Keywords
Non-Orthogonal Multiple Access (NOMA), Successive Interference Cancellation (SIC), Power Allocation, 5G and 6G Wireless Networks, Artificial Intelligence and Machine Learning, and Interference Management
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Ogenyi FC, Ugwu OPC, Eze VHU et al. Next-Generation Interference Management: A Narrative Review of power Allocation and SIC Enhancement in 5G/6G NOMA [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:888 (https://doi.org/10.12688/f1000research.169843.1)
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Review
Next-Generation Interference Management: A Narrative Review of power Allocation and SIC Enhancement in 5G/6G NOMA
[version 1; peer review: awaiting peer review]
PUBLISHED 09 Sep 2025
OPEN PEER REVIEW
REVIEWER STATUS
Abstract
Corresponding author:
Fabian Chukwudi Ogenyi
Competing interests:
No competing interests were disclosed.
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The author(s) declared that no grants were involved in supporting this work.
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© 2025 Ogenyi FC et al.
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite: Ogenyi FC, Ugwu OPC, Eze VHU et al. Next-Generation Interference Management: A Narrative Review of power Allocation and SIC Enhancement in 5G/6G NOMA [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:888 (https://doi.org/10.12688/f1000research.169843.1)
First published: 09 Sep 2025, 14:888 (https://doi.org/10.12688/f1000research.169843.1)
Latest published: 09 Sep 2025, 14:888 (https://doi.org/10.12688/f1000research.169843.1)
1. Introduction
The high speed of the development of wireless communication technologies is also dictated by the growing need for higher data rates, massive connectivity, and ultra-reliable low-latency communication (URLLC). The fifth-generation (5G) networks have already brought significant advancements compared to previous generations by providing better mobile broadband (eMBB) services, massive machine-type communication (mMTC), and critical communication services (Jain, 2025). Nevertheless, as the number of connected devices and data-intensive applications increases, it is clear that the demands of beyond-5G and 6G networks will be much more than those of 5G (De Alwis et al., 2021). Future networks will be required to provide not only very high throughput and ubiquitous connectivity but also more energy efficiency, effective utilization of spectrum and robustness against interference (Gu & Mohajer, 2024). These challenging demands have prompted scholars to investigate new multiple access strategies that would effectively utilize scarce spectral resources and be fair to users (Cao et al., 2024).
Non-orthogonal multiple access (NOMA) is one of the most promising candidates in this respect. In NOMA, several users simultaneously share the same resources in the frequency-time domain, unlike traditional orthogonal schemes that allocate resources, such as time, frequency, or code, to different users (Merin Joshiba et al., 2023). Such a spectrum-efficient method also makes it possible to multiplex users who have varying channel conditions, leading to a higher system capacity and connectivity among users (Gupta et al., 2025). In the 5G and beyond framework, NOMA has been regarded as one of the fundamental technologies that can enable massive connectivity of Internet of Things (IoT) devices, autonomous systems, and ultra-dense network deployment where the efficient use of scarce spectral resources is of paramount importance (Banafaa et al., 2024). The fact that NOMA can overlay user signals with different power levels has made it especially appealing to next-generation systems that are targeting the maximum spectrum efficiency without compromising fairness (Liu et al., 2022).
Successive Interference Cancellation (SIC) is at the heart of NOMA, as it enables the receiver to decode superimposed signals (Ghous et al., 2022). The base station in a typical downlink NOMA scenario applies more power to users with weaker channels and less power to the users with stronger channels (Qian et al., 2023). Stronger users use SIC to decode and subtract the signals of weaker users and then decode their signal, whereas weaker users just decode their assigned signal. This is an essential mechanism to translate the concept of NOMA into practice, as it allows multiple users to access shared resources at the same time, and intra-cell interference is minimized (Ni et al., 2021). The performance of SIC, however, is highly subject to the accuracy of power allocation techniques, channel state information (CSI), and decoding order, which makes its optimization a major focus of research (Gupta & Prakriya, 2022).
Although there are benefits, the challenges of integrating NOMA in 5G and beyond networks are not non-existent. The issue of interference management is one of the most important. Although SIC alleviates the intra-user interference, its performance can be degraded by lack of accurate channel estimation, error propagation during the decoding process, and the remaining interference due to the incomplete cancellation (Godugu et al., 2025). Furthermore, the power allocation is also difficult, since it is not easy to find a trade-off between sum-rate maximization, user fairness and energy efficiency. In heavily populated networks with a large number of users and dynamic traffic patterns, the power allocation optimization becomes even more complicated (Bikkasani & Yerabolu, 2024). Furthermore, as with SIC, error propagation is a constant issue: any error in decoding the signal of the first user propagates and negatively impacts the decoding of other users, with a serious impact on the performance of the system (Thaherbasha & Nageena Parveen, 2025).
Recognizing these difficulties, the improvement of better power allocation algorithms has become a necessity to increase the performance of SIC and guarantee stable operation of NOMA in the next wireless networks. The smart power allocation schemes can directly govern the efficiency of SIC, the system sum rate that can be achieved, and the fairness and energy consumption of the users. Recent studies have examined optimization techniques, metaheuristic methods (Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)) and hybrid techniques that integrate the advantages of the various methods. These approaches are designed to maximize throughput, as well as to make SIC more robust to interference and error propagation in real-world deployment scenarios (Papazoglou & Biskas, 2023).
