论文标题
通过监督学习方法的非合并OK符号检测BCC
Noncoherent OOK Symbol Detection with Supervised-Learning Approach for BCC
论文作者
论文摘要
人们一直在不断提高人体或周围使用的医疗设备的准确性和易用性。沟通对医疗应用至关重要，无线身体区域网络（WBAN）有可能革新诊断。尽管它的重要性，WBAR技术仍处于起步阶段，并且需要大量研究。我们考虑使用整个身体以及皮肤作为通信的媒介的身体通道通信（BCC）。 BCC对人体的自然循环和运动很敏感，这需要无线通信的非合并模型。为了准确处理在人体内部或人体内部工作的电子设备的实用应用，我们配置了具有On-Off Keying（OOK）调制的BCC的现实系统模型。我们提出了针对OOK符号的新型检测技术，并通过利用分布式接收和监督学习方法来提高性能。数值结果表明，所提出的技术对于BCC的非合并OOK传输有效。
There has been a continuing demand for improving the accuracy and ease of use of medical devices used on or around the human body. Communication is critical to medical applications, and wireless body area networks (WBANs) have the potential to revolutionize diagnosis. Despite its importance, WBAN technology is still in its infancy and requires much research. We consider body channel communication (BCC), which uses the whole body as well as the skin as a medium for communication. BCC is sensitive to the body's natural circulation and movement, which requires a noncoherent model for wireless communication. To accurately handle practical applications for electronic devices working on or inside a human body, we configure a realistic system model for BCC with on-off keying (OOK) modulation. We propose novel detection techniques for OOK symbols and improve the performance by exploiting distributed reception and supervised-learning approaches. Numerical results show that the proposed techniques are valid for noncoherent OOK transmissions for BCC.
