# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 3 Classification and Evolution of Interference Management Strategies in B5G Networks
### Classification and Analysis of Interference Management Strategies in B5G Networks
#### 3.2.4 Artificial Intelligence-Based Frameworks
In [155], the authors presented a deep RL algorithm for power allocation in dynamic HetNets, reducing outage rates and improving capacity. An AI-based data analytics is utilized in [156] for proactive IMI and adaptive reconfiguration in 6G networks. In [157], the authors suggested a joint Q-learning and network slicing framework for intelligent RA, achieving high SE while maintaining ICO across slices. While AI-based approaches provide adaptive IMI, their practical deployment is hindered by high model complexity, lack of RT inference capabilities on edge devices, and the need for large-scale labeled datasets [158]. Privacy concerns and limited standardization further complicate their integration into existing RAN infrastructure [159]. In addition to these barriers, real-world deployment of AI-based IM frameworks faces several challenges. First, most supervised learning models depend on vast labeled datasets, which are difficult to collect in dynamic and privacy-constrained wireless environments [160]. Even when data are available, it may not reflect live non-stationary behavior across HetNet layers. Moreover, the deployment of DL models at the network edge is constrained by hardware limitations including memory, energy, and latency which hinders their RT performance in mobile or resource-limited nodes [161]. Another critical issue is the lack of explainability in AI decision-making. Most deep neural models operate as black boxes, limiting transparency and operator trust [162]. FL, while a promising privacy-preserving solution, introduces issues such as non-independent and identically distributed (non-IID) data distributions, model convergence problems, and increased communication overhead between devices and aggregation servers [163]. These limitations must be overcome to ensure that AI-based IM systems can move from simulation and lab settings to robust, scalable, and interpretable deployment in live B5G networks. Table 5 summarizes the key AI-based frameworks methods discussed in this section. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Issue** & **Methodologies** & **Advantages** & **Limitations/Future Works** & **Ref.** \\ \hline To manage interference in UAV-assisted HetNets. & Q-learning-based RL algorithm. & Improving SE and adaptability under dynamic conditions. & Accurate channel estimation and stable convergence must be ensured. & [148] \\ \hline To optimize RA in D2D-enabled HetNets. & Multi-agent Q-learning framework. & Enhancing ST and IMI in dynamic networks. & May require high training time for densely deployed environments. & [149] \\ \hline To reduce interference while preserving data privacy. & FeDRL-D2D. & Increasing EE and maintaining privacy in D2D communication. & Model synchronization under heterogeneous data distributions needs refinement. & [150] \\ \hline To minimize inter-beam interference in mm-Wave networks. & Supervised ML algorithm for UA and POC. & Enhancing the sum rate performance by 13–30\% depending on NL. & Limited robustness in rapidly changing channel conditions. & [151] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of related works of AI-based frameworks in literature.