Abstract
With the explosive development of wireless communication and low power embedded techniques, Body Area Network (BAN) has opened up new frontiers in the race to provide real-time health monitoring. IEEE 802 has established a Task Group called IEEE 802.15.6 inNovember 2007 and aims to establish a communication standard optimized for low power, high reliability applied to medical and non-medical application for BANs. This paper overviews the path loss model and the communication scheme for implant-to-body surface channel presented by IEEE 802.15.6 standard. Comparing with the standard scheme where BCH (Bose-Chaudhuri-Hochquenghem) code is employing, we propose a new coding solution using convolutional code operating with Bit Interleaver based on the properties of implant-to-body surface channel. To analyze the performance of the two Error Correct Coding (ECC) schemes, we performed simulations in terms of Bit Error Rate (BER) and power consumption on MATLAB and FPGA platform, respectively. The simulation results proved that with appropriate constraint length, convolutional code has a better performance not only in BER, but also in minimization of resources and power consumption.
Highlights
IEEE 802 has established a Task Group called IEEE 802.15.6 in November 2007 and aims to establish a communication standard optimized for low power, high reliability applied to medical and non-medical application for Body Area Network (BAN)
Comparing with the standard scheme where BCH (Bose-Chaudhuri-Hochquenghem) code is employing, we propose a new coding solution using convolutional code operating with Bit Interleaver based on the properties of implant-to-body surface channel
As the advent of miniaturized sensors and actuators for monitoring, diagnostic and therapeutic, Body Area Networks (BANs) which operates in close vicinity to, on, or inside a human body has emerged as a key technology and has great potential to revolutionize the future of healthcare technology
Summary
As the advent of miniaturized sensors and actuators for monitoring, diagnostic and therapeutic, Body Area Networks (BANs) which operates in close vicinity to, on, or inside a human body has emerged as a key technology and has great potential to revolutionize the future of healthcare technology. Group Manchester code (GM) modulation scheme for medical in-body BAN systems is proposed in [5]. Nel model in its final report [13], and proposes a scheme for the data transmission [14] These papers and technical reports for IEEE 802.15.6, for example [15], consider an Additive White Gaussian Noise (AWGN) channel, which will cause random errors for implanted applications. Convolutional code is used to correct the random errors, bit interleaving to correct burst errors instead of BCH code to achieve high reliability The performance of these two coding scheme is measured and compared in power efficiency and reliability, which are estimated by power consumption or complexity of decoding and BER performance, respectively.
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