Distributed Resource Allocation for Multi-Cell Cognitive Radio Networks Based on Intra-Cell Overlay and Inter-Cell Underlay Spectrum Sharing

Hailan PENG  Takeo FUJII  

Publication
IEICE TRANSACTIONS on Communications   Vol.E96-B   No.6   pp.1566-1576
Publication Date: 2013/06/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.E96.B.1566
Print ISSN: 0916-8516
Type of Manuscript: PAPER
Category: Terrestrial Wireless Communication/Broadcasting Technologies
Keyword: 
distributed resource allocation,  game theory,  multi-cell overlaid CRN/PN,  OFDMA,  QoS,  

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Summary: 
In this paper, we consider a multi-cell cognitive radio network (CRN), which overlays a multi-cell primary network. To manage the coexistence, a primary-willingness based coexistent architecture and a novel intra-cell spectrum overlay and inter-cell spectrum underlay sharing method are proposed. In the system, primary base stations will broadcast pilot signals and interference margins to assist the CRN for interference channel evaluation and power control. Subject to the interference margins imposed by the primary network, we define a utility (payoff) function that can represent the secondary system performance while taking into account the co-channel interference among secondary cells. A distributed resource allocation scheme is devised to guarantee the primary performance, and at the same time, maximize the secondary utility without any cooperation among cognitive base stations (CBS). Quality of Service among users is also considered by the scheme such that the instantaneous data rate for each secondary user is larger than a given minimum rate. The resource allocation problem can be decomposed into two subproblems: subchannel allocation and distributed power allocation game (DPAG). We prove that there exists a Nash equilibrium in the DPAG and the equilibrium is unique. Moreover, the DPAG is also Pareto optimal in some constrained environments, that is, no CBS can further improve its performance without impairing others. The proposed algorithm turns out to converge to an equilibrium within a small number of iterations.