In this article, 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 $U_{2}$ , has a limited EC, assuming the same delay constraint as the “weak” user, referred to as $U_{1}$ . We demonstrate that for the weak user $U_{1}$ , 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 $U_{2}$ , 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 $U_{1}$ , and OMA over NOMA for $U_{2}$ 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.

中文翻译：

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统计QoS时延约束下NOMA上行网络的渐近性能分析。

在本文中，我们研究了在统计服务质量（QoS）延迟约束下，通过每个用户的有效容量（EC）捕获的上行链路非正交多址（NOMA）网络的性能。我们首先为两用户NOMA网络中的EC提出新颖的闭式表达式，并表明在高信噪比（SNR）区域中，“强” NOMA用户被称为 $ U_ {2} $ ，具有有限的EC，并假设与“弱”用户相同的延迟约束，称为 $ U_ {1} $ 。我们证明了对于弱用户 $ U_ {1} $ ，OMA和NOMA在低发射SNR时具有可比的性能，而NOMA在高SNR时的EC优于OMA。另一方面，对于强大的用户 $ U_ {2} $ ，在低SNR时，NOMA的OEC高于OMA，而在高SNR时，OMA更具优势。此外，我们表明，在高发射SNR的情况下，无论应用程序是否具有延迟容忍性，NOMA相对于OMA的性能增益对​​于 $ U_ {1} $ 和OMA超过NOMA $ U_ {2} $ 保持不变。当一个用户的延迟QoS固定时，就总EC而言，NOMA和OMA之间的性能差距随着另一用户的统计延迟QoS约束的减少而增大。接下来，通过介绍配对，我们显示出在总上行链路EC方面，具有用户配对功能的NOMA优于OMA。在有四个和六个用户的NOMA的情况下，给出了最佳的配对策略，这再次提高了功率分配在NOMA性能优化中的重要性。