Adaptive Frame Size Estimation Using Extended Kalman Filter for High-Stressed WLANs

Abstract

Demands for high throughput and stable service quality are increasing. Frame aggregation mechanisms in IEEE 802.11n wireless local area networks (WLANs) can provide improved throughput, but the effect of A-MSDU decreases significantly in error-prone channels. Therefore, adaptive frame size estimation (FSE) depending on the channel condition is required to maintain the improved throughput. In this paper, we proposed frame error rate (FER) based FSE scheme in errorprone and time-varying channel such as a high-stressed network. A tight FER bound is derived to obtain instantaneous link condition, and extended Kalman filter (EKF) is used to estimate frame size optimally for next transmission with current channel information. Our simulation results show that the proposed FSE scheme improves the throughput two times higher than a nonadaptation approach in high-stressed network condition.