A Link Quality Inference Model for IEEE 802.15.4 Low-Rate WPANs

Accurate estimation of PHY conditions would allow for better cross-layer resource management and provisioning. Unfortunately, most commercial-of-the-shelf (COTS) devices typically provide very limited information. For example, IEEE 802.15.4 Zigbee radios only provide received signal strength indicator (RSSI), link quality indicator (LQI) and noise floor readings, which are stored in frame check sequence (FCS) of MAC frames. In this paper, we revisit the issue of link quality prediction in IEEE 802.15.4 low rate wireless personal area networks (LR-WPAN) analytically and experimentally. By deciphering LQI readings available in Zigbee radios with CC2420 chipset, we demonstrate for the first time that LQI truly reflects the signal-to-noise ratio (SNR) at receiver. We also investigate the chip correlation (CORR) defined in CC2420 data sheet, and verify its relationship with LQI readings through measurement studies. Furthermore, in order to predict the instantaneous link quality for commodity Zigbee radios, we develop an inference model under different channel environments that uses instantaneous LQI readings as input. The proposed model is validated using extensive simulation and experimental study. We believe it will lead to more informed resource management decisions in WPANs.