This paper focuses on boosting the performance of small cell networks (SCNs) by integrating multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) in consideration of imperfect channel-state information (CSI). The estimation error and the spatial randomness of base stations (BSs) are characterized by using Kronecker model and Poisson point process (PPP), respectively. The outage probabilities of MIMO-NOMA enhanced SCNs are first derived in closed-form by taking into account two grouping policies, including random grouping and distance-based grouping. It is revealed that the average outage probabilities are irrelevant to the intensity of BSs in the interference-limited regime, while the outage performance deteriorates if the intensity is sufficiently low. Besides, as the channel uncertainty lessens, the asymptotic analyses manifest that the target rates must be restricted up to a bound to achieve an arbitrarily low outage probability in the absence of the inter-cell interference.Moreover, highly correlated estimation error ameliorates the outage performance under a low quality of CSI, otherwise it behaves oppositely. Afterwards, the goodput is maximized by choosing appropriate precoding matrix, receiver filters and transmission rates. In the end, the numerical results verify our analysis and corroborate the superiority of our proposed algorithm.
翻译:本文侧重于提高小型细胞网络(SCNs)的性能,考虑到不完善的频道状态信息(CSI),将多投入多输出(MIMO)和非横向多存(NOMA)结合起来,从而提高小型细胞网络(SCNs)的性能。基站(BS)的估计错误和空间随机性特征的特征是分别使用Kronecker模型和Poisson点进程(PPP),而IMO-NOMA增强的SCNs的外差概率首先在封闭形式中产生,考虑到两种组合政策,包括随机分组和远程分组。据发现,平均超值概率与干扰有限系统中BSs的强度无关,而基站(BSs)的超值性能则在强度足够低的情况下恶化。此外,随着频道不确定性的减少,无症状分析表明,目标率必须限制到一定达到一个任意低的外差概率,而没有细胞间干扰。 模拟、高度关联的估计错误改善了CSI低质量的外差性性能,另外,通过适当的筛选分析, 也表现了我们最优的递性分析。