论文标题
$α$ - fairness用户对下行链路Noma系统的配对,并连续取消取消
$α$-Fairness User Pairing for Downlink NOMA Systems with Imperfect Successive Interference Cancellation
论文作者
论文摘要
非正交多访问(NOMA)被认为是下一代细胞网络的主要多重访问技术之一。我们考虑一个具有不完善的连续干扰取消(SIC)的2用户对下行链路NOMA系统。我们考虑对功率分配因子的界限,然后将功率分配作为优化问题,以实现配对用户之间的{$α$ - fairness}。我们表明,在完美的SIC和$α> 2 $的情况下,基于{$α$ - fairness}的功率分配因子与功率分配因子的下限一致。此外,只要满足提出的标准,它就会收敛到上限,而SIC中的缺陷越来越不完美。同样,我们表明,对于$ 0 <α<1 $,最佳功率分配因子与派生的下限分配相吻合。基于这些观察结果,我们提出了低复杂性亚最佳算法。通过广泛的模拟,我们分析了提出的算法的性能,并将性能与最新算法进行比较。我们表明,即使基于FAR的配对取得了比提议的算法更好的公平性,但它未能达到与其正交多访问相等的速率,而SIC中的缺陷越来越不完美。此外,我们表明,与最先进的算法相比,提出的最佳和亚最佳算法在公平性方面取得了重大改善。
Non-orthogonal multiple access (NOMA) is considered as one of the predominant multiple access technique for the next-generation cellular networks. We consider a 2-user pair downlink NOMA system with imperfect successive interference cancellation (SIC). We consider bounds on the power allocation factors and then formulate the power allocation as an optimization problem to achieve {$α$-Fairness} among the paired users. We show that {$α$-Fairness} based power allocation factor coincides with lower bound on power allocation factor in case of perfect SIC and $α> 2$. Further, as long as the proposed criterion is satisfied, it converges to the upper bound with increasing imperfection in SIC. Similarly, we show that, for $0<α<1$, the optimal power allocation factor coincides with the derived lower bound on power allocation. Based on these observations, we then propose a low complexity sub-optimal algorithm. Through extensive simulations, we analyse the performance of the proposed algorithm and compare the performance against the state-of-the-art algorithms. We show that even though Near-Far based pairing achieves better fairness than the proposed algorithms, it fails to achieve rates equivalent to its orthogonal multiple access counterparts with increasing imperfections in SIC. Further, we show that the proposed optimal and sub-optimal algorithms achieve significant improvements in terms of fairness as compared to the state-of-the-art algorithms.