A Survey of Deep Learning Based NOMA: State of the Art, Key Aspects, Open Challenges and Future Trends
<p>Deep neural network general framework.</p> "> Figure 2
<p>An illustration of DL-based NOMA-VLC system.</p> "> Figure 3
<p>Power allocation and channel assignment of DL-based NOMA system.</p> "> Figure 4
<p>Sum rate of DPS-CoMP users (Mbps) versus the number of subchannels per cell [<a href="#B82-sensors-23-02946" class="html-bibr">82</a>].</p> "> Figure 5
<p>Performance of NOMA user pair through DNN-based technique [<a href="#B89-sensors-23-02946" class="html-bibr">89</a>].</p> "> Figure 6
<p>Performance comparison of MIMO-NOMA-DL and MIMO-NOMA-SIC [<a href="#B103-sensors-23-02946" class="html-bibr">103</a>].</p> "> Figure 7
<p>Sum rate versus SNR for the conventional and proposed SIC schemes with varying power allocations [<a href="#B98-sensors-23-02946" class="html-bibr">98</a>,<a href="#B119-sensors-23-02946" class="html-bibr">119</a>,<a href="#B120-sensors-23-02946" class="html-bibr">120</a>].</p> "> Figure 8
<p>Average energy consumption versus the offloading data length [<a href="#B132-sensors-23-02946" class="html-bibr">132</a>].</p> "> Figure 9
<p>NOMA-MEC scenario for uplink and downlink.</p> "> Figure 10
<p>UAV-based IRS-assisted NOMA system.</p> "> Figure 11
<p>A MIMO-NOMA system with multiple clusters [<a href="#B166-sensors-23-02946" class="html-bibr">166</a>].</p> "> Figure 12
<p>NOMA-enabled SWIPT.</p> "> Figure 13
<p>Basic challenges in NOMA.</p> ">
Abstract
:1. Introduction
1.1. Background
1.2. Scope and Contributions
1.3. Organization of the Paper
2. Related Work
Reference | Focus Area | Research Contribution |
---|---|---|
[11] | A comprehensive review of DL-based NOMA | This study surveys DL-based NOMA techniques, benefits, and challenges of DL integration with emerging technologies. It briefly discusses future directions for DL-based NOMA systems. |
[19] | DL-based NOMA for 5G networks | It discusses DL-based NOMA models exploited for user detection, signal detection, channel allotment, power allocation and resource allocation. It also highlights the shortcomings and benefits of DL approaches to solve NOMA challenges. |
[38] | Performance analysis of DL-based NOMA | It outlines DL-based NOMA, its challenges, and potential benefits. |
[37] | Overview of NOMA | It focuses on NOMA’s impact on multi-cell networks. |
[36] | Advantages of NOMA | It comprehensively discusses the benefits of NOMA and the integration of cognitive networks, MEC and MIMO. |
[40] | DL’s impact on wireless communication | It discusses DL-based sum rate maximization for NOMA |
[41] | NOMA in next-generation multiple access | It discusses NOMA as a promising candidate for 6G networks. It also discusses associated research opportunities and future visions. |
[42] | DL for downlinking MIMO-NOMA systems | This article surveys DL in SP blocks of downlink MIMO-NOMA systems. It briefly outlines possible future research directions. |
[43] | DL-enhanced NOMA transceiver designs for massive MTC | It comprehensively discusses state-of-the-art challenges and future directions for DL-enhanced NOMA systems from the perspectives of online adaptability and reconfigurability toward the ever-changing environment in future mMTC. |
[44] | DL-based power allocation in NOMA | It provides a review of the power allocation optimization problem through DL methods. It also outlines various future research directions. |
Our Work | DL-based NOMA | In this study, we comprehensively discuss state-of-the-art, key performance indicators, challenges, and future directions for DL-enhanced NOMA systems. We also discuss the integration of emerging technologies such as MEC, MIMO, OFDM, SWIPT and IRS with DL-based NOMA systems. |
3. Key Aspects for Practical Implementation of DL-Based NOMA
3.1. Resource Allocation
3.2. Power Allocation
Reference | Technique | Model | System Description | Model Limitations | Research Contribution |
---|---|---|---|---|---|
[35] | Reinforcement | Deep neural networks | Physical layer framework, BS with multiple antennas and multiple single-antenna users | Complicated DNN, the data set acquisition and model selection problems | Introduces an efficient, cutting DL-assisted 5G and beyond communication. |
[48] | Reinforcement | Artificial Neural Networks | Attention-based neural network exploiting an encoder-decoder structure, | The key problem is how to allocate limited resources to multiple users | Introduces a method to optimally allocate transmission resources through an attention-assisted neural network for channel assignment. |
[50] | Unsupervised | K-mean | K-means-based online user clustering algorithm | It does not consider more complicated clustering algorithms that are robust to noises and outliers | Satisfy the total transmission power sum-rate maximization issue until the QoS demands of the users. |
[52] | Reinforcement | Q-learning | A single base station in a NOMA system equipped with multiple antennas contending with a smart jammer | It considers a simplified jamming scenario. Theoretical analysis of more practical scenarios is still needed. | In the occupancy of a jamming device, the BS carries out our execution of power distribution. The process is developed as a game with a zero-sum outcome. |
