N
Nir Weinberger
Researcher at Massachusetts Institute of Technology
Publications - 64
Citations - 328
Nir Weinberger is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Computer science & Decoding methods. The author has an hindex of 9, co-authored 47 publications receiving 266 citations. Previous affiliations of Nir Weinberger include Technion – Israel Institute of Technology & Tel Aviv University.
Papers
More filters
Journal ArticleDOI
Codeword or Noise? Exact Random Coding Exponents for Joint Detection and Decoding
Nir Weinberger,Neri Merhav +1 more
TL;DR: The optimum detection/decoding rule is derived, in the sense of the best tradeoff among the probabilities of decoding error, false alarm, and misdetection, for the average code in the ensemble.
Journal ArticleDOI
Optimum Tradeoffs Between the Error Exponent and the Excess-Rate Exponent of Variable-Rate Slepian–Wolf Coding
Nir Weinberger,Neri Merhav +1 more
TL;DR: In this article, the optimal tradeoff between the error exponent and the excess-rate exponent for variable-rate Slepian-Wolf codes is analyzed, and upper and lower bounds on the optimal rate functions are derived via a simple class of variable rate codes which assign the same rate to all source blocks of the same type.
Posted Content
Optimum Trade-offs Between the Error Exponent and the Excess-Rate Exponent of Variable-Rate Slepian-Wolf Coding
Nir Weinberger,Neri Merhav +1 more
TL;DR: The optimal tradeoff between the error exponent and the excess-rate exponent for variable-rate Slepian-Wolf codes is analyzed and bounds on the optimal rate functions are derived, namely, the minimal rate assigned to each type class, needed in order to achieve a given target error exponent.
Journal ArticleDOI
On the Reliability Function of Distributed Hypothesis Testing Under Optimal Detection
Nir Weinberger,Yuval Kochman +1 more
TL;DR: It is conjecture that the resulting random-coding bound is ensemble-tight, and consequently optimal within the class of quantization-and-binning schemes.
Proceedings ArticleDOI
Exponent Trade-off for Hypothesis Testing Over Noisy Channels
TL;DR: This work extends the view to a trade-off between the two error exponents, additionally building on multiple codebooks and two special messages with unequal error protection, which generalizes Shimokawa et al.