## 4 Simulation Results  
Simulations were conducted to compare the performance of CAQT with the query-based (QT) algorithm [21], the typical aloha-based algorithm called dynamic frame-slotted Aloha (DFSA) [14; 18],  
Figure 9: Pseudo codes of CAQT. and ERE-ABS [6] in terms of number of slots and number of idle slots under varying number of tags and PERs. The number of slots means the total number of slots to identify all tags, and the number of idle slots is the total number of idle slots during the tag identification procedure. The estimated number of tags during the progress of tag identification was also investigated to ensure DFSA can adjust the estimated number of tags under error-prone channel. The simulation environment setting was as the same as that in [6] for fair comparison. That is, for the DFSA algorithm, the minimum frame size was set to the minimum frame size in order to estimate \(n\) through the tag estimation algorithm; regarding the ERE-ABS, \(L_{est}\) was set to 128, the threshold for probability of idle slots \(P_{idle\_th}=0.2\), and the factor \(f_{d}=4\) used to reduce the number of tags; for CAQT, the \(\alpha\) was set to 0.75 to estimate channel condition. As shown in Figure 10, the effect of various PER ranging between 0 and 0.5 was explored when number of tags is 500. Figure 10a demonstrates that CAQT used the fewest slots to identify all tags, and Figure 10b also presents that CAQT had the fewest idle slots during the tag identification procedure. Therefore, CAQT outperformed QT, DFSA, and ERE-ABS under all PER cases. This was because QT and DFSA could not distinguish the unreadable slot was due to collision or channel error, meaning that QT and DFSA inescapably overestimated the number of tags within the coverage of the reader. Therefore, the overestimated number of tags caused that QT used too many slots to split the tags and results in lots of unnecessary idle slots for resolving the unidentified tags. On the other hand, the overestimated number of tags made DFSA difficult to accurately determine the optimal initial frame size and therefore suffer many idle slots. With regards to ERE-ABS, its inferior performance was due to that ERE-BAS merely estimated the PER before the tag resolution procedure. However, the channel status changed rapidly, the estimated PER might not accurately reflect the current channel condition since ERE-ABS did not dynamically adjust the PER during the collision resolving procedure. This resulted in the number of the idle slots still being large. Figure 10: The effect of Packet Error Rate (PER). CAQT owned the superior performance compared with other methods, and the higher PER was, the larger improvement CAQT had. As shown in Figure 9(a), CAQT only used 69.0%, 57.8%, and 87.2% slots consumed by QT, DFSA, and ERE-ABS, respectively, when the PER is 0.5. The two reasons were (1) it estimated PER in each slot including the collision resolution process in order to improve the accuracy of the tag estimation in the rapidly changing channel quality environment, and (2) when a reader received an unreadable message from tags, it then statistically determined the most efficient strategy, which was to ask the tag to retransmit it or to split the collide tags. Therefore, the number of unnecessary idle slots was reduced, as shown in Figure 9(b), and the performance was improved. It should be noted that the higher PER was, the more slots were interfered by poor channel condition, meaning that more slots were required to identify all tags by all algorithm. The CAQT also had better performance comparing with others when PER was equal to 0. This is because, QT and ERE-ABS simply divided collided tags into two groups while detecting a collision event, resulting in that more slots were used to resolve the collide tags and poor performance was caused. On the other hand, CAQT can estimate the number of collided tags of a collision event and then separate tags into groups of which the number equals the estimated collided tags.