Abstract:
In this paper, we study the power allocation problem for the downlink transmission in a set of closed-access femtocells which underlay a number of macrocells. We introduc...Show MoreMetadata
Abstract:
In this paper, we study the power allocation problem for the downlink transmission in a set of closed-access femtocells which underlay 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.
Published in: 2016 IEEE Global Communications Conference (GLOBECOM)
Date of Conference: 04-08 December 2016
Date Added to IEEE Xplore: 06 February 2017
ISBN Information: