Distributed Interference-Aware Power Control in Ultra-Dense Small Cell Networks: A Robust Mean Field Game

Abstract
In ultra-dense small cell networks, interference mitigation is very important due to severe interference. Interference dynamics caused by time-varying environment should be aware and characterized when an interference-aware power control policy is designed to mitigate interference. Meanwhile, interference perception should not be naturally assumed to have complete information with certainty. Generally, it’s known that a generic player will react to all the players’ actions and states in a power control game, which involves huge interferencerelated information exchange with dynamics and uncertainties. Therefore, to reduce requirements of complete information, we formulate a robust power control mean field game taking the uncertainties of both state dynamics and cost functions into consideration. To achieve the robust power control, we regard the power control problem as a game with players whose individual states are combined by a disturbance term and a Brownian motion. We derive the robust Fokker-Planck-Kolmogorov(FPK) and Hamilton-Jacobi-Bellman(HJB) equations, and based on which we propose the robust interference-aware power control algorithm. Simulation results demonstrate the improved performance and the robustness of the proposed algorithm.
Funding Information
  • National Science Foundation of China (61231008, 91638202)
  • Ministerio de Economía, Industria Competitividad (TEC2017-85587-R)
  • Special Financial Grant from the China Postdoctoral Science Foundation (2016T90894)
  • Special Financial Grant from the Shaaxi Postdoctoral Science Foundation (154066)
  • CETC Key Laboratory of Data Link Technology (20162309)
  • Natural Science Basic Research Plan in Shaanxi Province of China (2017JZ021)
  • State Key Laboratory on Integrated Services Networks (ISN02080001)
  • 111 Project (B08038)
  • Shaanxi Province Science and Technology Research and Development Program (2011KJXX-40)
  • U.S. NSF (CNS-1717454, CNS-1731424, CNS-1702850, CNS-1646607, ECCS-1547201, CMMI-1434789, CNS-1443917, ECCS-1405121)