• DocumentCode
    3062540
  • Title

    Asymptotic sum-capacity of random Gaussian interference networks using interference alignment

  • Author

    Aldridge, Matthew ; Johnson, Oliver ; Piechocki, Robert

  • Author_Institution
    Dept. of Math., Univ. of Bristol, Bristol, UK
  • fYear
    2010
  • fDate
    13-18 June 2010
  • Firstpage
    410
  • Lastpage
    414
  • Abstract
    We consider a dense n-user Gaussian interference network formed by paired transmitters and receivers placed independently at random in Euclidean space. Under natural conditions on the node position distributions and signal attenuation, we prove convergence in probability of the average per-user capacity CΣ/n to ½ E log(1 + 2SNR). The achievability result follows directly from results based on an interference alignment scheme presented in recent work of Nazer et al. Our main contribution comes through the converse result, motivated by ideas of `bottleneck links´ developed in recent work of Jafar. An information theoretic argument gives a capacity bound on such bottleneck links, and probabilistic counting arguments show there are sufficiently many such links to tightly bound the sum-capacity of the whole network.
  • Keywords
    Gaussian processes; probability; radio networks; radio receivers; radio transmitters; radiofrequency interference; Euclidean space; asymptotic sum capacity; average per-user capacity; bottleneck links; information theory; interference alignment scheme; node position distributions; probability; random n-user Gaussian interference networks; receivers; signal attenuation; transmitters; Attenuation; Capacity planning; Communication networks; Convergence; Frequency conversion; H infinity control; Mathematics; Multiple access interference; Polynomials; Transmitters; Networks; capacity; interference alignment; interference network; sum-capacity;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory Proceedings (ISIT), 2010 IEEE International Symposium on
  • Conference_Location
    Austin, TX
  • Print_ISBN
    978-1-4244-7890-3
  • Electronic_ISBN
    978-1-4244-7891-0
  • Type

    conf

  • DOI
    10.1109/ISIT.2010.5513390
  • Filename
    5513390