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There has been a continuing demand for improving the accuracy and ease of useof medical devices used on or around the human body. Communication is criticalto medical applications, and wireless body area networks (WBANs) have thepotential to revolutionize diagnosis. Despite its importance, WBAN technologyis still in its infancy and requires much research. We consider body channelcommunication (BCC), which uses the whole body as well as the skin as a mediumfor communication. BCC is sensitive to the body's natural circulation andmovement, which requires a noncoherent model for wireless communication. Toaccurately handle practical applications for electronic devices working on orinside a human body, we configure a realistic system model for BCC with on-offkeying (OOK) modulation. We propose novel detection techniques for OOK symbolsand improve the performance by exploiting distributed reception andsupervised-learning approaches. Numerical results show that the proposedtechniques are valid for noncoherent OOK transmissions for BCC.

Noncoherent OOK Symbol Detection with Supervised-Learning Approach for BCC