  Medical applications have driven many areas of engineering to optimize
diagnostic capabilities and convenience. In the near future, wireless body area
networks (WBANs) are expected to have widespread impact in medicine. To achieve
this impact, however, significant advances in research are needed to cope with
the changes of the human body's state, which make coherent communications
difficult or even impossible. In this paper, we consider a realistic
noncoherent WBAN system model where transmissions and receptions are conducted
without any channel state information due to the fast-varying channels of the
human body. Using distributed reception, we propose several symbol detection
approaches where on-off keying (OOK) modulation is exploited, among which a
supervised-learning-based approach is developed to overcome the noncoherent
system issue. Through simulation results, we compare and verify the performance
of the proposed techniques for noncoherent WBANs with OOK transmissions. We
show that the well-defined detection techniques with a
supervised-learning-based approach enable robust communications for noncoherent
WBAN systems.
