论文标题
NOMA上行链路网络在统计QoS延迟约束下对NOMA上行链路网络的渐近性能分析
Asymptotic Performance Analysis of NOMA Uplink Networks Under Statistical QoS Delay Constraints
论文作者
论文摘要
在本文中,我们研究了在统计服务质量(QoS)延迟约束下,上行链路非正交多访问(NOMA)网络的性能,这些延迟限制是通过每个用户的有效容量(EC)捕获的。我们首先在两用户NOMA网络中提出了EC的新型闭合形式表达式,并表明,在高信噪比(SNR)区域中,“强” Noma用户(称为U2)具有有限的EC,假定与“弱”用户相同的延迟约束,称为U1。我们证明,对于较弱的userU1,OMA和NOMA在低递质SNRS时具有可比的性能,而Noma在高SNRS的EC方面的表现优于OMA。另一方面,对于强大的用户U2,NOMA在小SNR中的EC比OMA高,而OMA在高SNR中变得更加有益。此外,我们表明,在高传输SNR中,无论应用是否延迟耐受性,NOMA在U1上的性能增长,而U2的NOMA对NOMA的性能保持不变。当一个用户的延迟QoS固定时,Noma和OMA之间的性能差距会随着另一个用户的统计延迟QOS约束而增加。接下来,通过介绍配对,我们表明,与总上行链路EC一起,NOMA的用户对优于OMA。最好的配对策略是在四个和六个用户Noma的情况下给出的,再次提高了电力分配在优化Noma的性能中的重要性。
In this paper, we study the performance of an uplink non-orthogonal multiple access (NOMA) network under statistical quality of service (QoS) delay constraints, captured through each user s effective capacity (EC). We first propose novel closed-form expressions for the EC in a two-user NOMA network and show that in the high signal-to-noise ratio (SNR) region, the 'strong' NOMA user, referred to as U2, has a limited EC, assuming the same delay constraint as the 'weak' user, referred to as U1. We demonstrate that for the weak userU1, OMA and NOMA have comparable performance at low transmit SNRs, while NOMA outperforms OMA in terms of EC at high SNRs. On the other hand, for the strong user U2, NOMA achieves higher EC than OMA at small SNRs, while OMA becomes more beneficial at high SNRs. Furthermore, we show that at high transmit SNRs, irrespective of whether the application is delay tolerant, or not, the performance gains of NOMA over OMA for U1, and OMA over NOMA for U2 remain unchanged. When the delay QoS of one user is fixed, the performance gap between NOMA and OMA in terms of total EC increases with decreasing statistical delay QoS constraints for the other user. Next, by introducing pairing, we show that NOMA with user-pairing outperforms OMA, in terms of total uplink EC. The best pairing strategies are given in the cases of four and six users NOMA, raising once again the importance of power allocation in the optimization of NOMA s performance.