TY - JOUR
T1 - Adaptive distributed estimation of signal power from one-bit quantized data
AU - Fang, Jun
AU - Li, Hongbin
PY - 2010/10
Y1 - 2010/10
N2 - We examine distributed estimation of the average power of a random signal in wireless sensor networks (WSNs). Due to stringent bandwidth/power constraints, each sensor quantizes its observation into one bit of information and sends the quantized data to a fusion center, where the signal power is estimated. We firstly introduce two fixed quantization (FQ) schemes, with the first using a single threshold and the second employing a pair of symmetric thresholds. The maximum likelihood (ML) estimators associated with the two FQ schemes are developed, and their corresponding Cramér-Rao bounds (CRBs) are analyzed. We show that the FQ approach, especially the second one, can achieve an estimation performance close to that of a clairvoyant estimator using unquantized data if the optimum quantization threshold is available; however, the optimum threshold is dependent on the unknown signal power, and as the threshold deviates from its optimum value, the performance degrades rapidly. To cope with this difficulty, we propose a distributed adaptive quantization (AQ) approach by which the threshold is dynamically adjusted from one sensor to another in a way such that the threshold converges to the optimum threshold. Our analysis shows that the proposed AQ approach is asymptotically optimum, yielding an asymptotic CRB equivalent to that of the FQ approach with optimum threshold.
AB - We examine distributed estimation of the average power of a random signal in wireless sensor networks (WSNs). Due to stringent bandwidth/power constraints, each sensor quantizes its observation into one bit of information and sends the quantized data to a fusion center, where the signal power is estimated. We firstly introduce two fixed quantization (FQ) schemes, with the first using a single threshold and the second employing a pair of symmetric thresholds. The maximum likelihood (ML) estimators associated with the two FQ schemes are developed, and their corresponding Cramér-Rao bounds (CRBs) are analyzed. We show that the FQ approach, especially the second one, can achieve an estimation performance close to that of a clairvoyant estimator using unquantized data if the optimum quantization threshold is available; however, the optimum threshold is dependent on the unknown signal power, and as the threshold deviates from its optimum value, the performance degrades rapidly. To cope with this difficulty, we propose a distributed adaptive quantization (AQ) approach by which the threshold is dynamically adjusted from one sensor to another in a way such that the threshold converges to the optimum threshold. Our analysis shows that the proposed AQ approach is asymptotically optimum, yielding an asymptotic CRB equivalent to that of the FQ approach with optimum threshold.
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U2 - 10.1109/TAES.2010.5595602
DO - 10.1109/TAES.2010.5595602
M3 - Article
AN - SCOPUS:78651111856
SN - 0018-9251
VL - 46
SP - 1893
EP - 1905
JO - IEEE Transactions on Aerospace and Electronic Systems
JF - IEEE Transactions on Aerospace and Electronic Systems
IS - 4
M1 - 5595602
ER -