Resource Allocation for Downlink NOMA Systems: Key Techniques and Open Issues
TLDR
In this paper, the authors proposed a divide-and-next-largest-difference-based user pairing algorithm to distribute the capacity gain among the NOMA clusters in a controlled manner.Abstract:
This article presents advances in resource allocation for downlink non-orthogonal multiple access (NOMA) systems, focusing on user pairing and power allocation algorithms. The former pairs the users to obtain high capacity gain by exploiting the channel gain difference between the users, while the latter allocates power to users in each cluster to balance system throughput and user fairness. Additionally, the article introduces the concept of cluster fairness and proposes the divide-and-next-largest-difference-based user pairing algorithm to distribute the capacity gain among the NOMA clusters in a controlled manner. Furthermore, performance comparison between multiple-input multiple-output NOMA (MIMO-NOMA) and MIMO orthogonal multiple access (MIMO-OMA) is conducted when users have pre-defined quality of service. Simulation results are presented, which validate the advantages of NOMA over OMA. Finally, the article provides avenues for further research on resource allocation for downlink NOMA.read more
Citations
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Energy-Efficient Power Allocation for Uplink NOMA
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Jammer-Aided Secure Communications for Cooperative NOMA Systems
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Power Minimization for Multi-Cell Uplink NOMA With Imperfect SIC
TL;DR: In this article, the authors investigated a multi-cell uplink NOMA system with imperfect successive interference cancellation (SIC) and proposed centralized and distributed optimal solutions to minimize the total power consumption under users' quality-of-service constraints.
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Impact of User Pairing on 5G Nonorthogonal Multiple-Access Downlink Transmissions
TL;DR: Both analytical and numerical results are provided to demonstrate that F-NOMA can offer a larger sum rate than orthogonal MA, and the performance gain of F- NOMA over conventional MA can be further enlarged by selecting users whose channel conditions are more distinctive.
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