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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NOMA and future 5G & B5G wireless networks: A paradigm
TL;DR: In this paper , a survey on NOMA and future 5G and B5G wireless communication networks is presented, which includes requirements and technologies for 5G, channel modeling, the role of nOMA in 5G/B5G, types of nomaa, NOMa's network architecture, mobility management (MM) in NOMaa, asynchronous and synchronous operations in NomA, energy and green aspects of nomo, Nomo's challenges, solutions to these challenges, nomo's performance indicators, and future research directions for next-generation wireless communication network.
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A Survey of Rate-optimal Power Domain NOMA Schemes for Enabling Technologies of Future Wireless Networks.
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Hybrid NOMA/OMA-Based Dynamic Power Allocation Scheme Using Deep Reinforcement Learning in 5G Networks
TL;DR: A deep actor-critic reinforcement learning (DACRL) algorithm is proposed to respectively model the policy function and value function for the actor and critic of the agent (i.e., the CBS), in which the actor can learn about system dynamics by interacting with the environment.
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Energy Efficiency Maximization in Downlink Multi-Cell Multi-Carrier NOMA Networks With Hardware Impairments
TL;DR: Simulation results demonstrate that the proposed algorithm achieves a performance comparable to that of the successive lower bound maximization (SLBM) algorithm and outperforms both fractional transmit power allocation benchmark for NOMA and the conventional orthogonal multiple access (OMA).
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Energy Efficiency–Delay Tradeoff for a Cooperative NOMA System
TL;DR: The tradeoff between the energy efficiency and delay problem in the cooperative relaying system is studied by using non-orthogonal multiple access (NOMA) and the fractional programming and control parameter-based Lyapunov optimization method is proposed to solve the formulated problem.
References
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Power-Domain Non-Orthogonal Multiple Access (NOMA) in 5G Systems: Potentials and Challenges
TL;DR: This paper comprehensively surveys the recent progress of NOMA in 5G systems, reviewing the state-of-the-art capacity analysis, power allocation strategies, user fairness, and user-pairing schemes in NomA.
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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.
Journal ArticleDOI
Power-Domain Non-Orthogonal Multiple Access (NOMA) in 5G Systems: Potentials and Challenges
TL;DR: In this paper, the authors comprehensively survey the recent progress of NOMA in 5G systems, reviewing the state-of-the-art capacity analysis, power allocation strategies, user fairness, and user-pairing schemes in NOMAs.