# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 3 Classification and Evolution of Interference Management Strategies in B5G Networks
### Taxonomy of IM Strategies in B5G Networks  
To provide a holistic overview of the various IMI strategies discussed in the previous subsections, this subsection introduces a structured taxonomy. The classification is organized by key interference sources, namely D2D communication, HetNets, ICI, and AI-based frameworks.  
Each category is mapped to representative mitigation strategies, categorized by their operational technique types (e.g., centralized, distributed, or learning-based) and the enabling technologies that support their implementation. This taxonomy serves as a visual and conceptual bridge between Sections Sections 3.1-3.4 and the standardized IM schemes outlined in Section 4.  
As shown in Table 6, IMI in B5G networks can be classified by interference source, mitigation strategies, technique type, and enabling technologies.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Interference Source** & **Mitigation Strategies** & **Technique Types** & **Enabling Technologies** \\ \hline D2D & POC, MOS, RA. & Centralized, distributed. & Game theory, adaptive control, SINR tuning. \\ \hline HetNets & Cell range expansion (CRE), LB, eICIC. & Coordinated Scheduling (COS), distributed. & Carrier aggregation (CA), ABSs, CoMP. \\ \hline ICI & FR, CoMP. & Cluster-based, JP. & BF, RAC. \\ \hline AI-Based frameworks & Predictive scheduling (PS), Dynamic RA, POC. & Supervised ML, RL, DL. & Radio access network intelligent controllers (RICs), FL, data analytics. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Taxonomy of IM strategies in B5G networks based on interference source, mitigation strategies, technique type, and enabling technologies.  
\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 jointly optimize link adaptation and POC in B5G. & Hybrid ML algorithm. & Increasing throughput and spectrum utilization in interference-limited scenarios. & Interpretation and computational cost of the model remain a challenge. & [152] \\ \hline To enable slicing and resource reuse in vehicular B5G networks. & DL-enhanced intelligent virtual RA. & Maintaining QoS with minimized interference across network slices. & Needs further evaluation in high-mobility vehicular environments. & [153] \\ \hline To perform scalable and distributed D2D allocation. & RL with matching theory. & Reducing interference and signaling overhead while maintaining high QoS. & Scalability and responsiveness in UD conditions require more analysis. & [154] \\ \hline To handle dynamic power allocation in 6G HetNets. & Deep RL-based framework. & Reducing outage rate and enhancing system capacity in RT environments. & Practical deployment constrained by latency and model update time. & [155] \\ \hline To proactively control interference in future networks. & AI-driven network analytics and optimization. & Improving IPR and adaptive reconfiguration. & Dependent on data availability and accuracy. & [156] \\ \hline To enhance spectrum and IM via slicing. & Joint Q-learning and network slicing framework. & Achieving high SE and isolation across users. & Large-scale testing for heterogeneous QoS demands is needed. & [157] \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._