Browsing by Author "Onumanyi, A. J."

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    Development of hybrid artificial intelligent based handover decision algorithm
    (Elsevier, 2017-04-01) Aibinu, A. M.; Onumanyi, A. J.; Adedigba, A. P.; Ipinyomi, M.; Folorunso, T. A.; Salami, Momoh-Jimoh E.
    The possibility of seamless handover remains a mirage despite the plethora of existing handover algorithms. The underlying factor responsible for this has been traced to the Handover decision module in the Handover process. Hence, in this paper, the development of novel hybrid artificial intelligent handover decision algorithm has been developed. The developed model is made up of hybrid of Artificial Neural Network (ANN) based prediction model and Fuzzy Logic. On accessing the network, the Received Signal Strength (RSS) was acquired over a period of time to form a time series data. The data was then fed to the newly proposed ahead ANN-based RSS prediction system for estimation of prediction model coefficients. The synaptic weights and adaptive coefficients of the trained ANN was then used to compute the ahead ANN based RSS prediction model coefficients. The predicted RSS value was later codified as Fuzzy sets and in conjunction with other measured network parameters were fed into the Fuzzy logic controller in order to finalize handover decision process. The performance of the newly developed ahead ANN based RSS prediction algorithm was evaluated using simulated and real data acquired from available mobile communication networks. Results obtained in both cases shows that the proposed algorithm is capable of predicting ahead the RSS value to about ±0.0002 dB. Also, the cascaded effect of the complete handover decision module was also evaluated. Results obtained show that the newly proposed hybrid approach was able to reduce ping-pong effect associated with other handover techniques.
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    A modified Otsu’s algorithm for improving the performance of the energy detector in cognitive radio
    (Urban & Fischer, 2017-09-01) Onumanyi, A. J.; Onwuka, E. N.; Aibinu, A. M.; Ugweje, O. C.; Salami, Momoh-Jimoh E.
    In this paper, we present a modified Otsu’s algorithm for solving the automatic threshold estimation problem in energy detection based Cognitive Radio (CR) application. The modified algorithm was tested extensively and compared with other known algorithms using both simulated and real datasets. In particular, our findings reveal that the modified algorithm provides an averagely lower false alarm rate than the other techniques compared with in this paper. Furthermore, the results obtained show that the algorithm is independent of the bandwidth’s size, while having a total complexity of O (V), where V is the total sample size. Thus, from the results of this paper, full and effective automatic blind spectrum sensing using an Energy Detector is possible in CR. This can be achieved at a Signal-to-Noise Ratio of 5 dB to meet the IEEE 802.22 draft standard of P D> 90% and P FA< 10%.
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    A real valued neural network based autoregressive energy detector for cognitive radio application
    (Hindawi, 2014) Onumanyi, A. J.; Onwuka, E. N.; Aibinu, A. M.; Ugweje, O. C.; Salami, Momoh-Jimoh E.
    A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application. This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system. By using appropriate modules and a well-designed detector, the power spectral density (PSD) of the AR system transfer function was estimated and subsequent receiver operating characteristic (ROC) curves of the detector generated and analyzed. A high detection performance with low false alarm rate was observed for varying signal to noise ratio (SNR), sample number, and model order conditions. The proposed RVNN based ED was then compared to the simple periodogram (SP), Welch periodogram (WP), multitaper (MT), Yule-Walker (YW), Burg (BG), and covariance (CV) based ED techniques. The proposed detector showed better performance than the SP, WP, and MT while providing better false alarm performance than the YW, BG, and CV. Data provided here support the effectiveness of the proposed RVNN based ED for CR application.