[53] | Reinforcement | Special Neural Network | Single BS using cooperative reinforcement learning algorithm for adaptive power allocation in D2D communication | It considers a single-cell setup, while a multi-cell setup is still required. | Considering the OFDM system without NOMA, maximize overall SU interference while keeping data rate and power. |
[54] | Reinforcement | Q-learning | Dynamic multichannel access problem, where multiple correlated channels consider an unknown joint Markov model | Computationally expensive, DQN is not easy to tune, and more realistic and complicated scenarios such as multi-user systems are not considered. | Channel switching technique for realizing dynamic systems. |
[55] | Supervised | CNN | The power control scheme in D2D communication in multi-channel based on CNN | Compared with DNN, it still lacks fitting ability. | Maximization of throughput by configuring the power. |
[56] | Reinforcement | Two DNNs connected fully | DNN-based multi-channel cognitive radio networks, where the secondary user uses channels without interference from the primary user. | This system does not consider an optimal DNN structure. | Considering an underlay cognitive radio network, it focuses on maximizing spectral efficiency. Keeping the interference of primary users below the threshold, the secondary users provide high spectral efficiency. |
3.3. Channel State Information
3.4. Successive Interference Cancellation
Context | [77] | [78] | [79] | [80] | [81] | [76] |
---|---|---|---|---|---|---|
CR-NOMA | Yes | Yes | Yes | Yes | Yes | Yes |
Downlink | Yes | Yes | Yes | Yes | Yes | Yes |
Uplink | - | - | - | - | - | - |
Imperfect SIC | Yes | Yes | Yes | - | - | Yes |
Imperfect CSI | - | - | - | - | Yes | Yes |
OP analysis | Yes | - | Yes | Yes | Yes | Yes |
Throughput | - | Yes | - | Yes | - | Yes |
3.5. User Fairness
3.6. Impulse Noise
3.6.1. Impact of IN in NOMA
3.6.2. IN Mitigation Techniques
3.7. Transceiver Design
Multiuser Detection Design [98,99,100,101,102,103,104,105,106]
3.8. DL for Channel Estimation
3.9. DL for Beamforming and Selection
3.10. DL for Modulation and Signal Processing
4. Integration of DL-Based NOMA with Emerging Technologies
4.1. Mobile Edge Computing (MEC)
Reference | NOMA Incorporation | Number of Users | Offloading Mode | DL-Based Algorithm | Key Potential |
---|---|---|---|---|---|
[134] | Yes | Single user | Partial | Yes | It gives better performance as compared to FDMA-based MA-MEC |
[137] | Yes | Multi-user | Partial | Yes | It speeds up convergence for better solutions than conventional optimization approaches |
[138] | Yes | Single user | Partial | Yes | The DRL-based algorithm can achieve the near-optimal offloading solution fastly after enough learning |
[139] | Yes | Multi-user | Binary | No | It increases the number of users to offload tasks and reduces the users’ average offloading delay |
[140] | Yes | Multi-user | Binary-Partial | No | It outperforms the binary computation offloading mode, and NOMA outperforms TDMA in the context of computation efficiency |
[141] | No | Multi-user | Binary | Yes | It can achieve near-optimal performance while substantially reducing the computation time by more than an order of magnitude than existing optimization techniques |
4.2. OFDM-Based NOMA
4.3. IRS-Assisted NOMA
Context | [158] | [159] | [160] | [161] | [162] | [163] | [164] | [165] |
---|---|---|---|---|---|---|---|---|
NOMA system | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
OMA comparison | Yes | Yes | Yes | Yes | Yes | - | - | - |
Achievable rate optimization | Yes | - | Yes | - | - | - | Yes | Yes |
Op analysis | - | Yes | - | Yes | Yes | Yes | Yes | - |
Hardware impairment | - | - | - | - | Yes | Yes | - | |
NLoS consideration | - | - | - | - | Yes | Yes | Yes | - |
LoS consideration | Yes | Yes | Yes | Yes | - | - | - | Yes |
4.4. MIMO-Assisted NOMA
4.5. NOMA-Assisted SWIPT
5. Potential Challenges
5.1. Challenges in Resource Allocation
5.2. Challenges in Automatic Signal Detection
5.3. Development of End-to-End Framework
5.4. Implementation of More Than Three-User Pairing in Real-Time
5.5. Uplink NOMA
5.6. Hybrid NOMA
5.7. Mobility
5.8. Multicell NOMA Networks
5.9. Model Selection
5.10. Performance Analysis and Learning Mechanism
5.11. Imperfect Successive Interference Cancellation
5.12. Security Issues
6. Future Research Directions
6.1. Stability and Power Efficiency
6.2. Channel Atatistics
6.3. Novel Algorithms
6.4. Security
6.5. Receiver Complexity in SIC Implementation
6.6. Multi-Cell NOMA System
6.7. Mobility in NOMA
6.8. CSI in NOMA
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Mohsan, S.A.H.; Li, Y.; Shvetsov, A.V.; Varela-Aldás, J.; Mostafa, S.M.; Elfikky, A. A Survey of Deep Learning Based NOMA: State of the Art, Key Aspects, Open Challenges and Future Trends. Sensors 2023, 23, 2946. https://doi.org/10.3390/s23062946
Mohsan SAH, Li Y, Shvetsov AV, Varela-Aldás J, Mostafa SM, Elfikky A. A Survey of Deep Learning Based NOMA: State of the Art, Key Aspects, Open Challenges and Future Trends. Sensors. 2023; 23(6):2946. https://doi.org/10.3390/s23062946
Chicago/Turabian StyleMohsan, Syed Agha Hassnain, Yanlong Li, Alexey V. Shvetsov, José Varela-Aldás, Samih M. Mostafa, and Abdelrahman Elfikky. 2023. "A Survey of Deep Learning Based NOMA: State of the Art, Key Aspects, Open Challenges and Future Trends" Sensors 23, no. 6: 2946. https://doi.org/10.3390/s23062946