- Ph. D., University of Connecticut
- Communication Laboratory
- Statistical inference applied to decentralized systems
- Network information theory
- Signal processing for MIMO, multi-user, and multi-carrier communications
My current work focuses on decentralized inference and in particular, how inference functions can impact the value of information in a networked system. Specific topics of interests include the sufficiency principle in the decentralized setting that guides optimal data reduction, the role of feedback and interactive communication in networked inference, and architectural issues in data fusion systems.
Another area of current interest is in characterizing the performance limits of multi-user networks, and in particular the classical interference channel. On the applied side, we have been involved in developing MIMO communication systems for airborne platforms when MIMO channels exhibits rank deficiency due to the lack of scatterers and in developing robust, efficient, and secure software radio systems.
- ELE606: Probability
- ELE651: Digital Communications
- ELE751: Wireless Communications
- ELE757: Information Theory
- NSF Career Award (2006)
- Finalist, DARPA Spectrum Challenge (2013-2014)
- IEEE Fellow (2015)
Akofor and B. Chen,”Interactive distributed detection: architecture and performance analysis,”IEEE Trans. Information Theory. vol. 60, pp.6456-6473, Oct. 2014.
Xu, S. Zhu and B. Chen,”Decentralized data reduction with quantization constraints,”IEEE Trans. Signal Processing, vol. 62, pp. 1775-1784, April 2014.
Chen, B. Chen, and P.K. Varshney,”A new framework for distributed detection with conditionally dependent observations,”IEEE Trans. Signal Processing, vol. 60, pp. 1409-1419, March 2012.
Shang, G. Kramer, and B. Chen,”A new outer bound and the noisy-interference sum-rate capacity for Gaussian interference channels,”IEEE Transactions on Information Theory, vol. 55, pp. 689-699, February 2009.
Chen, R. Jiang, T. Kasetkasem, and P.K. Varshney,”Channel aware decision fusion for wireless sensor networks,”IEEE Trans. Signal Processing, vol. 52, pp. 3454-3458, Dec. 2004.