Learning in Markov Game for Femtocell Power Allocation with Limited Coordination
Description
In this paper, we study the power allocation problem for the downlink transmission in a set of closed-access femtocells which overlay a number of macrocells. We introduce a mutli-step pricing mechanism for the macrocells to control the cross-tier interference by femtocell transmissions without explicit coordination. We model the cross-tier joint power allocation process in the heterogeneous network as a non-cooperative, average-reward Markov game. By investigating the structure of the instantaneous payoff functions in the game, we propose a self-organized strategy learning scheme based on learning automata for both the macrocell base stations and the femtocell access points to adapt their transmit power simultaneously. We prove that the proposed learning scheme is able to find a pure-strategy Nash equilibrium of the game without the need for the femtocell access points to share any local information. Simulation results show the efficiency of the proposed learning scheme.
Date of creation, presentation, or exhibit
12-2016
Document Type
Conference Paper
Department, Program, or Center
Computer Science (GCCIS)
Recommended Citation
W. Wang, P. Huang, P. Hu, J. Na and A. Kwasinski, "Learning in Markov Game for Femtocell Power Allocation with Limited Coordination," 2016 IEEE Global Communications Conference (GLOBECOM), Washington, DC, USA, 2016, pp. 1-6. doi: 10.1109/GLOCOM.2016.7841950
Campus
RIT – Main Campus
Comments
Presented at the IEEE Global Communications Conference (GLOBECOM), Washington, DC, USA, 2016
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