Evaluation of Intra-Subband Power Allocation for a Downlink Non-Orthogonal Multiple Access (NOMA) System

Abstract

Non-Orthogonal Multiple Access (NOMA) schemes superpose multiple users in the power domain. It is commonly assumed that the achieved throughput in a downlink NOMA system increases with the channel gain difference between paired users. This paper investigates the accuracy of this claim when different intra-subband power allocation techniques are studied. Moreover, the Proportional Fairness (PF) scheduler is used as a resource allocation mechanism to highlight the importance of the choice of user pairing and of the intra-subband power allocation scheme. Results show that the achieved throughput does not always evolve in the same direction as the channel gain difference between paired users, owing to the choice of the intra-subband power allocation technique. In addition, it was found that the PF scheduler is not fully adapted to NOMA.

An Efficient Resource Allocation Algorithm for OFDM-Based NOMA in 5G Systems

“…As can be seen from Figure 5, the achievable sum-rate increased with increasing the total power to noise ratio for all algorithms. The results also indicate that the proposed algorithm effectively showed better overall sum rate performance as compared with the references [20,21,29] algorithms. The results also gave important information that the proposed algorithm was more robust in noisy environments than the existing state-of-the-art schemes.…”

“…The main simulation parameters are shown in Table 1. Figure 2 compares the total system throughput of the proposed user grouping algorithm with the reference [29] algorithm, exhaustive grouping algorithm of [20] and the random grouping algorithm of [21]. After using these four algorithms to group users of all sub-carriers in the cell, combined with the linear water filling-based power allocation algorithm proposed in this paper, the power allocation was performed on the users on each sub-carrier, and the total cell throughput obtained by the four algorithms was compared.…”

“…It can be seen from Figure 2 that the throughput performance of the proposed algorithm was better than that of the references [20,29], and also the search range was reduced and the complexity was greatly reduced. In addition, compared with the reference [21] algorithm, the proposed algorithm could effectively improve the total cell throughput. This is because the user grouping algorithm of the reference [21] completely separates the users into several sets, and does not take into account the influence of the channel state information (CSI) of each user, resulting in poor system throughput performance.…”

“…In addition, compared with the reference [21] algorithm, the proposed algorithm could effectively improve the total cell throughput. This is because the user grouping algorithm of the reference [21] completely separates the users into several sets, and does not take into account the influence of the channel state information (CSI) of each user, resulting in poor system throughput performance. Figure 2 that the throughput performance of the proposed algorithm was better than that of the references [20,29], and also the search range was reduced and the complexity was greatly reduced.…”

“…The results also gave important information that the proposed algorithm was more robust in noisy environments than the existing state-of-the-art schemes. Figure 6 compares the sum rate against the number of iterations of the proposed algorithm and the references [20,21,29] and OMA algorithms. As can be seen from Figure 6 the sum-rate increased with an increasing number of iterations for all the algorithms.…”