Power-Domain Non-Orthogonal Multiple Access (NOMA) in 5G Systems: Potentials and Challenges
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Abstract (arXiv)
Non-orthogonal multiple access (NOMA) is one of the promising radio access techniques for performance enhancement in next-generation cellular communications. Compared to orthogonal frequency division multiple access (OFDMA), which is a well-known high-capacity orthogonal multiple access (OMA) technique, NOMA offers a set of desirable benefits, including greater spectrum efficiency. There are different types of NOMA techniques, including power-domain and code-domain. This paper primarily focuses on power-domain NOMA that utilizes superposition coding (SC) at the transmitter and successive interference cancellation (SIC) at the receiver. Various researchers have demonstrated that NOMA can be used effectively to meet both network-level and user-experienced data rate requirements of fifth-generation (5G) technologies. From that perspective, 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. In addition, this paper discusses how NOMA performs when it is integrated with various proven wireless communications techniques, such as cooperative communications, multiple input multiple output (MIMO), beamforming, space time coding, and network coding, among others. Furthermore, this paper discusses several important issues on NOMA implementation and provides some avenues for future research.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Sep 2016.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 2; 2024: 2; 2025: 1; 2026: 1.
10 papers.
- 2023Non-Orthogonal Multiple Access Enhanced Multi-User Semantic Communicationcited by 6
- 2021Joint Resource Management for MC-NOMA: A Deep Reinforcement Learning Approachcited by 5
- 2023A Weighted Autoencoder-Based Approach to Downlink NOMA Constellation Designcited by 5
- 2024Modulation and Coding for NOMA and RSMAcited by 4
- 2025Successive Interference Cancellation-aided Diffusion Models for Joint Channel Estimation and Data Detection in Low Rank Channel Scenarioscited by 4
- 2021Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMAcited by 1
- 2022GPU-accelerated partially linear multiuser detection for 5G and beyond URLLC systemscited by 1
- 2024Design Optimization of NOMA Aided Multi-STAR-RIS for Indoor Environments: A Convex Approximation Imitated Reinforcement Learning Approachcited by 1
- 2022GPU-Accelerated Machine Learning in Non-Orthogonal Multiple Accesscited by 0
- 2026Neutral-Atom-based Quantum Optimization for Resource Allocation in NOMA Networkscited by 0
- 2026Neutral-Atom-based Quantum Optimization for Resource Allocation in NOMA Networks22 Sep 2026
- 2025Successive Interference Cancellation-aided Diffusion Models for Joint Channel Estimation and Data Detection in Low Rank Channel Scenarios20 Jan 2025
- 2024Modulation and Coding for NOMA and RSMA30 Sep 2024
- 2024Design Optimization of NOMA Aided Multi-STAR-RIS for Indoor Environments: A Convex Approximation Imitated Reinforcement Learning Approach19 Jun 2024
- 2023A Weighted Autoencoder-Based Approach to Downlink NOMA Constellation Design23 Jun 2023
- 2023Non-Orthogonal Multiple Access Enhanced Multi-User Semantic Communication12 Mar 2023
- 2022GPU-Accelerated Machine Learning in Non-Orthogonal Multiple Access13 Jun 2022
- 2022GPU-accelerated partially linear multiuser detection for 5G and beyond URLLC systems13 Jan 2022
- 2021Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA3 Apr 2021
- 2021Joint Resource Management for MC-NOMA: A Deep Reinforcement Learning Approach29 Mar 2021