Path Loss Predictions in the VHF and UHF Bands Within Urban Environments: Experimental Investigation of Empirical, Heuristics and Geospatial Models

(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] Deep knowledge of how radio waves behave in a practical wireless channel is required for effective planning and deployment of radio access networks in urban environments. Empirical propagation models are popular for their simplicity, but they are prone to introduce high prediction errors. Different heuristic methods and geospatial approaches have been developed to further reduce path loss prediction error. However, the efficacy of these new techniques in built-up areas should be experimentally verified. In this paper, the efficiencies of empirical, heuristic, and geospatial methods for signal fading predictions in the very high frequency (VHF) and ultra-high frequency (UHF) bands in typical urban environments are evaluated and analyzed. Electromagnetic field strength measurements are performed at different test locations within four selected cities in Nigeria. The data collected are used to develop path loss models based on artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and Kriging techniques. The prediction results of the developed models are compared with those of selected empirical models and field measured data. Apart from Egli and ECC-33, the root mean squared error (RMSE) produced by all other models under investigation are considered acceptable. Specifically, the ANN and ANFIS models yielded the lowest prediction errors. However, the empirical models have the lowest standard deviation errors across all the bands. The findings of this study will help radio network engineers to achieve efficient radio coverage estimation; determine the optimal base station location; make a proper frequency allocation; select the most suitable antenna; and perform interference feasibility studies.This work was supported jointly by the funding received from IoT-Enabled Smart and Connected Communities (SmartCU) Research Cluster and the Center for Research, Innovation and Discovery (CUCRID) of Covenant University, Ota, Nigeria.Faruk, N.; Popoola, SI.; Surajudeen-Bakinde, NT.; Oloyede, AA.; Abdulkarim, A.; Olawoyin, LA.; Ali, M.... (2019). Path Loss Predictions in the VHF and UHF Bands Within Urban Environments: Experimental Investigation of Empirical, Heuristics and Geospatial Models. IEEE Access. 7:77293-77307. https://doi.org/10.1109/ACCESS.2019.2921411S7729377307

Spatial Variability Study of Duty Cycle in GSM 900 and 1800 MHz Bands in Rural and Urban Environments

This paper examines the spatial variability of duty cycle in the GSM 900 and 1800 MHz bands within Kwara State, Nigeria. The results show spatial variance in the duty cycle with average occupancies of 1.67%, 17.76%, 10.55% and 0.39%, 11.00% and 5.11 in the rural, urban and all locations for 900 and 1800 MHz bands. Findings also show that there is very high positive correlation between rural 900/1800 MHz and urban 900/1800 MHz. But very high negative correlations exits between urban 900 and rural 1800, and urban 1800 and rural 1800. There is a weak and negative correlation between rural and urban 900 MHz, rural-urban 1800. These results clearly showthe abundance of unutilised spectrum within the GSM bands. Therefore, regulatory commissions should adopt flexible spectrum reuse strategy to relax the regulatory bottlenecks to maximize the scarce radio resources in the licensed bands, especially for rural network deployment

Large-scale radio propagation path loss measurements and predictions in the VHF and UHF bands

For decades now, a lot of radio wave path loss propagation models have been developed for predictions across different environmental terrains. Amongst these models, empirical models are practically the most popular due to their ease of application. However, their prediction accuracies are not as high as required. Therefore, extensive path loss measurement data are needed to develop novel measurement-oriented path loss models with suitable correction factors for varied frequency, capturing both local terrain and clutter information, this have been found to be relatively expensive. In this paper, a large-scale radio propagation path loss measurement campaign was conducted across the VHF and UHF frequencies. A multi-transmitter propagation set-up was employed to measure the strengths of radio signals from seven broadcasting transmitters (operating at 89.30, 103.5, 203.25, 479.25, 615.25, 559.25 and 695.25 MHz respectively) at various locations covering a distance of 145.5 km within Nigerian urban environments. The measurement procedure deployed ensured that the data obtained strictly reflect the shadowing effects on radio signal propagation by filtering out the small-scale fading components. The paper also, examines the feasibilities of applying Kriging method to predict distanced-based path losses in the VHF and UHF bands. This method was introduced to minimize the cost of measurements, analysis and predictions of path losses in built-up propagation environment

Measuring the impact of the digital economy in developing countries: A systematic review and meta- analysis

