By Topic

Optimality of binning for distributed hypothesis testing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Rahman, M.S. ; Sch. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA ; Wagner, A.B.

We study a distributed hypothesis testing problem in which data is compressed distributively and the detector seeks to decide between two possible distributions for the data. The aim is to characterize all achievable encoding rates and exponents of the type 2 error probability when the type 1 error probability is at most a fixed value. For related problems in distributed source coding, schemes based on random binning are useful and often optimal. For distributed hypothesis testing, however, the use of binning is hindered by the fact that the overall error probability may be dominated by errors in binning process. We show that despite this complication, binning is optimal for a class of problems in which the goal is to “test against conditional independence.” We also use this optimality result to give an outer bound for a more general class of instances of the problem.

Published in:

Communication, Control, and Computing (Allerton), 2010 48th Annual Allerton Conference on

Date of Conference:

Sept. 29 2010-Oct. 1 2010