Downlink Analysis of NOMA-Enabled Cellular Networks With 3GPP-Inspired User Ranking

Abstract

This paper provides a comprehensive downlink analysis of non-orthogonal multiple access (NOMA) enabled cellular networks using tools from stochastic geometry. As a part of this analysis, we develop a novel 3GPP-inspired user ranking technique to construct a user cluster for the non-orthogonal transmission by grouping users from the cell center (CC) and cell edge (CE) regions. This technique allows to partition the users with distinct link qualities, which is imperative for the NOMA performance. Our analysis is focused on the performance of a user cluster in the typical cell, which is significantly different from the standard stochastic geometry-based approach of analyzing the performance of the typical user.For this setting, we first derive the moments of the meta distributions for the CC and CE users under NOMA and orthogonal multiple access (OMA). Using this, we then derive the distributions of the transmission rates and mean packet delays under non-real time (NRT) and real-time (RT) service models, respectively, for both CC and CE users. Finally, we study two resource allocation (RA) techniques with the objective of maximizing the cell sum rate (CSR) under NRT service, and the sum effective capacity (SEC) under RT service. In addition to providing several useful design insights, our results demonstrate that NOMA provides improved rate region and higher CSR as compared to OMA. In addition, we also show that NOMA provides better SEC as compared to OMA for the higher user density. Index TermsNon-orthogonal multiple access, user ranking, meta distribution, transmission rate, packet delay, cell sum rate, effective capacity, Poisson point process.The authors are with Wireless@VT, Bradley

Joint Spatial-Propagation Modeling of Cellular Networks Based on the Directional Radii of Poisson Voronoi Cells

“…In this case, simply pairing a cell-center user as the strong user and an edge user as the weak one may be quite inefficient, since the edge user may be closer to the BS than the "cell-center" user. Conversely, if "cell-center" and "cell-edge" are defined based on relative distances between serving and interfering base stations [24], [25], then a "cell-edge" user may actually be quite far from the edge of the cell. A potential model to pair users for Poisson Voronoi cells is to select a "cell-center" user uniformly at random inside the cell, and select an edge user whose angle differs only slightly from that of the "cell-center" user.…”

A User-Centric Frequency Reuse in Non-full Interference Cellular Networks

“…However, in classical full interference networks, since all BS are active, both aspects lead to similar conclusions on average and a user located at the cell border undergoes severe performance degradation. For instance, in [4], [5], [10], the ratio of the distance between the typical user and the serving BS to the distance between the typical user to the nearest interfering BS is computed. If the ratio is larger than a threshold, then the user is a cell edge user; otherwise, it is a cell center user.…”

“…In [8], the authors used the instanta-neous SINR based classification and got an approximation of the coverage probability of the typical cell edge user for PPPmodeled 3-tier heterogeneous networks. Within this general direction, the authors in [10] used the location-based cell center/edge user classification and derived the moments of the meta distribution under non-orthogonal/orthogonal multiple access techniques. All these previous works have been done with full interference assumption, i.e., considering that all BS, or a fraction of them but non-related to the coverage probability, act as interferers to the typical user whatever the bandwidth it uses.…”

Rayleigh Sönümlemeli Kanalda NOMA Üstünlük Koşulunun Belirlenmesi

“…In addition, interferences values depend on the power allocation and it is done based on user order inside a NOMA cluster but follows an opposite principle of water-filling mechanism. The most of the literature utilize a distance based ordering of the users due to its less complexity and analytical tractability [9][10][11][12][13][14][15]. In [9], NOMA and OMA spectrum efficiencies were compared according to their distance from the base station (BS) and the NOMA superiority condition was derived for both users assuming only a basic channel model where the channel gain depends a distance based channel model (path loss model).…”

“…ii) As the power allocation is crucial for the performance of NOMA, we investigate the effect of power ratios on NOMA superiority conditions. To the best of our knowledge, in the current literature [9,[13][14][15][16][17][18] about the NOMA superiority, where a distance based ranking of the users are utilized as a link quality metrics, neither the power control nor the effect of power ratios has not been considered. We also evaluate the accuracy of the derived boundary distance for NOMA superiority for different power ratios.…”