# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 3 Classification and Evolution of Interference Management Strategies in B5G Networks
### Comparative Analysis of Optimization Objectives Across Interference Management Domains  
To provide a quantitative perspective on the evolution and focus areas of IM strategies in B5G networks, Figure 7 presents a comparative analysis across four major IM categories: D2D communication, HetNets, ICI, and AI-Based frameworks. Each domain is evaluated based on six critical optimization objectives: maximizing data rate, maximizing  
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**Interference Type** & **Common Mitigation Strategies** & **Strengths** & **Limitations** & **Example Techniques** \\ \hline D2D & POC, RA, MOS. & Low latency, SR. & Cross-tier interference, mode switching complexity. & Adaptive POC, MOS, D2D clustering. \\ \hline HetNets & CRE, eICIC, LB. & Load offloading, improved coverage. & Coordination overhead (CO), control signaling. & ABS, CRE, CA. \\ \hline ICI & CoMP, BF, COS. & Improves edge throughput, reduces overlap. & Requires tight BS coordination and backhaul. & Joint transmission CoMP, coordinated BF. \\ \hline AI-Based Frameworks & Dynamic RA, PS, RL-based POC. & Adaptive, scalable, context-aware. & High complexity, data requirements, trust issues. & RL in self-organizing networks (SONs), RICs, FL. \\ \hline \hline \end{tabular}
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Table 7: Comparative summary of IMI techniques in B5G networks across key interference domains.  
EE, maximizing SE, maximizing SINR, minimizing power consumption, and minimizing outage probability.  
The data underlying Figure 7 are extracted from the surveyed literature, for D2D [75; 76; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88], HetNets [108; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128; 129; 130; 131; 132; 133; 134; 135; 136; 137; 138], ICI [139; 140; 141; 142; 143; 144; 145; 146; 147; 148; 149; 150; 151; 152; 153; 154; 155; 156; 157], and AI-based frameworks [148; 149; 150; 153; 157]. This analysis highlights clear trends: for instance, minimizing power consumption appears as a dominant objective across all domains, particularly in D2D and HetNet scenarios where energy-constrained UEs and SC deployments are common. Maximizing SE is another prevalent goal, particularly in AI-based frameworks where dynamic SR and intelligent scheduling are increasingly emphasized.  
In contrast, objectives such as minimizing outage probability and maximizing SINR receive relatively less focus in AI-based studies, indicating an opportunity for future research to better integrate reliability-centric optimization in intelligent IM systems. Similarly, maximizing EE, while critical, is slightly less emphasized compared to maximizing data rate and SE, especially in ICI mitigation approaches where throughput often takes precedence.  
This comparative analysis provides valuable insight into the current research emphasis across different IM strategies and highlights potential research gaps, such as the need for balanced attention to EE and reliability, particularly in AI-driven IM for B5G and beyond. It also partially addresses the lack of simulation by offering a systematic numerical survey to quantify research trends.  
To further strengthen the technical analysis, Table 8 provides a consolidated summary of reported performance gains from selected IMI techniques across recent studies. This summary highlights key performance metrics, quantifiable improvements, and their deployment scenarios in B5G networks.  
Figure 7: Comparative analysis of optimization objectives across interference management domains.  
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