This paper addresses the problem of Access Point (AP) channel assignment in dense IEEE 802.11 Wireless Local Area Networks (WLANs) implemented in a real world scenario, based on a housing complex located in Oslo, Norway. Currently, the APs composing this housing complex are centrally configured through a channel selection approach based on a genetic algorithm that aims to minimize the cumulative interference experienced by each AP. In this work, we present the performance of an alternative channel assignment algorithm in comparison to the existing genetic one. More specifically, the algorithm investigated in this work aims to minimize a networkwide parameter called interference impact, which represents the interference caused by an AP to all the other APs in the neighbourhood. Moreover, the algorithm is implemented using the spectrum programming architecture Wi-5 based on Software-Defined Networking (SDN). Although the benefits of this algorithm have been demonstrated in simulated environments, this work presents its first evaluation in a dense real world scenario. The performance analysis illustrates the important gains obtained in terms of the data transmissions quality through the proposed algorithm compared against the AP channel selection approach currently implemented in the considered scenario.
WiFi is one of the most widely deployed networking technologies, and understanding WiFi performance is therefore of great importance. The WiFi MAC layer sometimes introduces significant and variable delays. No existing models of the WiFi protocol describe WiFi performance in terms of complete latency distributions. In this work, we present a novel model of WiFi performance. We explicitly define our model in terms of the latency introduced at each step in the protocol state machine, and the model produces complete latency distributions. We validate the model by comparing its outputs to previous modeling work and real-world measurements. Finally, we use our results to quantify the latency distribution of WiFi as a function of the duration of transmit opportunities and the number of stations competing for the channel. Quantifying this relation represents a significant improvement in our understanding of WiFi performance that would not be possible with existing models.
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