This narrative review thus attempts to present at length a scientific discussion that connects power allocation strategies and SIC performance improvement to NOMA systems for 5G and beyond. The review discusses the history of power allocation methods, the use of these methods to enhance SIC, and the complexity-efficiency-robustness trade-off. By summarizing the results of recent research, the review lays stress on the fact that the adaptive and intelligent power allocation is the key to the solution of the interference management problem and the reliable performance in the future wireless systems. Finally, the review aims to offer a more profound understanding of the role of sophisticated power allocation schemes in defining the success of SIC in supporting next-generation interference.
2. Fundamentals of NOMA and SIC
The concept of NOMA is a radical change from the traditional orthogonal multiple access systems employed in earlier decades of wireless communication systems (Banafaa et al., 2024). Contrary to orthogonal schemes like OFDMA (Orthogonal Frequency-Division Multiple Access) and TDMA (Time-Division Multiple Access), where each user is assigned a different frequency or time resource, NOMA uses the power domain to serve multiple users on the same frequency-time resource (Kebede et al., 2022). The idea is based on the superposition coding scheme at the transmitter whereby the signals destined to different users are linearly combined and transmitted with different power levels. The power is allocated in a way proportional to the inverse of the channel gains of the users: the users with weaker channel conditions get assigned higher transmission powers, and those with stronger channel gains get lower power values. Depending on the receiver, proper decoding techniques are applied to de-multiplex the overlaid signals (Altun et al., 2022).
NOMA is characterized by the user ordering a concept that plays a pivotal role in effective decoding. Normally, the channel conditions of users are ranked, and the farthest or weakest user is ranked first and the nearest or strongest user last (Mohsan et al., 2023). This sequence determines the power distribution as well as the sequence of decoding. As an example, a two-user NOMA system will assign more power to the weaker user and directly decode its signal at the expense of the stronger user signal (Abbasi & Yanikomeroglu, 2022). The more powerful user implements SIC by decoding the low-power signal of the weaker user, deducting it from the composite signal, and then decoding its own, lower-power signal, as illustrated in Figure 1. This power-domain multiplexing and user ordering combination gives NOMA the ability to provide spectral efficiency, user connectivity, and system throughputs higher than orthogonal schemes (Khan & Singh, 2024).
Figure 1. NOMA and SIC conceptual diagram.
This figure illustrates how, in NOMA, the stronger user performs SIC by first decoding the weaker user’s low-power signal, subtracting it from the composite signal, and then decoding its own signal.
SIC is part of the essence of NOMA operation since it is an advanced multi user detection technique, which decomposes overlapping signals. The mechanism of SIC works in a step-by-step procedure of decoding (Siddiky et al., 2025). A receiver initially picks up the signal of the user with the greatest power, decodes it, and then subtracts its reconstruction of the signal out of the received composite signal. This process minimizes the interference to successive signals, which enhances the ability to decode the less powerful signals more accurately by the receiver (Zuberi et al., 2023). The procedure is repeated until all the user signals are recovered successfully. Theoretically, SIC allows optimal signal separation and maximizes the advantages of power-domain multiplexing, and thus is a key to practical NOMA implementations (Ahmed et al., 2024).
Nevertheless, SIC has its limitations, and these limitations present a big challenge to the practical application in 5G and beyond networks. The main issue is the error propagation; that is, when the receiver makes a wrong decision during the decoding process of a signal with high power, the error will be propagated to the subsequent cancellation process, thus creating a chain of errors that will degrade the performance of the system (Pirayesh & Zeng, 2022). This issue is aggravated in cases where the CSI is imperfect because wrong channel estimations cause wrong reconstruction and subtraction of signals. Moreover, SIC adds computational complexity and latency to the decoding process because it is sequential, with the signal of each user having to be decoded before another signal can be worked on (Gupta & Prakriya, 2022). Unless we embrace enhanced hardware or algorithmic approaches, this sequentiality renders SIC unsuitable for ultra-low-latency applications. Moreover, SIC in dense user settings with lots of overlapping signals is very complex, and it is a scalability issue in the future network (Ahmed et al., 2024).
The design of power allocation strategies, in this regard, becomes extremely important, since it directly influences the accuracy and efficiency of SIC. Allocation of the transmission power across users not only defines the sequence and efficacy of the cancellation procedure but also controls the system performance in terms of throughput, energy efficiency, and fairness (Bikkasani & Yerabolu, 2024). In the case of improper power allocation, the signals received can be too close to each other in terms of power levels, and it will be hard to decode them with SIC and very error-prone. On the other hand, in cases of too-high power differences, the stronger users can be affected by low throughput even though their channel conditions are favourable, which is inefficient and unfair (Lee, 2023). Therefore, a tradeoff between the system sum rate and the fair resource allocation among users needs to be achieved.
Besides, power allocation strategies should consider the various needs of 5G and 6G applications. In such a case, when the maximization of throughput is of primary concern, we can assign higher power to the users with better channel conditions, whereas fairness-based schemes should not leave weaker users with lower data rates (Alsaedi et al., 2023). In URLLC, power allocation should focus on reducing the complexity of SIC and error propagation to achieve high levels of reliability (Jain, 2025). The significance of power allocation thus does not only have to do with the physical layer but also the overall system design, which influences quality of service, spectral efficiency, and user experience (Arfaoui et al., 2020).
