V. Kapnadak, M. Senel, and E. J. Coyle, "Adaptive Quantization for Distributed Characterization of Interferers in Wireless Networks," SenSIP 2008 Special Issue of Digital Signal Processing, Dec. 2010.


Adaptive Quantization for Distributed Characterization of Interferers in Wireless Networks
We consider the problem of estimating the distance to a device transmitting in the 2.4GHz ISMband that is interfering with users of an 802.11b wireless network. Accurate estimation of this quantity enables the wireless network to dynamically change its operating characteristics, such as its transmit power levels and channel assignments, to minimize this interference. The estimate is made by a cluster of wireless sensor motes deployed along the edge of an 802.11b network. The motes perform a 1-bit quantization of the Received Signal Strength (RSS) using a dithered quantization framework. The quantized bits are transmitted over a Binary Symmetric Channel (BSC) to the Cluster Head (CH), which then uses a Maximum-Likelihood Estimation (MLE) technique to estimate the unknown parameter. We propose a framework in which the CH uses an iterative parameter estimation scheme in which it provides low-overhead feedback to the motes to adjust their threshold values for the 1-bit dithered quantization process. Evaluation of the Root Mean Squared Error (RMSE) of this iterative scheme shows that it performs significantly better than iterative approaches in which all the motes use either identical thresholds or the non-identical thresholds proposed in [16],[17]. Our iterative scheme also tracks sudden changes in the distance to the interferer and is robust to fluctuations in the crossover probability in the BSC.