Dynamic Spectrum Allocation Based on MEG Algorithm

Guangen WU  Pinyi REN  Zhou SU  

Publication
IEICE TRANSACTIONS on Communications   Vol.E94-B   No.11   pp.3077-3088
Publication Date: 2011/11/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.E94.B.3077
Print ISSN: 0916-8516
Type of Manuscript: PAPER
Category: Wireless Communication Technologies
Keyword: 
cognitive networks,  secondary spectrum market,  DSAS,  MEG algorithm,  

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Summary: 
Dynamic spectrum allocation (DSA) based on secondary spectrum market is considered a promising technology to improve spectrum utilization efficiency and to relieve the wireless spectrum shortage problem. We propose a dynamic spectrum allocation algorithm named market equilibrium and game (MEG), and construct a complete secondary spectrum market. The market based on the MEG algorithm consists of two submarkets: multiple primary services providers (PSPs) and a dynamic spectrum allocation server (DSAS) form the high submarket, while the low submarket is composed of the DSAS and a number of secondary users. In the low submarket, the MEG algorithm provides a game type selection strategy. By this strategy, the DSAS can win more payoffs with lower unit spectrum price, which encourages secondary users to use more spectrum. A secondary user can also choose its preferable game type between dynamic game and Nash bargaining flexibly. On the other hand, a bargaining procedure in the high submarket is designed in the MEG algorithm to ensure that market equilibrium is quickly reached. A performance analysis shows that the strategy of game type selection is fair and feasible for both the DSAS and the secondary users. Moreover, the bargaining procedure is better than the existing algorithm which adjusts price step by step in the high submarket. Simulation results also demonstrate that the market fluctuation in the low submarket is passed to the high submarket by way of the DSAS. The MEG algorithm can effectively satisfy the highly-fluctuating demands from the secondary users. In addition, the MEG algorithm can improve the payoffs of all players and increase spectrum utilization efficiency.