To conclude, NOMA and SIC create a strong basis for the next-generation wireless communication that allows multiple users to share a single spectrum resource with increased efficiency (Mohsan et al., 2023). Although NOMA can maximize the use of the spectral resources by power-domain multiplexing and ordering users, the effectiveness of SIC as its enabling technology is closely related to the efficiency of power allocation (Abuajwa et al., 2025). Knowledge of these basics is critical to appreciating the problems and solutions in the development of better algorithms to manage interference and to enhance SIC in 5G and beyond wireless systems (Yang et al., 2024).
3. Evolution of power allocation techniques in NOMA
The core of NOMA is power allocation that directly affects the success of SIC and, therefore, the overall performance of the system (Ghous et al., 2022). A large number of power allocation strategies have been proposed over the last decade in order to find a compromise between the maximization of the sum-rate, fairness, and computational complexity (Abuajwa et al., 2022; Abdolrasol et al., 2021). These approaches have gone through a transformation, starting with the basic and fixed to complex and adaptive algorithms that are using optimization theory, heuristics, and artificial intelligence, as illustrated in Figure 2. This section follows the evolution of power allocation strategies, highlighting the advantages and disadvantages of each method.
Figure 2. Evolution of power techniques in NOMA.
This figure illustrates the transformation of approaches from basic fixed methods to advanced adaptive algorithms employing optimization theory, heuristics, and artificial intelligence.
3.1 Fixed Power Allocation (FPA): Simplicity versus inefficiency
The simplest and oldest power allocation scheme in NOMA is Fixed Power Allocation (FPA), according to which the power coefficients are provided to the users in advance and do not depend on their current CSI (Ahmed et al., 2021). Normally more power is assigned to users with weaker channels and less to users with stronger channels. Although this method is enticing because of its easiness and low cost of implementation, it is not efficient. FPA lacks the responsiveness to real-time changes in channel conditions, and as such, it can lead to sub-optimal throughput, poor fairness, and error propagation in SIC (Budhiraja et al., 2021). As an example, in the case of channel conditions that vary widely, fixed allocation cannot guarantee reliable decoding of weaker users or efficient use of resources by the stronger users (Gao et al., 2022). Therefore, using FPA as a baseline cannot support the dynamic adaptability of next-generation networks. Table 1 represents the general characteristics of this method and technique.
Table 1. Comparative summary of power allocation techniques in NOMA.
| Technique | Key features | Advantages | Limitations | Complexity | References |
|---|---|---|---|---|---|
| Fixed Power Allocation (FPA) | Predefined power coefficients; simple allocation | Low implementation cost; easy to deploy | Suboptimal throughput; unfair to weak users; poor SIC performance | Low | Ahmed et al. (2021); Budhiraja et al. (2021); Gao et al. (2022) |
| Channel-Aware Power Allocation (CAPA) | Adapts power based on instantaneous CSI | Improved throughput and SIC reliability | Requires accurate CSI; increased signaling overhead | Medium | Clerckx et al. (2024); Liu et al. (2025); Zhang et al. (2024) |
| Convex Optimization | Formulates power allocation as constrained optimization problem | Mathematically rigorous; near-optimal solutions | High computational complexity; less scalable | High | Du et al. (2024) |
| Game-Theoretic Approaches | Models users/base stations as rational players | Distributed allocation; fairness optimization | Convergence issues; may not guarantee global optimum | High | Liu et al. (2021); Banafaa et al. (2024) |
| Genetic Algorithm (GA) | Evolutionary algorithm exploring solution space | Avoids local optima; adaptable | Slower convergence; parameter tuning required | Medium | Amirghafouri et al. (2025) |
| Particle Swarm Optimization (PSO) | Swarm intelligence; particles adjust based on personal and global best | Fast convergence; simple implementation | Premature convergence in high-dimensional problems | Medium | Priyadarshi & Kumar (2025); Papazoglou & Biskas (2023) |
| Hybrid GA-PSO | Combines GA exploration with PSO convergence | Balanced exploration–exploitation; improves sum rate and SIC | Moderate complexity; requires hybrid tuning | Medium–High | Sun et al. (2021); Forghani et al. (2024) |
| Deep Reinforcement Learning (Deep RL) | Learns allocation policies from environment | Adaptive, real-time decisions; robust to dynamics | Needs large training data; generalization challenges | High | Mahmood et al. (2022); Singh et al. (2025); Zhang et al. (2022a); Ogenyi et al. (2025) |
3.2 Channel-aware Power Allocation (CAPA)
To address these issues, CAPA strategies have been proposed in the literature, which vary the distribution of power according to the current CSI of the users. By leveraging the real-time channel gain information, CAPA will guarantee that the weaker users are allocated enough power to guarantee a reliable decoding process, whereas the stronger users are forced to operate at reduced but still effective power levels (Clerckx et al., 2024). This dynamic allocation is better in throughput, fairness, and SIC accuracy than static schemes. Nonetheless, CAPA itself has its own shortcomings, especially the requirement to provide regular and accurate CSI feedback, which raises signalling overhead and complexity (Liu et al., 2025). Furthermore, CSI used in practice can be imperfect, i.e., may have errors in estimation, feedback delay or hardware limitation, which can hurt the effectiveness of CAPA. However, it is a substantial step toward increased flexibility of NOMA systems (Zhang et al., 2024).
3.3 Convex optimization and game theory optimisation-based approaches
In addition to channel-aware techniques, optimization techniques have been widely investigated to find power allocation that maximizes the system objective, including sum rate, energy efficiency, or fairness. Power allocation can be formulated as a constrained optimization problem using convex optimization techniques, and such problems can be solved efficiently using Lagrangian multipliers or dual decomposition techniques. These approaches offer mathematically sound solutions and may guarantee global or near-global optimality in the convex case (Du et al., 2024).
