Vol.70, No.1, 2022, pp.195-212, doi:10.32604/cmc.2022.017105
Resource Allocation for Throughput Maximization in Cognitive Radio Network with NOMA
  • Xiaoli He1, Yu Song2,3,*, Yu Xue4, Muhammad Owais5, Weijian Yang1, Xinwen Cheng1
1 School of Computer Science, Sichuan University of Science and Engineering, Zigong, 643000, China
2 Department of Network Information Management Center, Sichuan University of Science and Engineering, Zigong, 643000, China
3 School of Information Engineering, South West University of Science and Technology, Mianyang, 621010, China
4 School of Computer and Software, Nanjing University of Information Science and Technology, Jiangsu, 210044, China
5 Alhamd Islamic University Airport Road Quetta, Balochistan, Pakistan
* Corresponding Author: Yu Song. Email:
Received 18 January 2021; Accepted 21 February 2021; Issue published 07 September 2021
Spectrum resources are the precious and limited natural resources. In order to improve the utilization of spectrum resources and maximize the network throughput, this paper studies the resource allocation of the downlink cognitive radio network with non-orthogonal multiple access (CRN-NOMA). NOMA, as the key technology of the fifth-generation communication (5G), can effectively increase the capacity of 5G networks. The optimization problem proposed in this paper aims to maximize the number of secondary users (SUs) accessing the system and the total throughput in the CRN-NOMA. Under the constraints of total power, minimum rate, interference and SINR, CRN-NOMA throughput is maximized by allocating optimal transmission power. First, for the situation of multiple sub-users, an adaptive optimization method is proposed to reduce the complexity of the optimization solution. Secondly, for the optimization problem of nonlinear programming, a maximization throughput optimization algorithm based on Chebyshev and convex (MTCC) for CRN-NOMA is proposed, which converts multi-objective optimization problem into single-objective optimization problem to solve. At the same time, the convergence and time complexity of the algorithm are verified. Theoretical analysis and simulation results show that the algorithm can effectively improve the system throughput. In terms of interference and throughput, the performance of the sub-optimal solution is better than that of orthogonal-frequency-division-multiple-access (OFDMA). This paper provides important insights for the research and application of NOMA in future communications.
Resource allocation; non-orthogonal multiple access; cognitive radio network; throughput maximization; Chebyshev; convex
Cite This Article
He, X., Song, Y., Xue, Y., Owais, M., Yang, W. et al. (2022). Resource Allocation for Throughput Maximization in Cognitive Radio Network with NOMA. CMC-Computers, Materials & Continua, 70(1), 195–212.
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