Cognitive wyner networks with clustered decoding

Amos Lapidoth, Nathan Levy, Shlomo Shamai Shitz, Michèle Wigger

Research output: Contribution to journalArticlepeer-review

Abstract

We study an interference network where equally numbered transmitters and receivers lie on two parallel lines, with each transmitter opposite its intended receiver. We consider two short-range interference models: the asymmetric network, where the signal sent by each transmitter is interfered only by the signal sent by its left neighbor (if present), and a symmetric network, where it is interfered by both its left and its right neighbors. Each transmitter is cognizant of its own message, the messages of the tltransmitters to its left, and the messages of the trtransmitters to its right. Each receiver decodes its message based on the signals received at its own antenna, at the rlreceive antennas to its left, and at the rrreceive antennas to its right. For such networks, we provide upper and lower bounds on the multiplexing gain, i.e., on the high signal-to-noise ratio asymptotic logarithmic growth of the sum-rate capacity. In some cases, our bounds coincide, e.g., for the asymmetric network. Our results exhibit an equivalence between the transmitter side-information parameters tl,trand the receiver side-information parameters rl,rrin the sense that increasing/decreasing tlor trby a positive integer δ has the same effect on the multiplexing gain as increasing/decreasing rlor rrby δ. Moreover - even in asymmetric networks - there is an equivalence between the left side-information parameters (tl,rl) and the right side-information parameters (tr,rr).

Original languageEnglish
Article number6869020
Pages (from-to)6342-6367
Number of pages26
JournalIEEE Transactions on Information Theory
Volume60
Issue number10
DOIs
StatePublished - 1 Oct 2014

Keywords

  • Clustered decoding
  • dirty-paper coding
  • interference networks
  • message cognition
  • multiplexing gain
  • successive interference cancellation

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences

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