Coding sets with asymmetric information
Abstract
We study the following oneway asymmetric transmission problem, also a variant of modelbased compressed sensing: a resourcelimited encoder has to report a small set $S$ from a universe of $N$ items to a more powerful decoder (server). The distinguishing feature is asymmetric information: the subset $S$ is comprised of i.i.d. samples from a prior distribution $\mu$, and $\mu$ is only known to the decoder. The goal for the encoder is to encode $S$ obliviously, while achieving the informationtheoretic bound of $S \cdot H(\mu)$, i.e., the Shannon entropy bound. We first show that any such compression scheme must be {\em randomized}, if it gains nontrivially from the prior $\mu$. This stands in contrast to the symmetric case (when both the encoder and decoder know $\mu$), where the Huffman code provides a nearoptimal deterministic solution. On the other hand, a rather simple argument shows that, when $S=k$, a random linear code achieves nearoptimal communication rate of about $k\cdot H(\mu)$ bits. Alas, the resulting scheme has prohibitive decoding time: about ${N\choose k} \approx (N/k)^k$. Our main result is a computationally efficient and linear coding scheme, which achieves an $O(\lg\lg N)$competitive communication ratio compared to the optimal benchmark, and runs in $\text{poly}(N,k)$ time. Our "multilevel" coding scheme uses a combination of hashing and syndromedecoding of ReedSolomon codes, and relies on viewing the (unknown) prior $\mu$ as a rather small convex combination of uniform ("flat") distributions.
 Publication:

arXiv eprints
 Pub Date:
 July 2017
 arXiv:
 arXiv:1707.04875
 Bibcode:
 2017arXiv170704875A
 Keywords:

 Computer Science  Data Structures and Algorithms;
 Computer Science  Information Theory;
 Computer Science  Networking and Internet Architecture