A Robust Game-Based Algorithm for Downlink Joint Resource Allocation in Hierarchical OFDMA Femtocell Network System

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 50; no. 7; pp. 2445 - 2455
• Main Authors: Yu, Junzhi, Han, Song, Li, Xinbin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Communication systems; Complexity; Downlink; Downlinking; Femtocell networks; Frequency division multiple access; Game theory; Games; hierarchical systems; Interference; joint resource allocation; Orthogonal Frequency Division Multiplexing; Particle swarm optimization; Resource allocation; Resource management; Robustness; Stations; Uncertainty; Wireless networks
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2018.2817586

Femtocell is a promising technology for wireless service networks to facilitate sustainable and efficient services for users. This paper deals with a downlink joint channel assignment and power allocation problem with multiple channels, users, constraints, and uncertainties in the orthogonal frequency division of a multiple-access hierarchical femtocell network system. Specifically, a hierarchical robust Stackelberg game, which aims to achieve robust equilibrium, is first proposed for resource allocation with uncertainties. Then, a low-complexity, low-interference, high-efficiency, and high-performance algorithm is presented to handle the complex robust joint allocation problem. Considering the demand capacity of macro-base stations, an efficient fitness function in conjunction with particle swarm optimization-constriction factor, is utilized to yield the best response in the upper game to satisfy multiple constraints. Meanwhile, an iterative waterfilling algorithm is exploited to achieve the best response of femto-base stations in the lower game. Lastly, a stop protocol is established for two-tier users to accomplish an efficient robust Stackelberg equilibrium. Comparative results demonstrated that the proposed algorithm is superior to the existing game-based algorithms.