TY - GEN

T1 - Line spectrum estimation from broadband power detection bits

AU - Mehanna, Omar

AU - Sidiropoulos, Nicholas D.

AU - Tsakonas, Efthymios

PY - 2013/10/22

Y1 - 2013/10/22

N2 - Line spectrum estimation from analog signal samples is a classic problem with numerous applications. However, sending analog or finely quantized signal sample streams to a fusion center is a burden in distributed sensing scenarios. Instead, it is appealing to estimate the frequency lines from a few randomly filtered broadband power measurement bits taken using a network of cheap sensors. This leads to a new problem: line spectrum estimation from inequalities. Three different techniques are proposed for this estimation task. In the first two, the autocorrelation function is first estimated nonparametrically, then a parametric method is used to estimate the sought frequencies. The third is a direct maximum likelihood (ML) parameter estimation approach that uses coordinate descent. Simulations show that the underlying frequencies can be accurately estimated using the proposed techniques, even from relatively few bits; and that the ML estimates obtained with the third technique can meet the Cramer-Rao lower bound (also derived here), when the number of sensors is sufficiently large.

AB - Line spectrum estimation from analog signal samples is a classic problem with numerous applications. However, sending analog or finely quantized signal sample streams to a fusion center is a burden in distributed sensing scenarios. Instead, it is appealing to estimate the frequency lines from a few randomly filtered broadband power measurement bits taken using a network of cheap sensors. This leads to a new problem: line spectrum estimation from inequalities. Three different techniques are proposed for this estimation task. In the first two, the autocorrelation function is first estimated nonparametrically, then a parametric method is used to estimate the sought frequencies. The third is a direct maximum likelihood (ML) parameter estimation approach that uses coordinate descent. Simulations show that the underlying frequencies can be accurately estimated using the proposed techniques, even from relatively few bits; and that the ML estimates obtained with the third technique can meet the Cramer-Rao lower bound (also derived here), when the number of sensors is sufficiently large.

KW - Distributed spectrum sensing

KW - cognitive radio

KW - line spectrum estimation

KW - spectral analysis

UR - http://www.scopus.com/inward/record.url?scp=84885813044&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84885813044&partnerID=8YFLogxK

U2 - 10.1109/SPAWC.2013.6612081

DO - 10.1109/SPAWC.2013.6612081

M3 - Conference contribution

AN - SCOPUS:84885813044

SN - 9781467355773

T3 - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC

SP - 405

EP - 409

BT - 2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2013

T2 - 2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2013

Y2 - 16 June 2013 through 19 June 2013

ER -