Browsing by Author "Kolo J.G"

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HYBRID AUTOREGRESSIVE NEURAL NETWORK (ARNN) MODEL FOR SPECTRUM OCCUPANCY PREDICTION
(NJEAS, 2022) Ajiboye, Johnson Adegbenga; Adegboye B.A; Aibinu A.M; Kolo J.G; Ajiboye M.A; Usman A.U
A secondary spectrum user cannot transmit in a channel before sensing and knowing the spectrum occupancy state as this may cause interference. This poses a major challenge because these operations ought to be performed in each time slot and thereby causing a substantial delay before the user gains access to the spectrum, leading to inefficient utilization. Therefore, a channel predictive system will mitigate this problem. In this work, an ensemble machine learning model for spectrum occupancy prediction was developed. The developed model was trained using a sample of Power Spectrum Density (PSD) data collected from the field for a period of twenty four hours within a frequency range of 30-300 MHz. The frequency range was grouped into sub bands. Based on the training data and the corresponding output data, the neural network model trains itself to come up with the best weights which can generally be used by the AR model for unseen data. After computing the weights, the performance was first tested on the entire training data, on the validation dataset and on the test dataset. Prediction results revealed an overall accuracy of 98.32% with band 4 (74.85-87.45 MHz) having the highest accuracy of 99.01% and the lowest accuracy of 89.39% in band 2 (47.05-68 MHz).
One–Bit Compressive Sensing Algorithm for Wideband Spectrum Sensing: A Review
(I3C 2024, 2024-04-22) Majin R.N; Kolo J.G; Dauda U.S; Ajiboye, Johnson Adegbenga
Spectrum sensing (SS) is one of the most important techniques in wireless communication for understanding the radio environment. SS techniques, however, are ineffective since they require costly, impractical high-rate analog-to-digital converters (ADCs) for timely communications. However, there are challenges and limitations in traditional spectrum sensing techniques (SSTs), including estimation of sparsity level, selection of the number of measurements, noise uncertainty, receiver uncertainty, sensitivity at low signal-to-noise-ratio (SNR) values, and interference from channel coding. One-bit compression (CS) has been seen as a promising sensing technique that allows extremely easy, efficient, and fast sampling and quantization for wideband spectrum sensing. It can be used in cognitive radio (CR) communication by making use of sparsity in spectrum occupancy brought about by underutilization of the spectrum. In this paper, we provide an overview of compressive spectrum sensing (CSS) algorithms in wideband CR, the current state-of-the-art of CSS in wideband spectrum sensing (WBSS) communication, and its advantages and limitations.
Spectrum Occupancy Measurement in the VHF Band- Results and Evaluation in the Context of Cognitive Radio
(International Engineering Conference (IEC) 2019, 2019) Ajiboye, Johnson Adegbenga; Adegboye B.A; Aibinu A.M; Kolo J.G
In this paper the results of spectrum occupancy survey in the context of Cognitive Radio was presented. In Cognitive Radio, secondary unlicensed users are allowed to opportunistically use the primary licensed users’ bands with the understanding that there will be no interference i.e Secondary Users (SU) quits at the arrival of Primary Users (PU). A 24-hour measurement survey was carried out at the centre of Minna metropolis in Niger State, Nigeria covering a frequency range of VHF (30-300MHz). Aaronia HF 6065 V4 spectrum analyzer was used for data collection. Results show that the band allocated for Aeronautical Navigation has the highest spectral occupancy of 39.83% followed by the FM band with occupancy of 12.90% while the frequency band meant for Aeronautical Mobile and Space Operation has occupancy of 4.73% and TV Broadcasting 0.09%. The average occupancy of the VHF band is 14.39%.
Wireless Sensor Networks: State of Arts
(IEC 2023, 2023-03-21) Okafor A.C; Dauda U.S; Kolo J.G; Ohize H.O; Ajiboye, Johnson Adegbenga
The enhancement of the Wireless Sensor Network (WSN) have made technology much easier and is impacting on all fields of endeavours. The invention of WSN and the present advancement especially in the field of Engineering is the main focus of this article. The paper has also enlightened the current state of the WSN in data collection procedure. The marketplace of the WSN and its growth has been elaborated in the paper along with the future scope and challenges.