Robust Power Allocation for OFDM-Based Cognitive Radio Networks: A Switched Affine Based Control Approach | Zendy

Shi Pan | Zendy; Xiaohui Zhao | Zendy; Ying-Chang Liang | Zendy

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Robust Power Allocation for OFDM-Based Cognitive Radio Networks: A Switched Affine Based Control Approach

Author(s) -

Shi Pan,

Xiaohui Zhao,

Ying-Chang Liang

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2751565

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

In this paper, we investigate the robust power allocation issue in orthogonal frequency division multiplexing-based cognitive radio networks (CRNs) with unavoidable uncertainties (channel perturbations and variable environment). In this case, to control the performance degradation due to the uncertainties, we maximize the data rate of secondary users (SUs) by considering maximum allowable interference constraints and total power budget of SUs. To solve this problem, we design a controller for a switched affine system with state constraint. This system is based on a distributed projected dynamic system in accordance with the classical distributed convex optimization model for the power allocation and dynamic property of the Nash equilibrium. The robust controller design is on the basis of Lyapunov stability theory and linear matrix inequality to better realize the original power allocation from the control perspective. To the best of our knowledge, we are the first to solve the aforementioned problem by this kind of approach under the control frame, which is more practical for the realization in CRNs. Simulation results are provided to show the validation of the effectiveness of this approach in comparison with iterative water filling algorithm and the worst-case method.

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