On Channel State Inference and Prediction Using Observable Variables in 802.11b Network

Performance of cross-layer protocols that recommend the relay of corrupted packets to higher layers can be improved significantly by accurately inferring/predicting the bit error rate (BER) in the packets. In practice, higher layers observe the bits only after some hard decision. Hence physical layer link-quality indications, such as the signal strength of each individual bit, are not observable at higher layers. Therefore, it is essential to identify practically observable variables, which can be used for reasonably robust channel state inference/prediction (CSI/CSP). Here, inference specifically refers to estimating the BER in an already received packet, while prediction refers to anticipating the BER in a future packet. In this paper, we note that, in practical 802.11b devices, it is possible to acquire a Signal to Silence Ratio (SSR) indication and measure the background traffic intensity (p ) on a per packet basis. This paper, thus presents a measurement-based study that analyzes the utility of SSR and p as side-information for CSI/CSP. In this work, we exploit the method of types to measure the robustness of the observable side-information. Our analysis and simulations based on an extensive set of actual 802.11b traces exhibit the practical utility of the considered observable variables.