## 4 WPAN MAC: Designing and Analysis
### Performance Evaluation  
In this subsection we present the performance evaluation of WPAN MAC in terms of throughput, efficient bandwidth utilization, and delay with different ACK policies under error channel conditions. For the performance evaluation we carried out a simulation in Matlab [15]. The main parameters for our simulation are based on [16] and listed in Table 2. For the simulation results we do not consider the technology adopted at the physical layer, however the physical layer determines some network parameter values like inter-frame spaces, _etc._ Whenever necessary we choose the values of the physical layer dependent parameters by referring to [16]. Also, we do not consider any specific scheduling algorithm to allocate the channel time slots as it is outside the scope of this paper. The design of a simple but effective scheduling algorithm is still an open issue. The results obtained here are the average values of our collected data. Figure 7 shows the throughput for different payload size with different ACK polices without aggregation, for CTA and CAP, respectively. We assume an ideal channel condition for these results. Here, we can observe that No-ACK gives the best results as most of the CTA and CAP time is utilized for data transfer. However, the No-ACK policy is not suitable for every application due to its limited communication reliability, compared to other ACK policies. For a WPAN designer the design of a system system with higher data rate or higher throughput is always a key demand. One way to achieve this requirement is to increase the data rate; however, that also has some limitations. Figure 8 shows the theoretical upper limit that exists on throughput. Even if we increase the data rate to infinite without reducing the overhead, we can only get achieve the bounded throughput, as shown in Figure 8. To reduce the overhead and to increase the throughput, with maximum available practical data rate [16], we adopt the frame aggregation method for WPAN. Now, Figure 9 shows the similar results but with aggregation method applied to different ACK policies in CTA and CAP, respectively. Here, No-ACK with aggregation (No-ACK-AGG) is nothing but the simple frame aggregation technique with maximum K burst size (K = 16) [10]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline SIFS & 2.5 usec \\ MIFS & 1 usec \\ Preamble and PLCP Header & 9 usec \\ \(CW_{\min}\) & 8 \\ Payload Size & 1 \(\sim\) 5 KB \\ ACK Policy & 3 basic + K-Dly-ACK-AGG \\ Data Rate & 1 \(\sim\) 2 Gbps \\ Control Signal Rate & 48 Mbps \\ Nodes & 1 \(\sim\) 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters for Performance Evaluation. The K-Dly-ACK-AGG policy (in the remainder of the paper we use the terms \"\(K\)-Dly-ACK-AGG\" and \"DLY-ACK with aggregation\" interchangeably) can achieve somewhat close results to the No-ACK policy, as it reduces the unnecessary inter-frame time as well as the header size. For Figures 7(b) and 9(b) we assume a light load network, as our main focus is to get maximum throughput for each payload size. We also set contention window at its minimum value. Figure 8: Throughput upper limit _versus_ payload size during CTA and CAP. Figure 7: Throughput _versus_ payload size with different ACK policies without aggregation during CTA and CAP. From the aforementioned results it is easy to conclude that the aggregation method gives higher throughput at high data size but we found it to be the other way around. Figure 10 shows the percentage gain in throughput using an aggregation method for different payload sizes for CTA and CAP, respectively. We can observe that as payload size increases, percentage gain in throughput reduces, the reason for is being that at lower payload size we can send more data packets in given a CTA/CAP duration but this also increases overhead. With aggregation methods we can reduce overhead to a large extent but can't get the same benefit at higher payload size.