In this paper, we present a power control algorithm in cognitive radio multiple-input–multiple-output (MIMO) system over time-varying fading channels based on the utility-based framework. In particular, a more general objective function framework is proposed under a long- or short-term transmitting and interference power constraints for secondary users, which results the optimization problem is nonconvex. A stochastic optimization algorithm is proposed based on an improved quantum-behaved particle swarm optimization (IQPSO), which can solve the nonconvex optimization problem efficiently. The acceleration coefficients are adjusted with time-varying equations which can help the particles jump out the local optimum. Due to the characteristics of wave function of quantum Delta potential well model, IQPSO can get better optimal solutions in search space for the stochastic optimization problem. To show the efficiency of the proposed algorithm, the utilities of the MIMO CR system got by IQPSO are compared with other approaches in the literature in different case studies, which show that the proposed algorithm can solve the nonconvex problem efficiently and achieve significant throughput.
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This work is supported by Natural Science Foundation of Shandong Grants 2015ZRB01121, National Natural Science Foundation of China under Grant 61374108, 61573179, and the Young Taishan Scholars Program of Shandong Province of China under Grant tsqn20161043.
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Tang, M., Wei, X. & Li, W. Nonconvex utility-based power allocation for cognitive radio MIMO system over fading channels. Soft Comput 23, 11925–11933 (2019). https://doi.org/10.1007/s00500-018-03746-x
- Cognitive radio (CR)
- Nonconvex optimization
- Particle swarm optimization (PSO)
- Power allocation