Energy Detection in Medical Telemetry Systems using Logarithmic Adaptive Algorithm
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2020 by IJETT Journal|
|Year of Publication : 2020|
|Authors : Md. Zia Ur Rahman, S. Akanksha, R.P. Krishna Kalyan, S. Nayeem
|DOI : 10.14445/22315381/IJETT-V68I9P209|
MLA Style: Md. Zia Ur Rahman, S. Akanksha, R.P. Krishna Kalyan, S. Nayeem "Energy Detection in Medical Telemetry Systems using Logarithmic Adaptive Algorithm" International Journal of Engineering Trends and Technology 68.9(2020):49-56.
APA Style:Md. Zia Ur Rahman, S. Akanksha, R.P. Krishna Kalyan, S. Nayeem. Energy Detection in Medical Telemetry Systems using Logarithmic Adaptive Algorithm International Journal of Engineering Trends and Technology, 68(9),49-56.
In cognitive radio, spectrum sensing is one of the key issues. It prevents the harmful interference with the licensed users and it has to improve the spectrum’s utilization, for that it has to identify the available spectrum. Spectrum sensing in the cognitive radio systems enables to detect the unused portions of radio spectrum. The patient isn’t treated in time in real time scenario, if he is far away from hospital. Medical telemetry network plays a major role for this type of cases. Telemetry is mainly useful for the patients who are at a risk of abnormal heart activity. In wireless sensor networks, medical body area networks (MBAN) is a human-centric application which has more significance. For spectrum sensing, energy detection is mostly used technique. Energy detection doesn’t need of any previous data for aspect of primary user (PU) signal. In telemetry network problems due to the energy detection can be solved by proposed Error Normalized Least Mean Logarithmic Square (ENLMLS) methods. Results shows that the performance of dynamic selection of threshold which measures noise level of the signal in received signal gives better simulation in terms of increasing probability detection and decreasing false alarm.
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Cognitive radio, Energy detection, Health Care Monitoring, Medical Telemetry, probability detection, false alarm, Spectrum sensing, Threshold.