4.6 Article

A Kind of Novel Method of Power Allocation With Limited Cross-Tier Interference for CRN

期刊

IEEE ACCESS
卷 7, 期 -, 页码 82571-82583

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2921310

关键词

Cognitive radio network (CRN); power allocation; FBMC-OQAM; Lagrange dual

资金

  1. National Natural Science Foundation of China [61571328]
  2. Tianjin Key Natural Science Foundation [18JCZDJC96800]
  3. CSC Foundation [201308120010]
  4. Major Projects of Science and Technology in Tianjin [15ZXDSGX 00050]
  5. Training Plan of Tianjin University Innovation Team [TD12-5016, TD13-5025]
  6. Key Subject Foundation of Tianjin [15JCYBJC46500]
  7. Training Plan of Tianjin 131 Innovation Talent Team [TD2015-23]
  8. Major Projects of Science and Technology for their Services in Tianjin [16ZXFWGX00010, 17YFZC GX00360]

向作者/读者索取更多资源

A kind of novel method of power allocation with limited cross-tier interference for cognitive radio network (CRN) is proposed in this paper. In this method, an interference-limited power allocation algorithm based on filter bank multi-carrier-offset quadrature amplitude modulation (FBMC-OQAM) is put forward. In order to improve the energy efficiency of the entire network and protect secondary users (SUs) in the network from too much interference, cross-tier interference limit is adopted, at the same time, virtual queue is designed to transform the extra packet delay caused by the contention for the channel of multi-user into the queuing delay. Taking the energy efficiency as the objective function, a nonlinear programming approach with nonlinear constraints is innovatively proposed under the constraints of time delay and transmission power. An iterative algorithm in order to solve the problem is also put forward. In the new algorithm, the fractional objective function is transformed into polynomial form, and the global optimal solution is obtained by iteration after reducing the computational complexity. In addition, a sub-optimal algorithm is proposed to reduce computational complexity. The experimental results show that the optimal algorithm has higher performance while the sub-optimal algorithm has a lower computational complexity. The presented method has very good practical importance for the CRN.

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