Bandwidth Scaling for Efficient Inference Over a Power-Limited MAC

We introduce a likelihood based multiple access (LBMA) communication/estimation scheme for nonrandom parameter estimation in wireless sensor networks with additive multiple access channels. Constraining the system in terms of energy and allowing the available number of degrees of freedom to scale as nα, 0.5 <; α <; 1, we prove that LBMA is asymptotically efficient. Thus, the new scheme is appropriate for large networks. LBMA is, in addition, simple to implement and relies upon an intuitive approach.

[1]  M. Vetterli,et al.  On the capacity of large Gaussian relay networks , 2005 .

[2]  R. Gallager Information Theory and Reliable Communication , 1968 .

[3]  Ke Liu,et al.  On optimal parametric field estimation in sensor networks , 2005, IEEE/SP 13th Workshop on Statistical Signal Processing, 2005.

[4]  R. F.,et al.  Mathematical Statistics , 1944, Nature.

[5]  Ke Liu,et al.  Asymptotically optimal decentralized type-based detection in wireless sensor networks , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[6]  Michael Gastpar,et al.  On the capacity of large Gaussian relay networks , 2005, IEEE Transactions on Information Theory.

[7]  Lang Tong,et al.  A Likelihood-Based Multiple Access for Estimation in Sensor Networks , 2007, IEEE Transactions on Signal Processing.

[8]  Lang Tong,et al.  Type based estimation over multiaccess channels , 2006, IEEE Transactions on Signal Processing.

[9]  Toby Berger,et al.  An upper bound on the sum-rate distortion function and its corresponding rate allocation schemes for the CEO problem , 2004, IEEE Journal on Selected Areas in Communications.

[10]  Yasutada Oohama,et al.  The Rate-Distortion Function for the Quadratic Gaussian CEO Problem , 1998, IEEE Trans. Inf. Theory.