The digital economy, driven by Information and Communication Technology (ICT), has emerged as a significant contributor to economies worldwide. However, accurately defining and measuring its impact on national economies remains a complex endeavor. This paper explores the definition, measurement, role, and impacts of the digital economy across various economies. It also examines the involvement of governments and telecommunication regulators in assessing the digital economy and identifies future directions for developing countries. A systematic literature review utilizing the PRISMA Model is employed to investigate the factors and indices used to measure the digital economy. The findings highlight ongoing efforts to harmonize the definition and metrics; nonetheless, challenges persist due to the scarcity of appropriate datasets and variations in country-specific definitions. Additionally, the effectiveness of existing digital economy indices and toolkits in assessing the level of digitalization in developing countries is evaluated. The paper concludes that despite ongoing efforts to bridge the gaps, the concept of the digital economy remains defined and measured differently, necessitating a new definition that accounts for various contextual peculiarities. Furthermore, a roadmap is proposed to develop a toolkit that ensures comprehensive measurement, thus preventing an underestimation of the digital economy's contribution to the Gross Domestic Product (GDP) in developing countries. The paper underscores the need for international and multi-stakeholder dialogue to establish a common understanding of the digital economy's definition and measurement. Developing countries, such as Nigeria, are urged to develop or adopt new metrics tailored to their unique circumstances, facilitating an accurate and efficient quantification of the digital economy's impact on crucial indicators like GDP. Improved statistical data collection and recording methodologies are recommended for both governments and the private sector. Moreover, the paper advocates for the establishment of a Digital Economy Advisory Board (DEAB) in developing countries to maximize the benefits of the ongoing global transition to the digital economy

Farmers' perceived effects of climate change in cocoa production in Kwara State

The effects of climate change in Nigeria are already being felt as the frequency and intensity of extreme events like droughts and floods have increased. In mature cocoa plants, water deficit and excess result in low yield and increase the level of capsid and black-pod damage. Cocoa seedling mortality is encouraged by prolonged dry season due to changes in climate. The study investigated the perceived effects of climate change on cocoa seedling growth, flowering, pod development, yield, leaves, tree, insect pest and disease occurrence. It examined perceived climatic factors and amount realized from cocoa beans in Kwara State. A multi-stage random sampling procedure was used to select 60 cocoa farmers from three out of eight Local Government Areas producing cocoa in the study area. Interview schedule instrument was used in data collection and analyzed with descriptive statistics and correlation analysis. The results revealed that most (60%) of the farmers believed climate change retards cocoa seedling growth and lead to death. Half (50%) of them opined that climate change delays flowering of cocoa. Majority (70%) perceived it to delay pod development resulting in low yield. In terms of insect pest and diseases occurrence, 51.7 percent indicated that climate change altered crop yield and losses in cocoa farms. Many farmers (58.3%) feel it makes cocoa leaves to be abundant while more of them 65% claimed that climate change retards tree development for optimum production. However, 61.7% had high perception about the effect of climate change on cocoa production. Low Rainfall accounting for 61.7% was observed to be the main climatic factor contributing to climate change in the study area. The revenue realized from sales of cocoa beans was statistically related to farmers’ perceived effect of climate change on cocoa production (r = -0.412, p = 0.001 at p<0.05). The farmers’ perception of climate change is a reflection of the global problems posed by variations in weather impacting food production and income. There is need for concerted efforts that will mitigate the effects of climate change on cocoa production

Application of Computational Intelligence Algorithms in Radio Propagation: A Systematic Review and Metadata Analysis

The importance of wireless path loss prediction and interference minimization studies in various environments cannot be over-emphasized. In fact, numerous researchers have done massive work on scrutinizing the effectiveness of existing path loss models for channel modeling. The difficulties experienced by the researchers determining or having the detailed information about the propagating environment prompted for the use of computational intelligence (CI) methods in the prediction of path loss. This paper presents a comprehensive and systematic literature review on the application of nature-inspired computational approaches in radio propagation analysis. In particular, we cover artificial neural networks (ANNs), fuzzy inference systems (FISs), swarm intelligence (SI), and other computational techniques. The main research trends and a general overview of the different research areas, open research issues, and future research directions are also presented in this paper. This review paper will serve as reference material for researchers in the field of channel modeling or radio propagation and in particular for research in path loss prediction

Analysis of farmers’ Adaptation Strategies to Climate Change in Cocoa Production in Kwara State

