A Low Complex Spectrum Sensing Technique for Medical Telemetry System

Abstract

Wearable wireless sensors play a vital role in healthcare applications to connect remote patients with the hospital. Generally, wearable devices are used for monitoring, diagnosing, and treating various medical conditions. In this paper authors propose a novel energy detection scheme for spectrum allotment to a medical telemetry network. By using medical body area networks, we can improve remote patient monitoring as well as facilitating immediate response from the service provider. Further, we also outline the challenges of implementing spectrum sensing for body sensor networks. In this work, spectrum sensing using energy detection is used for developing medical telemetry networks. The proposed Normalized Median Least Mean Square (NMLMS) algorithm with sign regressor operation also solves the problem of complexity of circuit in basic spectrum sensing using energy detection phenomenon. In practical communication networks, the computational complexity for implementing the proposed technique on chip is a key parameter for developing lab on chip or system on chip. The experimental results show that performance of NMLMS gives better performance in terms of convergence. The probability of detection of a spectrum are demonstrated at various false alarm rates as 0.025, 0.05, and 0.1 and signal to noise ratio from −10 dB to 0 dB.