Enhanced Mathematical Modeling of Aggregation-Enabled WLANs with Compressed BlockACK

摘要

Aggregation-enabled wireless local area networks (WLANs) have an automatic repeat-request (ARQ) mechanism called a block acknowledgement window (BAW), which has a significant impact on frame aggregation size, throughput, and delay under noisy channel conditions. While accurate estimations of network performance are essential for traffic engineering and guaranteed quality of service, the existing mathematical modeling approaches do not include BAW operations, and thus, are not applicable to aggregation-enabled WLANs with BAW mechanism. In this paper, we propose a new and accurate Markov chain model to estimate the average aggregation size resulting from BAW operations under noisy channel conditions. The obtained average aggregation size is used in the proposed enhanced performance model of aggregation-enabled WLANs. Our numerical and simulation analysis shows that, for 50 nodes transmitting saturated uplink traffic on a channel with a packet error rate (PER) of 0.25, ignoring the BAW operations when modeling the current protocol results in throughput and access delay errors as high as 55 and 77 percent, respectively; but the proposed enhanced model, which captures the BAW's impact, produces less than 2.5 percent errors in throughput and access delay estimations.