|
| References | Mobility level | Deep learning architecture | Main contribution |
|
| Dong et al. [41] | Mobile edge | DRL | Minimizes normalized energy consumption with less complexity |
| Gante et al. [27] | Outdoor | Temporal CNN | Reduces the error of non-line-of-sight millimeter wave outdoor positions |
| Huang et al. [68] | Outdoor | CNN-LSTM | Improves traffic loads forecasting accuracy over baseline algorithms |
| Huang et al. [28] | User group | CNN | Reduces complexity of optimization and reduce computational time |
| Kim et al. [92] | User group | DDNN | Improves performance of cellular network while reducing computational time and complexity |
| Kim et al. [62] | Individual | Autoencoder | Deep-SCMA scheme performs better than conventional methods in terms of bit error rate and computational time |
| Klautau et al. [15] | Outdoor | DQL | Model channel and mobility |
| Lei et al. [61] | Individual | SSAE | Improves the performance of cache |
| Luo et al. [67] | Outdoor and indoor | CNN-LSTM | Converges fast in predicting channel information |
| Luo et al. [69] | User group | CNN-DQL | Maximizes power transmission |
| Maimó et al. [76] | Outdoor | DBN-LSTM | Detects symptoms on traffic flow |
| Maimó et al. [78] | Outdoor | DBN-LSTM | Integrates MEC in traffic flow for 5G |
| Ning et al. [42] | Vehicular network | DRL | Minimizes offloading cost and maintains user latency constrain simultaneously |
| Ozturk et al. [81] | Outdoor | Stacked LSTM | Potential for holistic handover management for 5G wireless network |
| Pang et al. [12] | Individual | LSTM | The intelligent framework reduces transmission delay |
| Razaak et al. [89] | Outdoor | GAN | Reduces efforts of human intervention |
| Sadeghi et al. [43] | User group | DRQL | Provides fast computational time and reduces complexity and requirement for memory |
| Shahriari et al. [39] | Outdoor | DRL | It was found that the communication load and caches misses are reduced with limited system overhead |
| Sundqvist et al. [79] | Individual | Adaboosted ensemble LSTM | Detects anomalies in random access network very fast |
| He et al. [29] | Outdoor | CNN | Detecting of signal interference with less computational complexity |
| Li et al. [44] | User group | DQN | Effective chaining |
| Xia et al. [45] | User group | DQN | Reduces energy cost and delay experience by users |
| Saetan et al. [93] | Outdoor | DDNN | Predicts power factor |
| Pradhan and Das [48] | Individual | RL | Provides packet drop probability and better resource utilization |
| Zhao et al. [49] | Outdoor | RL | Fast response to network demand |
| Ho et al. [50] | Outdoor | DQN | Solves the problem of base station allocation |
| Yu et al. [46] | Outdoor | DQN | Searches for optimal deployment location with high speed |
| Tang et al. [47] | Outdoor | DQN | Improved network performance based on throughput and packet drop rate |
| Chergui and Verikoukis [99] | Outdoor | DDNN | Estimates slices resources |
| El Boudani et al. [97] | Outdoor | DDNN | Predicts 3D position of mobile station |
| Xie et al. [51] | Outdoor | DQN | Improves efficiency of initial window decision |
| Hussain et al. [30] | Outdoor | CNN | Detects denial of service with high accuracy |
| Butt et al. [95] | Outdoor | DDN | Estimating user equipment positioning |
| Godala et al. [31] | Outdoor | CNN | Estimates channel state information |
| Li and Zhang [52] | Outdoor | DRL | Improves quality of service |
| Yu et al. [53] | Outdoor | DRL | Improves energy efficiency |
| Yu et al. [9] | Outdoor | LSTM | Resource allocation in TV broadband services |
| Liu et al. [82] | Outdoor | LSTM | Predicts hotspot for small virtual cell |
| Mismar et al. [54] | Outdoor | DQN | Signal to interference plus noise ratio improvement |
| Sim et al. [96] | Outdoor | DDNN | Selection of beam |
| Doan and Zhang [34] | Outdoor | CNN | Detects anomaly in 5G network |
| Memon et al. [84] | Outdoor | LSTM | Improves power savings and predicts discontinue reception |
| Klus et al. [32] | Localization | CNN | Predicts user localization and reduces unnecessary handover |
| Saeidian et al. [55] | Outdoor | DQN | Improves data rate at the edge and reduces power transmitted |
| Thantharate et al. [98] | Outdoor | DDNN | Detects security thread |
| Abbas et al. [90] | Outdoor | GAN | Manages slice |
| Abiko et al. [56] | Outdoor | DRL | Allocates radio resource without change in the number of slices |
| Alhazmi et al. [33] | Outdoor | LeNet-5 | Detects 5G signal from cellular system environment |
| Chen et al. [83] | Outdoor | LSTM | Predicts traffic flow while maintaining low complexity and running time |
| Abidi et al. [70] | Outdoor | Metaheuristic-based DBN + ANN | Provides 5G network slicing |
| Liu et al. [116] | Outdoor | YOLOv3 + deep SORT | Improves multiple people detection and monitoring |
| Ahmed et al. [35] | Outdoor | CNN | Improves efficiency in spectrum allocation |
| Ali et al. [100] | Outdoor | DNN | Efficient resource allocation |
| Cheng et al. [36] | Outdoor | CNN | Models mmWave for 5G communications |
| Clement et al. [71] | Outdoor | CNN + DDNN + LSTM | Modulation classification in 5G wireless network |
| Giannopoulos et al. [57] | Outdoor | DQN | Enhances energy efficiency in 5G cognitive |
| Gu et al. [58] | Outdoor | DNR | Improves quality of service and shortens convergence time |
| Guan et al. [37] | Outdoor | CNN | Predicts network traffic flow with limited dataset |
| Gumaei et al. [85] | Outdoor | DRNN | Detects drones from radio frequency signals |
| Rathore et al. [101] | Outdoor | DNN | Enhances security of intelligent 5G-enabled IoT |
| Ullah et al. [86] | Outdoor | DRNN | Predicts clone applications from android and application stores |
| Xu et al. [38] | Outdoor | CNN | Detects adversarial attacks in 5G-based CNN |
| Yu et al [53] | Outdoor | DRN | Reduces time required to execute computational offloading, resource allocation, and caching placement |
| Dinh et al. [59] | Outdoor | DQN | Improves quality of service and enhances throughput |
| Khan et al. [72] | Outdoor | LSTM-SVM | Improves congestion control in 5G/6G network |
| Kaya and Viswanathan [73] | Outdoor | LSTM-AE | LSTM-AE reduces overhead and enhances signal-to-noise ratio |
| Zhang et al. [74] | Outdoor | DRL-LSTM | The DRL-LSTM addresses the routing and link scheduling in mmWawe |
| Sedik et al. [75] | Outdoor | CNN-LSTM | Detects altered biometrics |
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