Performance Analysis of NOMA Uplink Networks under Statistical QoS Delay Constraints

Abstract

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.

On the Optimal Power Allocation at the MAC layer in the Asymptotic and in the Finite Blocklength

“…2 depicts the comparison of NOMA and OMA in terms of the EC, using the proposed power allocation policy. In fact, we showed in [12] that with fixed power coefficients, α 1 = 0.2 and α 2 = 0.8, OMA is more advantageous than NOMA for low-medium transmit SNRs, while NOMA outperforms OMA at high transmit SNRs for the weak user U 1 . Reverse conclusions is drawn for the strong user U 2 .…”

“…The objective function ( 12) is an increasing function for α 2 , thus the solution of [P1] can be directly obtained from the constraints. However, due to the complexity of the closedform expressions of E 1 c and E 2 c [12], it is almost impossible to find the optimal power coefficients for the whole range of SNRs. We propose here to solve this problem in the extreme SNRs, i.e.…”

Energy‐Efficient Resource Allocation for NOMA‐Enabled Internet of Vehicles

“…We compare the effect of different transmission rate thresholds R min on the system EE, as shown in Figure 5. According to Equation (7), when the transmission rate R ðtÞ n,l,k < R min , VR l cannot successfully decode the information from VT n , and we set R ðtÞ n,l,k = 0 in this case. That is, P ðtÞ n,l,k > 0 but R ðtÞ n,l,k = 0, which will seriously affect the system EE.…”

“…Such problems have been solved with the rise of 5th generation (5G) mobile networks. 5G introduces nonorthogonal multiple access (NOMA) technology that allows a resource block to be assigned to multiple users, thus greatly expanding the amount of access to the network [7]. In some cases, such as uplink communication intensive scenarios, NOMAenabled system has a significant performance improvement compared to OMA system.…”

On the Optimal Power Allocation and User Pairing for Uplink Non-Orthogonal Multiple Access Networks

“…To alleviate such issues, NOMA seems more realistic for small groups of superimposed users, in practice two or three and many related studies exist. In this direction, power allocation and user-pairing play an important role in optimizing NOMA's performance [3]- [10].…”