A Cognitive Mechanism for Rate Adaptation in Wireless Networks

Sophisticated wireless interfaces support multiple transmission data rates and the selection of the optimal data rate has a critical impact on the overall network performance. Proper rate adaptation requires dynamically adjusting data rate based on current channel conditions. Despite several rate adaptation algorithms have been proposed in the literature, there are still challenging issues related to this problem. The main limitations of current solutions are concerned with how to estimate channel quality to appropriately adjust the rate. In this context, we propose a Cognitive Rate Adaptation mechanism for wireless networks. This mechanism includes a distributed self-configuration algorithm in which the selection of data rate is based on past experience. The proposed approach can react to changes in channel conditions and converge to the optimal data rate, while allowing a fair channel usage among network nodes. Simulation results obtained underline performance benefits with respect to existing rate adaptation algorithms.