most appropriate neural network parameters for path loss prediction in Very High Frequency (VHF) band. Field measurements were conducted in an urban propagation environment to obtain relevant geographical and network information about the receiving mobile equipment and quantify the path losses of radio signals transmitted at 189.25 MHz and 479.25 MHz. Different neural network architectures were trained with varying kinds of input parameters, number of hidden neurons, activation functions, and learning algorithms to accurately predict corresponding path loss values. At the end of the experimentations, the performance of the developed Artificial Neural Network (ANN) models are evaluated using the following statistical metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Standard Deviation (SD) and Regression coefficient (R). Results obtained show that the ANN model that yielded the best performance employed four input variables (latitude, longitude, elevation, and distance), nine hidden neurons, hyperbolic tangent sigmoid (tansig) activation function, and the Levenberg-Marquardt (LM) learning algorithm with MAE, MSE, RMSE, SD and R values of 0.58 dB, 0.66 dB, 0.81 dB, 0.56 dB and 0.99 respectively. Finally, a comparative analysis of the developed model with Hata, COST 231, ECC-33 and Egli models showed that ANN-based path loss model has better prediction accuracy and generalization ability than the empirical models.

/pdf/determination-of-neural-network-parameters-for-path-loss-3zgw4egfwu.pdf

Determination of Neural Network Parameters for Path Loss Prediction in Very High Frequency Wireless Channel

Rain rate and rain attenuation predictions are one of the vital steps to be considered when analyzing a microwave satellite communication links at the Ku and Ka bands. In this paper, tools for the prediction of rain rate and rain attenuation are presented in the form of contour maps for Nigeria using a massive rainfall data bank of 30 years which are taken from measurements made from the coast to the arid region of Nigeria. Rain-rate maps for the country of Nigeria were developed using the models purposely designed for tropical zones while ITU-R models were used for the rain-attenuation maps. The information from these maps will be a good preliminary design tools for both terrestrial and earth-satellite microwave links and also provide a broad idea of rain attenuation for microwave engineers

/pdf/rain-rate-and-rain-attenuation-prediction-for-satellite-45j4f2kug0.pdf

Rain Rate and Rain Attenuation Prediction for Satellite Communication in Ku and Ka Bands Over Nigeria.

In the planning and design of microwave communication links, the structure of the radio refractive index in the lower part of the atmospheric boundary layer is very important. In this work, measurements of atmospheric pressure, temperature and relative humidity were made in Akure (7.15 ◦ N, 5.12 ◦ E), South Western Nigeria. Wireless weather stations (Integrated Sensor Suite, ISS) were'positioned at five different height levels beginning from the ground surface and at intervals of 50 m from the ground to a height of 200 m (0, 50, 100, 150 and 200 m) on a 220 m Nigeria Television Authority TV tower at Iju in Akure North Local Government area of Ondo State. The measurement of the atmospheric variables was made every 30 minutes everyday. The study utilized the data for the first year of measurement (January-December 2007) to compute the radio refractivity and its refractivity gradient in Akure. From these parameters, the vertical distributions of radio refractivity are then determined. The results obtained show that the propagation conditions have varying degree of occurrence with sub-refractive conditions observed to be prevalent between January-July while Super-refraction and Ducting were observed mostly between August-December.

https://www.jpier.org/pierc/pierc04/12.08082104.pdf

Vertical Profile of Radio Refractivity Gradient in Akure South-West Nigeria.

Artificial Neural Network model for the determination of GSM Rxlevel from atmospheric parameters

Propagation path loss models are useful for the prediction of received signal strength at a given distance from the transmitter; estimation of radio coverage areas of Base Transceiver Stations (BTS); frequency assignments; interference analysis; handover optimisation; and power level adjustments. Due to the differences in: environmental structures; local terrain profiles; and weather conditions, path loss prediction model for a given environment using any of the existing basic empirical models such as the Okumura-Hata’s model has been shown to differ from the optimal empirical model appropriate for such an environment. In this paper, propagation parameters, such as distance between transmitting and receiving antennas, transmitting power and terrain elevation, using sea level as reference point, were used as inputs to Artificial Neural Network (ANN) for the development of an ANN based path loss model. Data were acquired in a drive test through selected rural and suburban routes in Minna and environs as dataset required for training ANN model. Multilayer perceptron (MLP) network parameters were varied during the performance evaluation process, and the weight and bias values of the best performed MLP network were extracted for the development of the ANN based path loss models for two different routes, namely rural and suburban routes. The performance of the developed ANN based path loss model was compared with some of the existing techniques and modified techniques. Using Root Mean Square Error (RMSE) obtained between the measured and the model outputs as a measure of performance, the newly developed ANN based path loss model performed better than the basic empirical path loss models considered such as: Hata; Egli; COST-231; Ericsson models and modified path loss approach.

M. O. Ajewole

Papers

Rain Rate and Rain Attenuation Prediction for Satellite Communication in Ku and Ka Bands Over Nigeria.

Vertical Profile of Radio Refractivity Gradient in Akure South-West Nigeria.

Artificial Neural Network model for the determination of GSM Rxlevel from atmospheric parameters

Comparative Analysis of Basic Models and Artificial Neural Network Based Model for Path Loss Prediction

Investigation of path loss and modeling for digital terrestrial television over Nigeria