Moreover, game theory has also been found as an effective method of distributed power assignment in the case of multi-cell or multi-user NOMA. By modeling users or base stations as rational players competing for resources, game-theoretic techniques can optimize equilibrium strategies to trade off fairness and performance (Liu et al., 2021). Game theory and convex optimization, however, have limitations in their scalability and in their applicability in real time. These methods have significantly higher computational complexity as the number of users and constraints increases, which makes them less suitable in dense 5G/6G networks with highly dynamic environments (Banafaa et al., 2024).
3.4 Heuristic and metaheuristic algorithms
Heuristic and metaheuristic algorithms are able to identify near-optimal solutions with much less computational cost and have been explored to overcome scalability and complexity issues (Houssein et al., 2024).
The Genetic Algorithm (GA) is a computational method based on the principles of natural selection and evolution. Within the framework of NOMA, it represents potential power allocation solutions as chromosomes and, using selection, crossover, and mutation, it optimizes them iteratively. A is proficient at searching a large solution space and avoiding the local optimum. Its convergence rate can, however, be rather slow, and parameter adjustment is usually necessary to achieve effective performance (Amirghafouri et al., 2025).
Particle Swarm Optimization (PSO): PSO is another metaheuristic that is notably popular and is based on the collective behaviour of flocking birds or schools of fish (Priyadarshi & Kumar, 2025). In PSO, the possible solutions in the form of particles are moving in the solution space with the influence of individual experiences and the shared knowledge of the swarm. PSO converges more readily than GA and is less difficult to code. It is, however, susceptible to premature convergence, especially in high-dimensional optimization problems (Papazoglou & Biskas, 2023).
Hybrid and Adaptive Methods: To harness the advantages of both GA and PSO, hybrid algorithms, like the GA-PSO, have been presented. These bring together the global exploration ability of GA with the rapid convergence of PSO, striking a better balance between exploration and exploitation. These mixed schemes have been promising in increasing the sum rate, fairness, and SIC robustness in NOMA (Sun et al., 2021). Additionally, metaheuristics that allow the parameters to be altered in an adaptive manner during the run are being developed to enhance efficiency in dynamic network settings (Forghani et al., 2024).
3.5 AI/ML-driven methods: Reinforcement learning and deep learning
The latest trend in the development of power allocation methods is powered by artificial intelligence (AI) and machine learning (ML). In contrast to the traditional optimization or heuristic techniques, AI-driven approaches can train in the environment, which makes them a good fit for complex and dynamic network settings (Mahmood et al., 2022).
Reinforcement Learning (RL): Using trial-and-error interaction with the environment, RL enables an agent, such as the base station, to explore and learn optimal power allocation policies. With the feedback in terms of rewards (e.g., throughput maximization or the improvement of fairness), RL algorithms can learn to optimize power allocation in real time (Singh et al., 2025).
Deep Learning (DL): DL models, more specifically deep neural networks, are able to represent the mapping between CSI and optimal power allocation strategies. Trained, they are able to provide instant, near-real-time allocation decisions, which is why they are applicable to 5G and beyond. Nonetheless, DL needs big datasets to train and can fall prey to generalization problems when used in scenarios that are not the same as the training ones (Zhang et al., 2022a).
Collectively, these AI/ML-enabled methods represent the pinnacle of NOMA power allocation research, capable of scaling, adapting, and demonstrating resilience in ultra-dense and highly dynamic 6G networks (Ogenyi et al., 2025).
Overall, the development of power distribution in NOMA indicates a smooth transition between a fixed scheme and intelligent and adaptive schemes. Whereas FPA and CAPA formed the foundation, optimization-based procedures added mathematical rigour, metaheuristics added scalability, and AI/ML methods added unprecedented flexibility. This evolutionary path highlights the importance of power allocation in terms of both maximizing spectral efficiency and guaranteeing a reliable SIC and user fairness in the next-generation wireless systems (Zhang et al., 2022b; Ogenyi et al., 2025; Thirupathi et al., 2025).
4. Interference and SIC enhancement strategies
Interference control is one of the most important issues of NOMA system implementation, especially in terms of 5G and beyond networks. As NOMA multiplexes users in the power domain, its performance is extremely vulnerable to interference, both intra-cell and inter-tier (Zhang et al., 2022a). SIC’s performance depends on its power allocation accuracy and its ability to process and reduce the effects of various interference sources. It is in this vein that strategies that contribute to the improvement of SIC performance through interference characterization, management and adaptation have become the focus of current research (Salem et al., 2024).
4.1 Characterization of interference in NOMA: Intra, inter, cross-tier
Interference in NOMA networks may be grouped into intra-cell interferences, inter-cell interferences, and cross-tier interferences. Intra-cell interference is due to the presence of multiple users in the same cell that are multiplexed on the same frequency resource block (Zhang et al., 2022b). Even though SIC reduces this interference, its performance is highly dependent on the power allocation and decoding accuracy. Inter-cell interference occurs in multi-cell networks when base stations assign overlapping resources to their users, leading to cross-cell contamination that can degrade SIC decoding (Siddiqui et al., 2021). Lastly, cross-tier interference is particularly important in heterogeneous networks, where macrocells, small cells and device-to-device links may all exist. In this case, powerful signals of the higher-tier nodes can overpower weaker signals of the lower-tier nodes, which makes SIC challenging (Yang et al., 2024). A detailed expression of these sources of interference is important in engineering an effective enhancement strategy, as shown in Figure 3.
