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-  2018 

Power Allocation with Proportional Fairness in Downlink NOMA System
Power Allocation with Proportional Fairness in Downlink NOMA System

DOI: 10.15918/j.jbit1004-0579.17088

Keywords: non-orthogonal multiple access (NOMA) power allocation proportional fairness logarithmic throughput
non-orthogonal multiple access (NOMA) power allocation proportional fairness logarithmic throughput

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Abstract:

The non-orthogonal multiple access (NOMA) has been regarded as a candidate radio access technology in the 5th generation (5G) mobile networks. In this paper, the problem of power allocation is considered with proportional fairness for downlink NOMA. Our objective is to maximize the logarithmic throughput of the system, which achieves a good tradeoff between the system throughput and user fairness. The approximate optimal solution of this proposed method can be found by the means of quasi-Newton method. For any number of users K, the numerical results verify the accuracy and convergence of our proposed method. Compared to the exhaustive search method, the computational complexity is significantly reduced when the two methods achieve the same accuracy. Furthermore, we show that as the number of users increases, the number of iterations of our method increases with an approximately linear trend.
The non-orthogonal multiple access (NOMA) has been regarded as a candidate radio access technology in the 5th generation (5G) mobile networks. In this paper, the problem of power allocation is considered with proportional fairness for downlink NOMA. Our objective is to maximize the logarithmic throughput of the system, which achieves a good tradeoff between the system throughput and user fairness. The approximate optimal solution of this proposed method can be found by the means of quasi-Newton method. For any number of users K, the numerical results verify the accuracy and convergence of our proposed method. Compared to the exhaustive search method, the computational complexity is significantly reduced when the two methods achieve the same accuracy. Furthermore, we show that as the number of users increases, the number of iterations of our method increases with an approximately linear trend.

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