• DocumentCode
    43114
  • Title

    Physics-inspired methods for networking and communications

  • Author

    Saad, David ; Chi Yeung ; Rodolakis, Georgios ; Syrivelis, Dimitris ; Koutsopoulos, Iordanis ; Tassiulas, L. ; Urbanke, Rudiger ; Giaccone, Paolo ; Leonardi, Emilio

  • Author_Institution
    Aston Univ., Birmingham, UK
  • Volume
    52
  • Issue
    11
  • fYear
    2014
  • fDate
    Nov. 2014
  • Firstpage
    144
  • Lastpage
    151
  • Abstract
    Advances in statistical physics relating to our understanding of large-scale complex systems have recently been successfully applied in the context of communication networks. Statistical mechanics methods can be used to decompose global system behavior into simple local interactions. Thus, large-scale problems can be solved or approximated in a distributed manner with iterative lightweight local messaging. This survey discusses how statistical physics methodology can provide efficient solutions to hard network problems that are intractable by classical methods. We highlight three typical examples in the realm of networking and communications. In each case we show how a fundamental idea of statistical physics helps solve the problem in an efficient manner. In particular, we discuss how to perform multicast scheduling with message passing methods, how to improve coding using the crystallization process, and how to compute optimal routing by representing routes as interacting polymers.
  • Keywords
    crystallisation; encoding; iterative methods; message passing; multicast communication; physics; polymers; scheduling; statistical mechanics; telecommunication network routing; coding; communication networks; complex systems; crystallization process; iterative lightweight local messaging; message passing methods; multicast scheduling; network problems; optimal routing; physics-inspired methods; polymers; statistical mechanics methods; statistical physics methodology; Decoding; Network architecture; Optimization; Physics; Ports (Computers); Routing protocols; Statistical analysis;
  • fLanguage
    English
  • Journal_Title
    Communications Magazine, IEEE
  • Publisher
    ieee
  • ISSN
    0163-6804
  • Type

    jour

  • DOI
    10.1109/MCOM.2014.6957155
  • Filename
    6957155