## 7 Merging Energy and IoT in 5G, 6G
\begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline \hline
**ML** & **Core Idea** & **Limitations** & **Ref.** \\ \hline DL & 5G-oriented architecture in which to analyse network traffic. & Only TensorFlow was tested, and an in-depth comparison of deep learning frameworks would be essential. & [280] \\ \hline DL & Using ML methods to minimise link failure at handover. & It might enable cellular systems to escape threshold-based handover decisions and follow ML-based methods. & [281; 282; 283] \\ \hline DL & Reduce network load and availability. & Need to include mimicking the created model in a real production environment once the 5G ecosystem is accessible for customers. & [284; 285] \\ \hline DL & Create realistic synthetic data by exploiting the GAN’s capacity to generate and separate data. & Proactive SON algorithms run by GAN generated synthetic CDRs. & [286; 287] \\ \hline DL & Semantic edge cache optimization for multimedia services in 5G, which online improves caching settings depending on user playing behaviour. & Sharing the cache resource across many BSs is essential for heterogeneous networks. & [288; 289] \\ \hline ML & Verification of quality of experience. & Add more diverse and difficult scenarios, like deployment, client movement, and dynamic video and client attributes. & [290; 291] \\ \hline ML & Distributing resources to high mobility users while utilising only their position estimates. & Training the ML unit of the proposed approach will help alleviate inaccurately position information availability-related performance loss. & [292] \\ \hline ML & Consider the case of 5G network slicing, and elaborate on the design, construction, deployment, operation, control, and management of slices. & Extend this inquiry to examine representative ML strategies for autonomic allocation and adjustment of computing and network resources on the basis of service requirements. & [293; 294] \\ \hline ML & 5G challenges and the importance of a changed management paradigm for 5G. & Next steps include actions such as checking the proposed algorithms at scale on commercial networks to validate the idea of adaptive SDN route planning in contact. & [295; 296; 297; 298; 299] \\ \hline ML & MIMO mmWave-assisted downlink for multiple users and Massive MIMO beamforming. & Compared ML-based with CVX-based optimal RRM. & [300; 301; 302] \\ \hline ML & It is possible to learn basic state representations, minimising the complexity of the fundamental network design requirements. & Comparative simulation results demonstrated the suggested decision framework in multi-agent settings. & [303; 304] \\ \hline ML & Used ML to construct a framework for 5G network orchestration. & Mt. approaches use Decision Maker building blocks using neural networks for optimization. & [305; 306] \\ \hline ML & Allocation of the slice is explored and solved using ML approaches. & Solve for scalability. & [307] \\ \hline ML & Found features that alter NFP resiliency. Many of these features are design and engineering features, therefore are difficult to measure. & ML and blockchain can improve the resilience of aerial networks. & [308] \\ \hline ML & Presented a comprehensive self-healing mechanism as an enabler for truly holistic and resilient solution. & TL strategies will be researched in regards to self-healing, which focuses on applying corrective measures. & [309] \\ \hline ML & Flow of thinking led to this assumption, which is then used to define the journey towards CAN and a functional design of a typical CAN system. & Concurrently design and assemble a comprehensive CAN-based architecture. & [301; 302; 303; 304] \\ \hline ML & Research approaches for SON functions for anomaly detection, load balancing, and capacity optimization. & Next-generation SON networks should be built using ML-based user-centric techniques in order to achieve a linked eco-system with enhanced user experience. & [305; 306] \\ \hline ML & Plotted the locations of the drone-BSs and calculated the frequency reuse factor based on an octahedron-shaped structure. & Proposed approach drastically lowers the latency of drone-user UEs. & [307; 308] \\ \hline RL & Feature-learning was applied to mine and understand the complex correlation between multi-ROIs and link quality. & To anticipate the movement, apply strategy learning, and regard the location as the input. & [309] \\ \hline RL & Methodology is shown using a network simulation in ns-3 and the ML implementation in Python. & In autonomous and user-centric networks where the system can provide the most effective path for end-users when going from a given source to a destination, there is the potential to implement OCSF. & [310; 327] \\ \hline RL & Provided insight into the learning challenge of cognitive radio networks and highlights methodologies such as RL used in cognitive radio networks. & [328; 339] \\ \hline RL & Research towards a ML-based remote local (D2D) communication network focused on effective connectivity and minimum latency. & Q-learning offers a perspective on D2D, and in combination with RL-LCDC, offers a practical algorithm for decision making under uncertain network conditions. & [332; 333] \\ \hline RF & Designing a learning-based resource allocation strategy which incorporates simply position information is utilised. & Design parameter modifications, the suggested system is fairly resilient to inaccurate user positioning. & [334] \\ \hline RF & Cartel, a collaborative learning platform in edge clouds. & Add other ML models to the system. & [335] \\ \hline TL & Presented an ML approach that enables semi-blind UL and DL decoupling in mixed sub-6 GHz/mmWave cell-free SG networks. & [336; 337] \\ \hline SVM & Evaluate the predictive performance of four significant predictors that are crucial to achieve mobile traffic management, manage mobility, and conserve energy in future cellular networks. & Study robustness of these predictors versus varying training dataset sizes. Benchmark performances base on mobility traces from network CDRs. & [338; 339; 340] \\ \hline SVM & In order to develop a deep encoder model, a preliminary experimental environment is constructed to collect data on the Multi faces. & Designing an algorithm to discover the ideal deep auto-encoder model and improving the proposed dependability assessment method. & [341] \\ \hline DL & Following positive DL assisted communication results, committed to exploring a unique DL-based strategy for SCMA systems, motivated to increase BER performance. & Proposed schemes must be implemented and verified in a real environment system. An in-depth study of the structure of the learnt codebook may result in a higher decoding accuracy. & [342; 343] \\ \hline DL & Proposed an unsupervised DL strategy for cell outage detection and compared the results to a technique termed the nearest neighbour. & Mobility load balancing and intrusion detection are both DL concerns. & [344; 345; 346] \\ \hline \hline \end{tabular}
\end{table}