Figure 3. Interference and SIC enhancement strategies.
Figure 3 shows the detailed sources of interference in NOMA networks, including intra-cell, inter-cell, and cross-tier interference, which are critical considerations in engineering effective enhancement strategies.
4.2 SIC performance bottlenecks: Error propagation and channel estimation error
Although SIC could theoretically perform perfect signal separation, it has several performance bottlenecks in practice. Among the most critical ones, there is error propagation (Pandala et al., 2023). Because of the sequential decoding, an incorrect decision in decoding the signal of one user can propagate through remaining stages, causing poor system performance. This issue is particularly severe when decoding the signals of weaker users who have been allocated a large amount of power, as any error can corrupt the entire cancellation path (Siddiqui et al., 2021). The other major bottleneck is channel estimation error. In SIC, accurate CSI is needed to reconstruct signals and subtract them. Estimation noise, feedback delay, or hardware impairments result in imperfect CSI that causes residual interference to degrade decoding accuracy (Salem et al., 2024). Furthermore, in a fast-fading channel, channel changes can be too fast to track via SIC, thereby leading to substantial performance degradation. All these bottlenecks emphasize the need to have advanced strategies that can boost SIC resilience as outlined in Table 2.
Table 2. SIC performance bottlenecks and mitigation strategies.
| Bottleneck | Description | Mitigation strategies | Impact on SIC performance | References |
|---|---|---|---|---|
| Error Propagation | Incorrect decoding of a user signal affects subsequent SIC stages | Fairness-aware power allocation; hybrid GA-PSO; joint decoding order optimization | Reduces cascading errors; improves reliability | Pandala et al. (2023); Siddiqui et al. (2021); Qian et al. (2023) |
| Imperfect CSI | Channel estimation errors or feedback delays lead to residual interference | Robust optimization; stochastic or probabilistic power allocation; AI-driven adaptive methods | Enhances SIC accuracy under uncertain channel conditions | Salem et al. (2024); Lee & Lee (2021); Guo et al. (2022); Jiao et al. (2024) |
| Latency in SIC | Sequential decoding may introduce processing delays | Parallel processing techniques; low-complexity algorithms | Reduces decoding delay; maintains low-latency performance | Siddiqui et al. (2021); Liu et al. (2024) |
| Interference from Multi-cell or Cross-tier Sources | Overlapping transmissions in multi-cell or heterogeneous networks | Cooperative NOMA; interference-aware power allocation | Improves SIC reliability; mitigates inter-cell interference | Yang et al. (2024); Sharma et al. (2024) |
4.3 Improved power allocation for robust SIC
The most straightforward approach to the improved performance of SIC consists in the development of more efficient power allocation schemes. The main idea behind NOMA is to allocate more power to weak users and less power to strong users; however, this is a balance that needs to be well tuned to assure that SIC is reliable (Mohsan et al., 2023).
Trade-off between weak and strong users: A trade-off between weak and strong users is important in the case of SIC decoding since a large power difference between the weak and strong users will result in error-prone SIC decoding due to a lack of signal differentiation (Liu et al., 2024). On the other hand, when the gap is too large, high-powered users will experience low throughput, which creates inefficiency. Therefore, the optimal power allocation should balance between maximization of the system sum rate and SIC reliability (Salem et al., 2024).
Fairness-aware allocations: Besides maximizing throughput, fairness is a critical factor in the next-generation networks (Siddiqui et al., 2021). Fairness-based power assignment ensures no resource deprivation for users with chronically poor channels. Not only does this enhance user experience, but it also stabilizes SIC performance, since similar power differentiation minimizes the chance of decoding failures caused by interference (Maity et al., 2023).
4.4 Hybrid methods: Power allocation and SIC decoding order jointly optimized
One of the recent promising developments in NOMA research is a hybrid scheme that simultaneously optimises the power allocation and the SIC decoding order (Qian et al., 2023). Traditionally, the order of decoding is always based only on the channel gains of the users, where stronger users decode the weaker users first. Nevertheless, this static ranking might not always perform in the best fashion, especially in the case of heterogeneous environments with varying user demands (Yang et al., 2023). Joint optimization enables the decoding order to dynamically adjust along with power allocation to minimize error propagation to improve the overall system robustness. Convex programming, heuristic algorithms and machine learning-based frameworks have been used to tackle this joint optimization problem and they have resulted in considerable gains in the sum rate and SIC accuracy (Jiao et al., 2024).
4.5 Imperfect CSI and robustness strategies
In practical networks, the perfect CSI assumption is hardly ever true, and resilience mechanisms against imperfect CSI are key to successful practical deployment of NOMA. The robust optimization frameworks handle the uncertainties in CSI by developing worst-case and probabilistic models that protect the performance against estimation errors (Lee & Lee, 2021). Stochastic optimization methods, such as those, can allocate power by considering CSI uncertainty distributions and thus reduce the negative effect of imperfect information (Jiao et al., 2024). Machine learning algorithms can learn allocation policies that are less sensitive to CSI errors by using past experience and real-time feedback (Guo et al., 2022). These robustness-targeted measures guarantee that SIC can work reliably in realistic channel conditions, and thus NOMA can be used in large-scale and dynamic 5G/6G networks.
