# Toward an Advanced Human Monitoring System Based on a Smart Body Area Network for Industry Use
## 4 Results and Discussion
### PHY Performance
#### 4.1.2 PLCP Header Detection Failure Ratio  
Figures 6-8 show the PLCP header detection failure ratio in the case of several modulation schemes under the AWGN channel, the IEEE model CM3 in the medium shadowing case and the IEEE model CM3 in the strong shadowing case, respectively, as a function of the energy per symbol to noise power spectral density (\(E_{s}/N_{0}\)). Equations (6) and (7) included a Gaussian random variable representing the effect of shadowing. In other words, the medium shadowing case meant that \(N=n_{k}=0\), while \(N=3\sigma_{N},\ n_{k}=-3\) in the strong shadowing case. In the medium shadowing case, the \(K\) factor's true value was calculated as 4.5 when \(d\cong 1.0\) m from (6) and (7). Similarly, that of the strong shadowing case was derived as 0.14 when \(d\cong 1.0\) m. In all modulation schemes, the proposed scheme using the SFD suppressed the detection failure ratio, rather than only the SmartBAN preamble. The reason for this was that the cross-correlation value of the sequences used in the SFDs was superior to that of the SmartBAN preamble. Regarding the proposed scheme using the SFD, there was almost no difference in performance, depending on the sequences. In addition, performances less than \(10^{-4}\) were obtained by improving the \(E_{s}/N_{0}\) value. On the other hand, the performance of the detection failure rate converged  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sequence Type** & **Bit Sequence (Hexadecimal)** \\ \hline TI CC2650 sync word [35; 36] & 110100111001001 (0 xD391) \\ ITU-T Rec. H223 16-bit flag [37] & 10000111101010010 (0x87B2) \\ Orthogonal M-sequence [38] & 11110101010000 (0 xF590) \\ Manchester- coded Orthogonal M-sequence [38] & 10101001011001 (0 xA965) \\ \hline \hline \end{tabular}
\end{table}
Table 6: SFD used in computer simulations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Detail** \\ \hline Channel model & AWGN, IEEE model CM3 \\ Path loss model & IEEE model CM3 (Hospital Room) \\ Frequency spectrum & 2401–2481 MHz \\ Bandwidth (\(BW\)) & 2 MHz \\ Modulation & GFSK, QPSK, \(\pi/4\)-DQPSK, D8 PSK \\ Bandwidth-time product (BT) & 0.5 \\ Modulation index (h) & 0.5 \\ ECC (PLCP Header) & (36, 22) shortened BCH code \\ ECC (PSDU) & (127, 113) BCH code, (127, 85) BCH code \\ Maximum transmission power (\(P_{tr}\)) & 4 dBm \\ Thermal noise density (\(N_{0}\)) & \(-174\) dBm/Hz \\ Implementation losses (\(I_{\text{dB}}\)) & 6 dB \\ Receiver noise figure (\(NF_{\text{dB}}\)) & 13 dB \\ PSDU length (\(L_{PSDU}\)) & 127 bytes \\ ACK PSDU length (\(L_{PSDU,ACK}\)) & 127 bits \\ Preamble length (\(L_{preamble}\)) & 2 octets \\ PLCP header length (\(L_{PLCPheader}\)) & 40 bits \\ Symbol rate (\(R_{S}\)) & 1.0 Msps \\ Interframe spacing duration (\(T_{IFS}\)) & 150 \(\mu\)s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Computer simulation parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Detail** \\ \hline \(a\) & 6.6 \\ \(b\) & 36.1 \\ \(\sigma_{N}\) & 3.8 \\ \(K_{0}\) [dB] & 30.6 \\ \(m_{k}\) [dB] & 0.43 \\ \(\sigma_{k}\) & 3.4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Parameters of the IEEE model CM3.  
for GFSK modulation. Even with the proposed scheme, it did not achieve \(10^{-3}\). Then, the detection failure ratio was slightly improved by increasing the number of modulation levels. This occurred because the improvement of the cross-correlation value by repeating the preamble and the SFD according to the number of modulation levels had an effect.  
Figure 6: Physical layer convergence protocol (PLCP) header detection failure ratio in the case of several modulation schemes under the additive white Gaussian noise (AWGN) channel. (**a**) GFSK (SmartBAN) (**b**) QPSK (quadrature phase shift keying). (**c**) \(\pi\)/4-DQPSK (differential quadrature phase shift keying). (**d**) D8 PSK.  
Figure 7: _Cont_.