# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 4 Interference Mitigation Schemes and Techniques by 3 GPP
### Comparative Discussion and Trade-Offs  
While a wide range of IMI techniques have been standardized by the 3 GPP and proposed in the literature, their effectiveness, complexity, and suitability vary depending on deployment scenarios and infrastructure capabilities. For instance, eICIC, introduced in 3 GPP Rel-10 [188; 186], is well suited for macro-pico deployments and offers simple implementation using ABS. However, it may lead to spectral inefficiency in the macro cell during ABS periods. In contrast, CoMP, standardized in Rel-11 [188], can significantly enhance signal reception at the cell edge through joint transmission or DPS but requires high-capacity backhaul and precise synchronization, which limits its feasibility in less-connected or rural networks [209; 210; 211]. POC methods [164; 171], including both open-loop and closed-loop approaches, are relatively simple and effective in IM in D2D and HetNet scenarios, especially where network infrastructure cannot support advanced coordination. However, these techniques often fall short in dynamic environments with rapidly varying interference patterns. Meanwhile, BF and COS [164; 187] provide better directional IMI and temporal separation but require CSI sharing and synchronization, increasing signaling overhead and computational demands. AI-based IM frameworks, such as RL, DL, and FL, have emerged as promising solutions capable of dynamically adapting to network conditions [147; 150; 155]. These methods outperform static rule-based approaches in dense and heterogeneous deployments by enabling RT IPR and proactive REM [156; 167; 196]. However, their practical deployment remains constrained by training complexity, inference latency, and the need for robust data collection mechanisms [212; 213; 214]. Additionally, challenges such as trustworthiness and explainability must be addressed before wide-scale adoption [215; 216; 217]. To visualize the internal dynamics of these advanced frameworks, Figure 9 presents a high-level pipeline illustrating how AI techniques integrate with network elements to perform adaptive IMI. The pipeline outlines how data are collected from distributed network elements (e.g., BSs and UEs), processed through centralized or edge-based AI modules, and used to train or update learning models within the non-RT RIC. The near-RT RIC then executes RT decisions such as PF adjustments, POC, or RA based on these models. This integration enables dynamic, context-aware IMI but also introduces challenges related to training complexity, inference latency, and standardization of interfaces between AI components and RAN nodes. In real-world deployments, the effectiveness of IMI techniques can vary significantly depending on infrastructure capabilities, mobility patterns, and load conditions. As a result, there is growing interest in adaptive switching between IM strategies to ensure robust performance under varying scenarios. For instance, when high capacity backhaul and tight synchronization are available, CoMP offers substantial gains at the cell edge. However, in backhaul-limited or rural environments, fallback to eICIC with ABS or POC techniques may provide more feasible solutions [218; 219]. Similarly, AI-based controllers, such as near-RT RICs, can monitor traffic dynamics and interference levels to orchestrate RT transitions between strategies (e.g., deactivating CoMP clusters during low-load periods or dynamically applying eICIC in response to mobility-induced ICI) [220]. Such context-aware orchestration of IM techniques represents a scalable approach for future 5G and 6G networks. It aligns with the goals of zero-touch automation and intelligent RAN slicing while addressing trade-offs between complexity, overhead, and SE [182]. Adaptive strategy switching ensures that IM solutions are not only optimized for performance but also for EE, hardware constraints, and operator policy. Overall, trade-offs between implementation complexity, CO, adaptability, and SE must be considered when selecting an IMI technique.