To conclude, interference coordination and SIC in NOMA systems must be complex. By modelling the sources of interference, identifying bottlenecks like error propagation and less than perfect CSI, and developing better power allocation schemes, researchers are leading the way to more robust implementations (Salim et al., 2025). The integration of fairness-aware designs, simultaneous optimization of the decoding order and power, and robustness strategies in the presence of imperfect CSI additionally reinforces SIC performance. In combination, these strategies make NOMA an attractive option to mitigate interference in next-generation wireless networks, with the guarantee of reliability and scalability, as well as efficiency under the increasing connectivity challenges (Sharma et al., 2024).
5. Comparative insights from literature
The investigation of power allocation and SIC in NOMA systems has been developed significantly, and a rich literature on the trade-offs among the system throughput, fairness, and computational complexity exists. A critical review of these studies gives an understanding of the performance benefits and drawbacks of the various allocation strategies, the comparative advantages of conventional optimization versus heuristic and AI-based optimization, and the implications of such a paradigm shift on next-generation wireless networks.
5.1 The performance trade-offs include the sum rate, user fairness, and algorithm complexity
Among the themes that are repeatedly brought up in the literature is the trade-off that exists between sum-rate maximization, user fairness, and complexity of the algorithm (Umoga et al., 2024). Strategies that would maximize sum rate may over serve strong users at the expense of weak ones. On the other hand, the fairness-based power allocation schemes allocate resources in a more equitable way, and this may decrease the overall throughput of the system (Li & Li, 2025). On the one hand, highly optimized solutions, like those that arise as convex optimization or game-theoretic solutions, can perform near-optimally but can be associated with significant computation overhead, thus being less suitable in real-time applications in dense or dynamic networks (Zhang et al., 2025). Researchers have emphasized the necessity to balance these conflicting goals, since a solution that favours one metric too much can ruin overall network performance or user experience (Omar et al., 2024). An example is that heuristic or hybrid approaches can provide a trade-off that is both decent in throughput and fair and has reasonable complexity (Dai et al., 2025).
5.2 The comparison of the traditional optimization with AI/heuristic approaches
Convex optimization and game-theoretic optimization are conventional methods that give a mathematical foundation to the power allocation procedure (Memeti & Pllana, 2021). The techniques can provide globally or near-globally optimal solutions under well-defined constraints and assumptions, especially in the case of perfect channel state information (Singhal et al., 2024). Their scalability is, however, limited, and the computational load grows very fast with the number of users or cells. Heuristic and AI-driven methods, in turn, are more flexible and adaptive. Metaheuristic optimization techniques like GA and PSO are capable of exploring the large solution spaces efficiently and adapting to dynamic network conditions, but at the expense of convergence time or even suboptimality (Sun et al., 2021; Priyadarshi & Kumar, 2025). RL and DL are AI-driven techniques that can be transformative since they can learn optimal or near-optimal allocation policies using historical or real-time data (Singh et al., 2025). Such approaches are particularly powerful in dynamic, dense, and imperfectly characterized settings where conventional optimization can fail, but they must be trained and tested thoroughly to generalize well to a wide range of network settings (Zhang et al., 2022b).
5.3 Case studies: GA, PSO, GA-PSO hybrid, and deep RL methods in the enhancement of SIC
The literature has a number of case studies on the practical performance of various power allocation schemes in optimizing SIC. The SIC robustness has been demonstrated to be enhanced via GA-based methodologies, which explore a wide variety of allocation combinations to minimize error propagation among multiple users. The PSO-based techniques exhibit rapid convergence and consistent performance in moderately complex situations but may have the problem of early convergence in highly dynamic situations (Haris et al., 2024). Hybrid GA-PSO algorithms combine the strengths of both genetic algorithms and particle swarm optimisation, achieving a balance between exploration and exploitation that results in improved sum rates, fairness, and SIC accuracy (Saad et al., 2024). Additionally, deep reinforcement learning techniques have also developed as a very promising approach to real-time adaptation. Deep RL agents can learn the channel conditions to power allocation policies mapping and dynamically adapt the allocations to maximize throughput and fairness with consideration of the interference and channel uncertainties (An et al., 2023). Comparative results are consistent that hybrid and AI-based solutions are superior to fixed or purely optimization-based schemes in adaptability, robustness, and SIC performance, especially in multi-cell or heterogeneous network deployments (Fasihi & Mark, 2025). These case studies and key insights are summarized in Table 3.
Table 3. Case studies of Heuristic and AI-driven methods in enhancing SIC.
| Method | Application scenario | Key findings | Advantages & limitations | References |
|---|---|---|---|---|
| GA | Single-cell NOMA with 3-5 users | Improved SIC accuracy; balanced throughput | Advantage: robust exploration; Limitation: slow convergence | Sun et al. (2021); Haris et al. (2024) |
| PSO | Multi-cell NOMA scenarios | Faster convergence than GA; moderate sum-rate improvement | Advantage: simple implementation; Limitation: may converge prematurely | Priyadarshi & Kumar (2025); Haris et al. (2024) |
| GA-PSO Hybrid | Multi-user heterogeneous networks | Optimized sum rate, fairness, and SIC reliability | Advantage: combines exploration and convergence; Limitation: moderate complexity | Saad et al. (2024); Sun et al. (2021) |
| Deep RL | Dynamic NOMA networks with imperfect CSI | Adaptive power allocation; robust SIC under variable channels | Advantage: real-time adaptability; Limitation: requires training, sensitive to environment shifts | An et al. (2023); Singh et al. (2025); Zhang et al. (2022b) |
5.4 Lessons learned and key design principles
From the comparative analysis, several key lessons emerge. First, dynamic adaptability is essential; static or fixed schemes cannot cope with the variability of channel conditions, user density, or network topology in 5G and beyond systems (Shoaib et al., 2024). Second, joint consideration of power allocation and SIC decoding order significantly enhances performance by reducing error propagation and maximizing spectral efficiency (Vijay, 2025). Third, hybrid and AI-driven methods provide the best compromise between throughput, fairness, and complexity, demonstrating scalability in dense networks and resilience under imperfect CSI (Shahid et al., 2025). Fourth, fairness must be explicitly incorporated into algorithm design to ensure equitable service for all users, particularly in heterogeneous or mMTC scenarios (Hendaoui et al., 2025). Finally, robustness against interference and channel estimation errors is crucial for reliable SIC operation, and strategies that account for uncertainties—whether through stochastic optimization or learning-based approaches—are more likely to succeed in real-world deployments (Mohsan et al., 2023). These design principles collectively guide the development of next-generation power allocation schemes that can sustain high-performance NOMA operation in dynamic, multi-user wireless environments (Salim et al., 2025).
