Source-Channel Communication in One-Helper Problem

We consider the m-helper problem for the special case of m = 1 where one source provides partial side information to the fusion center (FC) to help reconstruction of the main source signal. Both sources communicate information about their observations to the FC through an orthogonal multiple access channel (MAC) without cooperating with each other. We characterize the optimal tradeoff between the transmission cost, i.e., power, and the distortion D using Shannon's separation source and channel coding theorem. We show that the separation approach outperforms the analog forwarding approach in the 1- helper problem. We also determine the optimal power scheduling to minimize the total power consumption in the network.

[1]  Sui Tung,et al.  Multiterminal source coding (Ph.D. Thesis abstr.) , 1978, IEEE Trans. Inf. Theory.

[2]  João Barros,et al.  On the capacity of the reachback channel in wireless sensor networks , 2002, 2002 IEEE Workshop on Multimedia Signal Processing..

[3]  Zhi-Quan Luo,et al.  Multiterminal Source-Channel Communication Under Orthogonal Multiple Access , 2005 .

[4]  Yasutada Oohama,et al.  Rate-distortion theory for Gaussian multiterminal source coding systems with several side informations at the decoder , 2005, IEEE Transactions on Information Theory.

[5]  Toby Berger,et al.  The CEO problem [multiterminal source coding] , 1996, IEEE Trans. Inf. Theory.

[6]  Zhi-Quan Luo,et al.  Multiterminal Source–Channel Communication Over an Orthogonal Multiple-Access Channel , 2007, IEEE Transactions on Information Theory.

[7]  Pramod Viswanath,et al.  Rate Region of the Quadratic Gaussian Two-Encoder Source-Coding Problem , 2006, ISIT.

[8]  Gregory J. Pottie,et al.  Lossy source coding of multiple Gaussian sources: m-helper problem , 2004, Information Theory Workshop.

[9]  J. Nicholas Laneman,et al.  Cooperative diversity in wireless networks: algorithms and architectures , 2002 .

[10]  Stark C. Draper,et al.  Successive structuring of source coding algorithms for data fusion, buffering, and distribution in networks , 2002 .

[11]  Y. Oohama Gaussian multiterminal source coding , 1995, Proceedings of 1995 IEEE International Symposium on Information Theory.

[12]  Stark C. Draper,et al.  Side information aware coding strategies for sensor networks , 2004, IEEE Journal on Selected Areas in Communications.

[13]  Michael Gastpar,et al.  Source-Channel Communication in Sensor Networks , 2003, IPSN.

[14]  Pramod Viswanath,et al.  Rate Region of the Quadratic Gaussian Two-Terminal Source-Coding Problem , 2005, ArXiv.

[15]  M. Reza Soleymani,et al.  Power-Distortion Performance of Successive Coding Strategy in Gaussian Ceo Problem , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[16]  Thomas J. Goblick,et al.  Theoretical limitations on the transmission of data from analog sources , 1965, IEEE Trans. Inf. Theory.

[17]  Michael Gastpar,et al.  To code, or not to code: lossy source-channel communication revisited , 2003, IEEE Trans. Inf. Theory.