Changing climate and weather patterns are predicted to have severe negative impacts on food production, food security and natural resources in the immediate and coming years. Climate change alters the development of cocoa pods, insect pests and pathogens which translate into lower crop yields and impact farm income. This study examined the climate change adaptation strategies of farmers on cocoa production practices. A multi-stage random sampling procedure was used to select 60 cocoa farmers from three out of eight Local Government Areas (LGAs) producing cocoa in Kwara State. Interview schedule was used in data collection and analyzed with descriptive statistics and correlation analysis. The results reveal that 60.7% of the farmers were male. Majority (59.9%) of the farmers were between middle and old age with farming experience of 21-30 years and farm size of mainly between 0.4-2.7 hectares. Most farmers (85%) observed an extension beyond the normal dry months of November to February. This situation could have some implications on cocoa production. The main climate change strategies adopted by farmers include praying for rain (86.7%), use of improved varieties, (81.7%), climate prediction (76.7%), changes in cropping pattern and agro-forestry (75% each), control of soil erosion (73.3%) and fertilizer application (60%). Inadequate irrigation, 41.7% and crop diversification, 36.7%; constituted minor strategies. The cocoa production practices still adopted were weeding, 98.3%; seedling planting, 96.7%; insect pest and diseases control, 95%; bush clearing, 93.3%; fermentation and drying, 91.7%; tree felling, 88.4%; pruning, 85% and burning before planting, 70%. However, Pearson product moment correlation coefficient (PPMC) showed that a significant relationship existed between age of cocoa farm (0.016) and cocoa production practices at P<0.05. Cocoa farmers’ climate change strategies (0.121) were not statistically related with cocoa production practices at P< 0.05. Most of the strategies and practices currently used by farmers should be improved upon to ensure appropriate agronomic practices and adaptation to changes in climate

Analysis of farmers’ Adaptation Strategies to Climate Change in Cocoa Production in Kwara State

Changing climate and weather patterns are predicted to have severe negative impacts on food production, food security and natural resources in the immediate and coming years. Climate change alters the development of cocoa pods, insect pests and pathogens which translate into lower crop yields and impact farm income. This study examined the climate change adaptation strategies of farmers on cocoa production practices. A multi-stage random sampling procedure was used to select 60 cocoa farmers from three out of eight Local Government Areas (LGAs) producing cocoa in Kwara State. Interview schedule was used in data collection and analyzed with descriptive statistics and correlation analysis. The results reveal that 60.7% of the farmers were male. Majority (59.9%) of the farmers were between middle and old age with farming experience of 21-30 years and farm size of mainly between 0.4-2.7 hectares. Most farmers (85%) observed an extension beyond the normal dry months of November to February. This situation could have some implications on cocoa production. The main climate change strategies adopted by farmers include praying for rain (86.7%), use of improved varieties, (81.7%), climate prediction (76.7%), changes in cropping pattern and agro-forestry (75% each), control of soil erosion (73.3%) and fertilizer application (60%). Inadequate irrigation, 41.7% and crop diversification, 36.7%; constituted minor strategies. The cocoa production practices still adopted were weeding, 98.3%; seedling planting, 96.7%; insect pest and diseases control, 95%; bush clearing, 93.3%; fermentation and drying, 91.7%; tree felling, 88.4%; pruning, 85% and burning before planting, 70%. However, Pearson product moment correlation coefficient (PPMC) showed that a significant relationship existed between age of cocoa farm (0.016) and cocoa production practices at P<0.05. Cocoa farmers’ climate change strategies (0.121) were not statistically related with cocoa production practices at P< 0.05. Most of the strategies and practices currently used by farmers should be improved upon to ensure appropriate agronomic practices and adaptation to changes in climate

Path loss predictions for multi-transmitter radio propagation in VHF bands using Adaptive Neuro-Fuzzy Inference System

Path loss prediction is an important process in radio network planning and optimization because it helps to understand the behaviour of radio waves in a specified propagation environment. Although several models are currently available for path loss predictions, the adoption of these models requires a trade-off between simplicity and accuracy. In this paper, a new path loss prediction model is developed based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) for multi-transmitter radio propagation scenarios and applicable to the Very High Frequency (VHF) bands. Field measurements are performed along three driving routes used for testing within the urban environment in Ilorin, Kwara State, Nigeria, to obtain the strength values of radio signals received from three different transmitters. The transmitters propagate radio wave signals at 89.3 MHz, 103.5 MHz, and 203.25 MHz, respectively. A simple five-layer optimized ANFIS network structure is trained based on the back-propagation gradient descent algorithm so that given values of input variables (distance and frequency) are correctly mapped to corresponding path loss values. The adoption of the Pi membership function ensures better stability and faster convergence at minimum epoch. The developed ANFIS-based path loss model produced a low prediction error with Root Mean Square Error (RMSE), Standard Deviation Error (SDE), and correlation coefficient (R) values of 4.45 dB, 4.47 dB, and 0.92 respectively. When the ANFIS-based model was deployed for path loss predictions in a different but similar propagation scenario, it demonstrated a good generalization ability with RMSE, SDE, and R values of 4.46 dB, 4.49 dB, and 0.91, respectively. In conclusion, the proposed ANFIS-based path loss model offers desirable advantages in terms of simplicity, high prediction accuracy, and good generalization ability, all of them critical features for radio coverage estimation and interference feasibility studies during multi-transmitter radio network planning in the VHF bands. Keywords: Path loss models, Adaptive Neuro-Fuzzy Inference System, Neural network, Radio wave propagatio