## 4 WPAN MAC: Designing and Analysis
### Performance Evaluation
Thus there is an open issue for a designer to choose an appropriate data packet size for the needed gain in throughput. From Figures 7 and 9 we can observe the value of throughput at different payload sizes but still these results are not sufficient to find the optimum payload size with given ACK policies. So, we obtained the same results under Gaussian wireless channel model with different BER rates. However, only a part of  
Figure 10: Increment in throughput. Figure 9: Throughput _versus_ payload size with different ACK policies with aggregation during CTA and CAP. the results are presented here to reduce the number of graphs in order to maintain the lucidity of the paper. Figures 11 and 12 show the throughput for different payload sizes under a given BER value during CTA and CAP, with and without aggregation, respectively. It can be seen that an optimal payload size exists for a given BER value, and the optimal payload size increases as BER values decreases. As shown in the mentioned figures the throughput first increases, and then decreases with increasing payload size (even with the aggregation) in error prone channels. This is because without the protection of FCS in an individual payload frame, a single bit error may corrupt the whole frame which will waste lots of medium time usage and counteract the efficiency  
Figure 11: Throughput _versus_ payload size with different ACK policies without aggregation during CTA and CAP. Figure 12: Throughput _versus_ payload size with different ACK policies with aggregation during CTA and CAP. produced by an increased payload size. So, the initial increase of the curves in figures show the effect of increased transmission efficiency over the effect of increased frame error probability, while decreses in the curves show the opposite results. From the above mentioned figures we can determine the optimum payload size value for a given BER value. As shown in the results K-Dly-ACK with aggregation policy give the best results after No-ACK policy. For all our throughput results during CAP time we selected a light-load network scenario. However, it is very interesting to note that for the duration of CAP duration [Figures 11(b) and 12(b)]; throughput performance depends on the number of active stations and backoff window size. Figure 13 further investigates this observation. As shown in this figure the throughput under a given BER value, with and without aggregation, respectively, decreases as the number of  
As shown in Figure 14, IB can improve the performance to a large extent because of its unique feature of avoiding collision among the competing nodes. Here we did not apply IB for No-ACK policy as it has to use with fixed CW size, so the results for No-ACK policy are same as shown in Figure 13. From the aforementioned results we only get the optimal payload size for a fixed BER value, which might not be a sufficient result for a WPAN designer, so to check the performance of a given network under the range of BER values with different payload sizes, we obtained the subsequent results as shown in Figure 15. As we mentioned earlier, for the sake of clarity in the paper, we omitted here several results with different payload sizes. Figure 15 shows the normalized throughput for a given BER value with different ACK policies when payload size is set to 3 KB in CTA and CAP, respectively. As the BER value increases the optimal payload size and the optimal throughput decreases. From the figure we can observe that the No-ACK policy with aggregation has larger throughput over large range of BER values than other ACK policies, because a No-ACK policy with aggregation gives maximum usage of CTA and CAP duration. Also, we can notice that at higher value of BER throughput reaches to zero, at this value payload size is too big for the given system. To control the decrement in throughput we apply a payload size adjusting mechanism or payload adjusting mechanism (PAM) to WPAN MAC, as explained in Section 3.2.