Type-Based Random Access for Distributed Detection Over Multiaccess Fading Channels

The problem of distributed detection in a sensor network over multiaccess fading channels is considered. A random-access transmission scheme referred to as the type-based random access (TBRA) is proposed and analyzed. Error exponents of TBRA under noncoherent detection are characterized with respect to the mean transmission rate and the channel-coherence index. For the zero-mean multiaccess fading channels, it is shown that there exists an optimal mean-transmission rate that maximizes the detection-error exponents. The optimal mean-transmission rate can be calculated numerically or estimated using the Gaussian approximation, and it gives a sensor-activation strategy that achieves an optimal allocation of transmission energy to spatial and temporal domains. Numerical examples and simulations are used to compare TBRA with the conventional centralized time-division multiple access (TDMA) scheme. It is shown that for the zero-mean multiaccess fading channels, TBRA gives substantial improvement in the low signal-to-noise ratio (SNR) regime whereas for the nonzero mean fading channels, TBRA performs better over a wide range of SNR.

[1]  Ananthram Swami,et al.  Detection of Gauss–Markov Random Fields With Nearest-Neighbor Dependency , 2007, IEEE Transactions on Information Theory.

[2]  Venugopal V. Veeravalli,et al.  Decentralized detection in sensor networks , 2003, IEEE Trans. Signal Process..

[3]  Flemming Topsøe,et al.  Some inequalities for information divergence and related measures of discrimination , 2000, IEEE Trans. Inf. Theory.

[4]  Akbar M. Sayeed,et al.  Optimal Distributed Detection Strategies for Wireless Sensor Networks , 2004 .

[5]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[6]  Amir Dembo,et al.  Large Deviations Techniques and Applications , 1998 .

[7]  L. Tong,et al.  Asymptotic detection performance of type-based multiple access in sensor networks , 2005, IEEE 6th Workshop on Signal Processing Advances in Wireless Communications, 2005..

[8]  L. Tong,et al.  Distributed Statistical Inference using Type Based Random Access over Multi-access Fading Channels , 2006, 2006 40th Annual Conference on Information Sciences and Systems.

[9]  Lang Tong,et al.  One aspect to cross-layer design in sensor networks , 2004, IEEE MILCOM 2004. Military Communications Conference, 2004..

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

[11]  Lang Tong,et al.  A Large Deviation Analysis of Detection Over Multi-Access Channels with Random Number of Sensors , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[12]  Venugopal V. Veeravalli,et al.  Asymptotic results for decentralized detection in power constrained wireless sensor networks , 2004, IEEE Journal on Selected Areas in Communications.

[13]  Pramod K. Varshney,et al.  Distributed Detection and Data Fusion , 1996 .

[14]  Lang Tong,et al.  Asymptotic Detection Performance of Type-Based Multiple Access Over Multiaccess Fading Channels , 2007, IEEE Transactions on Signal Processing.

[15]  J. Wendelberger Adventures in Stochastic Processes , 1993 .

[16]  R. Strichartz The way of analysis , 1995 .

[17]  Rick S. Blum,et al.  Distributed detection with multiple sensors I. Advanced topics , 1997, Proc. IEEE.

[18]  Pramod K. Varshney,et al.  Distributed detection with multiple sensors I. Fundamentals , 1997, Proc. IEEE.

[19]  José M. F. Moura,et al.  Detection in decentralized sensor networks , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[20]  Pramod K. Varshney,et al.  Channel aware decision fusion in wireless sensor networks , 2004, IEEE Transactions on Signal Processing.

[21]  Lang Tong,et al.  Estimation Over Deterministic Multiaccess Channels , 2004 .

[22]  Peter Willett,et al.  On the optimality of likelihood ratio test for local sensor decision rules in the presence of non-ideal channels , 2003 .