3rd International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications

Research Article

Power and Rate Control for Cognitive Radios: A Dynamic Programming Approach

  • @INPROCEEDINGS{10.1109/CROWNCOM.2008.4562462,
        author={long gao and Shuguang Cui},
        title={Power and Rate Control for Cognitive Radios: A Dynamic Programming Approach},
        proceedings={3rd International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications},
        publisher={IEEE},
        proceedings_a={CROWNCOM},
        year={2008},
        month={7},
        keywords={Cognitive Radio Power and Rate Control Dyanmic Programming},
        doi={10.1109/CROWNCOM.2008.4562462}
    }
    
  • long gao
    Shuguang Cui
    Year: 2008
    Power and Rate Control for Cognitive Radios: A Dynamic Programming Approach
    CROWNCOM
    IEEE
    DOI: 10.1109/CROWNCOM.2008.4562462
long gao1,*, Shuguang Cui1,*
  • 1: Department of Electrical and Computer Engineering Texas A&M University College Station, TX, 77843
*Contact email: lgao@ece.tamu.edu, cui@ece.tamu.edu

Abstract

In this paper, we investigate the power and rate control schemes for multiple cognitive radio (CR) links in the same neighborhood, which operate over multiple channels (frequency bands) in the presence of licensed primary radios (PRs). Specifically, by considering a practical delay in spectrum sensing, an efficient algorithm based on dynamic programming (DP) is proposed to maximize the average sum-rate of the CR links over a finite time horizon under the constraints on the CR-to-PR interference and the average transmit power for each CR link. In the proposed algorithm, the PR occupancy of each channel is modeled as a discrete-time Markov chain (DTMC). Based on such a model, a novel power and rate control strategy is derived, which is a function of the delayed spectrum sensing output, the instantaneous channel gains for the CR links, and the remaining power budgets for the CR transmitters. Simulation results show that the proposed algorithm leads to significant performance improvement over heuristic algorithms.