In summary, the literature indicates that no single power allocation strategy universally outperforms others across all metrics (Song et al., 2025). Instead, effective design requires a careful balance of sum rate, fairness, and computational efficiency, coupled with adaptive, robust algorithms capable of enhancing SIC performance under realistic conditions (Dipinkrishnan & Kumaravelu, 2024). By synthesizing these insights, researchers can develop more resilient and intelligent NOMA systems for 5G and beyond (Song et al., 2025; Dipinkrishnan & Kumaravelu, 2024).
6. Emerging trends and future directions
With the advent and subsequent maturity of 5G networks and the visioning of 6G networks, research on NOMA and SIC is shifting towards more intelligent, energy-efficient and robust designs (Salim et al., 2025). The trends suggest that the combination of AI and ML with power allocation and SIC will be central to the high-performance targets of next-generation wireless systems (Singh et al., 2024). Intelligent algorithms, specifically reinforcement learning and deep learning algorithms, allow adapting to the dynamic conditions of a complex network, such as a variable channel state, interference patterns, and user densities. These smart techniques are capable of learning optimal or near-optimal power allocation policies and SIC decoding orders in real time and can therefore enhance spectral efficiency, fairness, and error propagation mitigation in real time, which is a significant transition towards fully adaptive wireless networks (Dipinkrishnan & Kumaravelu, 2024).
The other major trend is the focus on energy-efficient and green communications. With networks becoming denser and the number of connected devices multiplying exponentially, energy consumption is an issue of prime concern. Efficient power allocation strategies in NOMA can greatly minimize transmission power demands with no impact on the throughput and SIC reliability. Research is also becoming more concerned with the development of algorithms that can optimize both the spectral efficiency and the energy consumption, thus enabling sustainable network operation (Mohsan et al., 2023). Energy-conscious reinforcement learning and hybrid metaheuristic optimization techniques are being considered to meet low-power operation without compromising performance, which is why green communication is important to future NOMA systems.
This is facilitated by the need to handle the massive connectivity, especially the IoT and mMTC, and thus innovation in SIC and power allocation schemes (Cheikh et al., 2025). In these scenarios, the networks must support thousands or even millions of devices, each with unique data requirements and channel environments. Conventional SIC techniques can be computationally infeasible, and predetermined power allocation schemes can be generally inadequate to ensure reliable decoding. The emerging solutions include hierarchical NOMA structures, dynamic user clustering, and adaptive power allocation algorithms that are focused on reliability and fairness to an enormous number of devices. The dense networks are especially well suited to the AI-driven methods, which can learn scalable policies that guarantee robust SIC performance and interference management (El-Hajj, 2025).
Another possible direction of improvement of the system performance is cross-layer optimization and cooperative NOMA. Cross-layer designs offer the advantage of more efficient use of resources and reduced interference by considering power allocation at the physical layer, scheduling at the medium access control layer, and routing at the network layer in a joint manner. Cooperative NOMA, in which relay nodes or users aid in the decoding and forwarding process, enhances SIC reliability and coverage, especially to cell edge or shadowed users. These approaches present the possibilities to optimize throughput, fairness, and reliability in heterogeneous networks, which is in line with the 6G performance requirements (Clerckx et al., 2024).
Lastly, the 6G URLLC direction imposes challenging constraints on both the power allocation and SIC performance. Applications like self-driving cars, remote surgery, and factory automation require very high reliability and low latency. In this respect, strong SIC schemes play a key role in reducing error propagation as well as in guaranteeing consistent decoding across channels. Sophisticated adaptive algorithms, mixed optimization methods and algorithms and AI-based predictive models will be key to addressing these needs. The area of research is also evolving to integrate strong SIC with low-latency scheduling, reliability-based resource allocation, and smart interference management, all of which represent the future of URLLC-enabled NOMA systems.
The developing dynamics of NOMA studies revolve around the concepts of intelligence, adaptability, energy efficiency, and scalability. The combination of AI/ML, energy-efficient designs, support of massive connectivity, cross-layer and cooperative techniques, and robust SIC schemes are all characteristic of the future of power allocation in the 5G and 6G networks (Cheikh et al., 2025). By attending to these trends, researchers can come up with very robust, highly efficient, and scalable NOMA systems that can accommodate the varied needs of the next-generation wireless communications, thus enabling the potential of the digital ecosystems of the future. Table 4 outlines the new trends and future perspectives in the field of NOMA and SIC, along with their advantages and obstacles.
