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 user U1, 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. Index Terms-Beyond 5G (B5G), effective capacity, low latency, non-orthogonal multiple access (NOMA), quality of service (QoS), user-pairing.
In the fifth generation and beyond (B5G), delay constraints emerge as a topic of particular interest, e.g. for ultra-reliable low latency communications (URLLC) such as autonomous vehicles and enhanced reality. In this paper, we study the performance of a two-user uplink NOMA network under statistical quality of service (QoS) delay constraints, captured through each user's effective capacity (EC). We propose novel closed-form expressions for the EC of the NOMA users and show that in the high signal to noise ratio (SNR) region, the "strong" NOMA user has a limited EC, assuming the same delay constraint as the "weak" user. We demonstrate that for the weak user, OMA achieves higher EC than NOMA at small values of the transmit SNR, while NOMA outperforms OMA in terms of EC at high SNRs. On the other hand, for the strong user the opposite is true, i.e., NOMA achieves higher EC than OMA at small SNRs, while OMA becomes more beneficial at high SNRs. This result raises the question of introducing "adaptive" OMA / NOMA policies, based jointly on the users' delay constraints as well as on the available transmit power.
In the fifth generation and beyond (B5G), delay constraints emerge as a topic of particular interest, e.g. for ultra-reliable low latency communications (URLLC) such as autonomous vehicles and enhanced reality. In this paper, we study the performance of a two-user uplink NOMA network under statistical quality of service (QoS) delay constraints, captured through each user's effective capacity (EC). We propose novel closed-form expressions for the EC of the NOMA users and show that in the high signal to noise ratio (SNR) region, the "strong" NOMA user has a limited EC, assuming the same delay constraint as the "weak" user. We demonstrate that for the weak user, OMA achieves higher EC than NOMA at small values of the transmit SNR, while NOMA outperforms OMA in terms of EC at high SNRs. On the other hand, for the strong user the opposite is true, i.e., NOMA achieves higher EC than OMA at small SNRs, while OMA becomes more beneficial at high SNRs. This result raises the question of introducing "adaptive" OMA / NOMA policies, based jointly on the users' delay constraints as well as on the available transmit power.
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