Genetic Algorithm-Holt-Winters Based Minute Spectrum Occupancy Prediction: An Investigation

In this research, the suitability of a genetic algorithm (GA) modified Holt-Winters (HW) exponential model for the prediction of spectrum occupancy data was investigated. Firstly, a description of spectrum measurement that was done during a two-week duration at locations (8.511 °N, 4.594 °E) and (8.487 °N, 4.573 °E) of the 900 MHz and 1800 MHz bands is given. In computing the spectrum duty cycle, different decision thresholds per band link were employed due to differing noise levels. A frequency point with a power spectral density less than the decision threshold was considered unoccupied and was assigned a value of 0, while a frequency point with a power spectral density larger than the decision threshold was considered occupied and was assigned a value of 1. Secondly, the spectrum duty cycle was used in the evaluation of the forecast behavior of the forecasting methods. The HW approach uses exponential smoothing to encode the spectrum data and uses them to forecast typical values in present and future states. The mean square error (MSE) of prediction was minimized using a GA by iteratively adjusting the HW discount factors to improve the forecast accuracy. A decrease in MSE of between 8.33 to 44.6% was observed

Genetic Algorithm-Holt-Winters Based Minute Spectrum Occupancy Prediction: An Investigation

In this research, the suitability of a genetic algorithm (GA) modified Holt-Winters (HW) exponential model for the prediction of spectrum occupancy data was investigated. Firstly, a description of spectrum measurement that was done during a two-week duration at locations (8.511 °N, 4.594 °E) and (8.487 °N, 4.573 °E) of the 900 MHz and 1800 MHz bands is given. In computing the spectrum duty cycle, different decision thresholds per band link were employed due to differing noise levels. A frequency point with a power spectral density less than the decision threshold was considered unoccupied and was assigned a value of 0, while a frequency point with a power spectral density larger than the decision threshold was considered occupied and was assigned a value of 1. Secondly, the spectrum duty cycle was used in the evaluation of the forecast behavior of the forecasting methods. The HW approach uses exponential smoothing to encode the spectrum data and uses them to forecast typical values in present and future states. The mean square error (MSE) of prediction was minimized using a GA by iteratively adjusting the HW discount factors to improve the forecast accuracy. A decrease in MSE of between 8.33 to 44.6% was observed

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

Equalization of DS-UWB Systems using Genetic Algorithm with Adaptive Parameters

We use adaptive generation along with some other parameters to investigate their effects on the performance of Genetic Algorithm (GA) in comparison to a previous work, where the output of a RAKE receiver is utilized as the input to a GA so as to reduce the inter-symbol interference (ISI) due to the frequency selectivity of UWB channels because of the very high rate of transmission. The effects of two different scaling methods and two mutation types, on the performance of a GA when used with a receiver for the equalization of the channel of a direct sequence ultra wideband (DS-UWB) wireless communications system are presented. The results show that fitness scaling has effects on GA based optimization while mutation prevents local convergence

Equalization of DS-UWB Systems using Genetic Algorithm with Adaptive Parameters

We use adaptive generation along with some other parameters to investigate their effects on the performance of Genetic Algorithm (GA) in comparison to a previous work, where the output of a RAKE receiver is utilized as the input to a GA so as to reduce the inter-symbol interference (ISI) due to the frequency selectivity of UWB channels because of the very high rate of transmission. The effects of two different scaling methods and two mutation types, on the performance of a GA when used with a receiver for the equalization of the channel of a direct sequence ultra wideband (DS-UWB) wireless communications system are presented. The results show that fitness scaling has effects on GA based optimization while mutation prevents local convergence

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