# Opportunities and Challenges for Error Correction Scheme for Wireless Body Area Network--A Survey
## 5 Review on ECS Methods on WBAN
### Harq
#### 5.2.2 Harq-Ii  
We present a review of HARQ-II based ECS in Table 7. In [48], the authors introduced adaptive HARQ-II for WBAN based on UWB-PHY. Figure 7 illustrates the concept that is presented in paper [48]. The paper considered the different types (medical and non-medical data) with different QoS requirements data transmission. The coding, modulation, and demodulation scheme is the same for both types of data transmission. The reliability of medical data is enhanced by the use of selective ARQ and by transmitting the parity bits and data bits alternatively during retransmission when NACK was received from the receiver. However, ARQ is not applied to non-medical data transmission. The author claimed that the proposed scheme is suitable for the IEEE 802.15.6 CM3 (on-body to on-body devices) scenario and the same is considered during the simulation. However, CM3 incurs different types of channels (refer to Table 3), such as LOS and NLOS. The paper did not outline what type of specific channel has been used during the simulation. In [48], BCH (63, 51) is used as ECC and binary pulse position modulation (2 PPM) as modulation in HARQ-II. In the MAC layer, the paper considers medium access control methods of slotted ALOHA and polling during the simulation. The performance of the proposed system measured in terms of the probability of accepting error frame rate and buffer usages versus SNR. The result indicated that the use of polling improves the performance during the presence of multiple users when compared with slotted ALOHA. However, the simulation result of throughput efficiency versus maximum number of transmission showed that the use of slotted ALOHA perform better than polling in the high SNR region. The presented results of the proposed method are standalone performance without considering WBAN network settings. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref. (Yr)** & **Proposed Technique** & **WBAN/Standard/Layer/Data/Channel** & **Results** \\ \hline \multirow{3}{*}{[46] (010)} & Adaptive convolutional & One hop star network and single & \\  & code with ARQ & WBAN/IEEE 802.15.6/Cross layer/no & MAE and rate of increase \\  & & discussion/AWGN & of frequency band \\ \hline \multirow{3}{*}{[47] (016)} & \multirow{3}{*}{BCH with adaptive ARQ} & One hop star network and single & \\  & & WBAN/IEEE 802.15.6/Cross & \\  & & layer/NA/AWGN & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A review of HARQ-I based ECS. In [50, 56], the authors proposed the combination of decomposable codes (based on convolutional code in [50] and Reed-Muller codes in [49]) and Weldon's ARQ to adjust the balance between error correcting capability and the number of retransmission according to required QoS, depending on data types. The concept of ECS that is used in these papers is HARQ-II that is similar to [48]. However, these papers uses the Reed-Muller code [49] and convolutional code [50] instead of BCH code, Weldon' ARQ instead of selective ARQ, and only considers medical data with different user priority levels. In [50], the paper outlines the process of multiplexing of various data types i.e., data with different user priority, as illustrated in Figure 8. In both papers, the operation of Weldon's ARQ and respective decomposable ECCs are shown. In [50], the comparison results is presented in terms of residual BER and non-medical/AWGN. \begin{table}
\begin{tabular}{c c c c} \hline
**Ref. (Yr)** & **Proposed Technique** & **WBAN/Standard/Layer/Data/Channel** & **Results** \\ \hline \multirow{4}{*}{[48] (010)} & \multirow{4}{*}{ \begin{tabular}{c} BCH code with \\ selective ARQ \\ \end{tabular}  } & One hop star network and single & \multirow{4}{*}{\ \begin{tabular}{c} Accepting frame error \\ rate, buffer usage and \\ throughput efficiency \\ \end{tabular}  } \\  & & & \\ \cline{1-1}  & & One hop star network and single & \multirow{4}{*}{ \begin{tabular}{c} Residual BER \\ and throughput efficiency \\ \end{tabular}  } \\
[52] (015) & \begin{tabular}{c} Convolutional code with \\ Weldon’s ARQ \\ \end{tabular}  &