# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 5 Critical Analysis and Open Research Challenges
### Future Research Directions
#### 5.8.9 Practical Challenges in AI-Based IM  
Although AI-based IMI strategies such as DL, RL, and FL have demonstrated strong performance in dynamic B5G environments, their real-world adoption faces notable practical constraints. First, training high-performing AI models requires extensiveand representative datasets, which are often unavailable or difficult to collect in mobile, privacy-constrained wireless networks [158; 160]. Supervised and RL models further suffer from long training times and the need for retraining in response to changing interference conditions. Second, deploying AI inference on edge devices presents significant limitations. Mobile UE, IoT nodes, and edge gateways typically have constrained compute, memory, and power budgets, which makes it difficult to run DL models with low latency [161]. This undermines the RT responsiveness required for functions like POC or dynamic RA in URLLC and mMTC scenarios. Additionally, the inference cost is not only computational but also includes latency introduced by data movement, model complexity, and lack of hardware acceleration in edge platforms. Techniques like pruning, quantization, and tiny ML can help reduce model size, but they often trade off prediction accuracy or robustness under diverse interference conditions [241; 242]. FL, while beneficial for preserving privacy and decentralizing learning, brings challenges of model divergence due to non-IID data, increased communication overhead for aggregation, and slow convergence [159; 163]. These issues are amplified in dense HetNets with frequent topology changes. Finally, the lack of explainability in AI decisions limits operator trust and compliance in mission-critical networks. Most DL models act as black boxes, offering little transparency into why certain IM decisions are made [162; 253]. Regulatory and standardization gaps also persist regarding model lifecycle management, auditing, and cross-vendor interoperability. Overcoming these challenges is essential to bridge the gap between theoretical AI potential and practical, scalable deployment in interference-critical B5G scenarios. As highlighted in Figure 7, while maximizing SE and minimizing power consumption have been primary focuses across different IM domains, future research must also address the relatively underexplored objectives, such as outage probability minimization and EE optimization, especially in AI-based IMI frameworks. Furthermore, while AI-based IMI techniques offer promising adaptability in dynamic B5G environments, their practical deployment introduces challenges that extend beyond technical feasibility. As discussed in Section 4.4.5, FL suffers from non-IID data across devices, leading to model divergence and slow convergence [254]. Additionally, performing AI inference at the edge is constrained by latency, compute limitations, and energy constraints, which hinder RT decision-making in interference-critical scenarios [158]. These challenges are compounded by a lack of standardization in AI interfaces, trust frameworks, and model lifecycle management, especially in multi-vendor RAN environments [255]. Overcoming these technical and standardization barriers is essential to transition AI-based IMI strategies from simulation testbeds to scalable, real-world B5G deployments. In addition, the transition from traditional optimization to AI-native IM frameworks demands not only performance improvements but also explainable and trustworthy models. As discussed in Section 4.4.5, FL suffers from non-IID data across devices, leading to model divergence and slow convergence [254]. Performing inference at the edge is further constrained by latency, computing limitations, and energy constraints, hindering RT decision-making [158]. These issues are compounded by the lack of standardization in AI interfaces, trust frameworks, and lifecycle management, especially across multi-vendor RAN ecosystems [255].