TY - JOUR
T1 - Subspace-Aware Index Codes
AU - Kailkhura, Bhavya
AU - Theagarajan, Lakshmi Narasimhan
AU - Varshney, Pramod K.
N1 - Funding Information:
Manuscript received February 16, 2017; revised April 6, 2017; accepted April 8, 2017. Date of publication April 12, 2017; date of current version June 19, 2017. This work was supported in part by the NSF under Grant ECCS 1609916, and in part by the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DEAC52-07NA27344 (LLNL-JRNL-718227). The associate editor coordinating the review of this paper and approving it for publication was R. C. de Lamare. (Bhavya Kailkhura and Narasimhan Theagarajan contributed equally to this work.) (Corresponding author: Bhavya Kailkhura.) B. Kailkhura was with the Department of EECS, Syracuse University, Syracuse, NY 13244 USA. He is now with Lawrence Livermore National Laboratory, Livermore, CA 94550 USA (e-mail: kailkhura1@llnl.gov).
Publisher Copyright:
© 2017 IEEE.
PY - 2017/6
Y1 - 2017/6
N2 - In this letter, we generalize the well-known index coding problem to exploit the structure in the source-data to improve system throughput. In many applications (e.g., multimedia), the data to be transmitted may lie (or can be well approximated) in a low-dimensional subspace. We exploit this low-dimensional structure of the data using an algebraic framework to solve the index coding problem (referred to as subspace-aware index coding) as opposed to the traditional index coding problem which is subspace-unaware. Also, we propose an efficient algorithm based on the alternating minimization approach to obtain near optimal index codes for both subspace-aware and -unaware cases. Our simulations indicate that under certain conditions, a significant throughput gain (about 90%) can be achieved by subspace-aware index codes over conventional subspace-unaware index codes.
AB - In this letter, we generalize the well-known index coding problem to exploit the structure in the source-data to improve system throughput. In many applications (e.g., multimedia), the data to be transmitted may lie (or can be well approximated) in a low-dimensional subspace. We exploit this low-dimensional structure of the data using an algebraic framework to solve the index coding problem (referred to as subspace-aware index coding) as opposed to the traditional index coding problem which is subspace-unaware. Also, we propose an efficient algorithm based on the alternating minimization approach to obtain near optimal index codes for both subspace-aware and -unaware cases. Our simulations indicate that under certain conditions, a significant throughput gain (about 90%) can be achieved by subspace-aware index codes over conventional subspace-unaware index codes.
KW - Index coding
KW - alternating minimization
KW - coded side-information
KW - low-dimensional data
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U2 - 10.1109/LWC.2017.2693362
DO - 10.1109/LWC.2017.2693362
M3 - Article
AN - SCOPUS:85028426790
SN - 2162-2337
VL - 6
SP - 366
EP - 369
JO - IEEE Wireless Communications Letters
JF - IEEE Wireless Communications Letters
IS - 3
M1 - 7898367
ER -