• DocumentCode
    2201395
  • Title

    Optimization of power and channel allocation using the deterministic channel model

  • Author

    Zhao, Yue ; Pottie, Gregory J.

  • Author_Institution
    Dept. of Electr. Eng., Univ. of California, Los Angeles, Los Angeles, CA, USA
  • fYear
    2010
  • fDate
    Jan. 31 2010-Feb. 5 2010
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    In a multiuser interference channel, solving the optimal power and channel allocation for a weighted sum-rate maximization is a well-known non-convex problem, and has NP complexity. In this paper, we apply the recently developed deterministic channel model, and obtain a new formulation for this classic problem. Although the non-convex nature remains unavoidable, we exploit novel insights and techniques to significantly reduce the algorithm´s complexity, while still guaranteeing its asymptotic optimality. For cellular structured networks with a fixed number of cells, our algorithm has a worst-case polynomial complexity. We provide simulation solutions of this non-convex optimization in a seven-cell network. The proposed algorithm also computes performance upper bounds in all simulation cases as a numerical verification of the solutions´ optimality. The upper bounds demonstrate very small gaps from the maximum achieved objective values of the simulation solutions.
  • Keywords
    cellular radio; channel allocation; computational complexity; multiuser channels; optimisation; polynomials; radiofrequency interference; NP complexity; cellular structured networks; deterministic channel model; multiuser interference channel; numerical verification; optimal power-channel allocation; polynomial complexity; Cells (biology); Cellular networks; Channel allocation; Computational modeling; Gaussian noise; Interference channels; Noise level; Polynomials; Power system modeling; Upper bound;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory and Applications Workshop (ITA), 2010
  • Conference_Location
    San Diego, CA
  • Print_ISBN
    978-1-4244-7012-9
  • Electronic_ISBN
    978-1-4244-7014-3
  • Type

    conf

  • DOI
    10.1109/ITA.2010.5454096
  • Filename
    5454096