Asymptotic Performance Analysis of NOMA Uplink Networks Under Statistical QoS Delay Constraints

Abstract

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.

Statistical Power Grid Observability under NOMA-based Communication Constraints

“…is the normalized QoS exponent [15]. The parameter θ k is related to the probability of successful transmission within the delay constraint as…”

“…Finally, (22) means that the observability of the power grid is ensured, i.e., above a given threshold O th . (15) The optimization problem in ( 15) is very challenging. First of all, there are expectations in the constraints which are hard to evaluate.…”

“…where the parameter m j is called the penalty factor, which will continue to increase with iteration, whose index is given by j. The proposed algorithm to solve the problem in (15) for a fixed G is given by Algorithm 2.…”

Rate Analysis and Deep Neural Network Detectors for SEFDM FTN Systems

“…As in SEFDM the complexity burden is moved to the receiver, it is better suited for the uplink rather Arsenia Chorti is with ETIS UMR8051 CY Cergy Paris Université, ENSEA, CNRS, France and David Picard is with the École des Ponts ParisTech. than the downlink; e.g., for massive machine type communications (mMTC) in which the bandwidth crunch is more accentuated, SEFDM can provide a possible alternative to nonorthogonal multiple access (NOMA) that requires coordination between the users [14].…”

Low-Latency Driven Performance Analysis for Single-Cluster NOMA Networks

“…However, [8] and [9] focus on designing the power control policy, and the analysis in terms of delay-constrained achievable rate is not involved. Analytical expressions of EC for downlink NOMA transmissions are provided in [10] and [11], and the EC for uplink NOMA is considered in [12], but all studies consider user-paired NOMA, where all N users are separated into N 2 clusters each with two users. To the best of our knowledge, the performance of the EC for single-cluster NOMA, where all users are in one cluster and share the same resource via NOMA, has not been explored in the existing literature.…”