Table 4. Emerging trends and future directions in NOMA and SIC.
| Trend | Description | Key benefits | Research challenges | References |
|---|---|---|---|---|
| AI/ML Integration | Reinforcement learning, deep learning, and predictive models for dynamic allocation | Adaptive, intelligent power allocation; improved SIC performance | Large training data requirements; generalization to unseen scenarios | Singh et al. (2024); Dipinkrishnan & Kumaravelu (2024) |
| Energy-Efficient & Green Communication | Low-power power allocation strategies and energy-aware optimization | Reduced network energy consumption; sustainable operation | Balancing energy efficiency with throughput and fairness | Mohsan et al. (2023) |
| Massive Connectivity (IoT, mMTC) | Supporting thousands/millions of devices in dense networks | Scalable SIC; reliable decoding for weak devices | Computational complexity; interference management in ultra-dense networks | Cheikh et al. (2025); El-Hajj (2025) |
| Cross-layer Optimization & Cooperative NOMA | Jointly optimizing power allocation, SIC, scheduling, and cooperative relaying | Enhanced spectral efficiency, fairness, and robustness | Complex system design; coordination overhead in multi-tier networks | Clerckx et al. (2024) |
| URLLC & Robust SIC for 6G | Ultra-reliable low-latency communication with SIC optimization | Ensures reliability and low latency for critical applications | Stringent latency and reliability requirements; handling dynamic channels | Dipinkrishnan & Kumaravelu (2024); Salim et al. (2025) |
7. Conclusion
This review has been a complete analysis of power allocation and SIC improvement methods in NOMA systems and how they have been used in 5G wireless networks and beyond. The analysis of NOMA and SIC demonstrates that the joint power-domain multiplexing of several users and subsequent simultaneous signal decoding holds great promise in terms of spectral efficiency, connectivity and overall system throughput. Nevertheless, the actualization of such benefits is dependent on smart power allocation schemes and strong SIC procedures that can deal with interferences, error propagation, and dynamic channel conditions.
The literature reveals that the power allocation methods have changed a lot in the past. Early fixed power allocation schemes are simple to implement; however, they do not adapt to varying channel conditions and user requirements, which can result in suboptimal throughput and high SIC errors. Channel-aware power allocation techniques have been developed to provide adaptivity, which enhances reliability and efficiency, but are limited by requirements on accurate and timely channel state information. Optimisation-based approaches, e.g., convex optimization and game-theoretic frameworks, have the advantage of having rigorous solutions that are able to optimize system objectives, but the long computation time makes them unsuitable in high-density networks. Heuristic and metaheuristic algorithms, which include GA, PSO, and hybrid approaches to GA-PSO, can provide scalable solutions that achieve a balance between throughput, fairness, and SIC performance. Recently, machine learning and artificial intelligence methods, such as deep reinforcement learning, have demonstrated strong potential for the real-time learning of optimal power allocation policies in uncertain and complex network environments. These studies show how adaptive and intelligent power allocation is vital for improving SIC accuracy, interference mitigation, and balanced system performance.
The other important conclusion is that SIC performance is very sensitive to power allocation and network conditions. Propagation of error, inaccuracies in channel state information, and latency during decoding remain significant bottlenecks, particularly in dense and heterogeneous networks. We have demonstrated that the proposed strategies, which include fairness-aware power allocation, robust decoding order optimisation, and resiliency against CSI uncertainty, significantly enhance SIC reliability. Furthermore, hybrid and AI-based solutions not only lead to a higher level of decoding accuracy but also enable the networks to adapt dynamically to fluctuations of user density, channel fading, and interference and provide consistent performance across different network conditions. The interaction between power allocation and SIC becomes one of the guiding design principles, as it becomes clear that no single metric, e.g., throughput, can be used to define the efficacy of NOMA systems, but that a more holistic approach balancing multiple objectives is necessary.
The research pathway indicates some promising future research areas for NOMA on 5G and 6G networks. The combination of AI and machine learning with power allocation and SIC will likely lead to the realization of the fully adaptive and autonomous wireless system, which is capable of supporting massive connectivity, ultra-low latency and high reliability. Green and energy-efficient communication approaches will become increasingly important for achieving sustainable network operations in dense networks. The additional optimization of the system will be performed across the layers and through collaborative NOMA designs to improve interference management in the system. Lastly, the shift to 6G URLLC environments will require powerful SIC schemes that can be effectively used in the context of the high reliability and latency requirements, which will guarantee smooth operation of such applications as autonomous vehicles, industrial automation, and remote healthcare.
In summary, it can be stated that smart power distribution, which is adaptive, intelligent, and robust, is the key to successful SIC in the NOMA systems. Dynamic allocation strategies, fairness-aware designs, and AI-driven learning mechanisms are the three elements that can be combined to make future wireless networks scalable, high-throughput, and reliable, even under complex interference and channel conditions. The lessons learnt through the current body of literature present a blueprint to the creation of the future generation of NOMA-based systems and emphasize the need to be holistic, adaptive, and forward-looking in terms of interference management in 5G and 6G networks. The ongoing development of power distribution and SIC optimization schemes will be at the centre of unleashing the potential of NOMA as a major enabler of high-performance, resilient and intelligent wireless communications.
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Ogenyi FC, Ugwu OPC, Eze VHU et al. Next-Generation Interference Management: A Narrative Review of power Allocation and SIC Enhancement in 5G/6G NOMA [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:888 (https://doi.org/10.12688/f1000research.169843.1)
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