# 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  
The increasing complexity and dynamic nature of B5G networks have introduced new challenges to conventional IM techniques. Traditional optimization-based approaches often struggle to adapt in RT to UD deployments, heterogeneous environments, and rapid UM. Recently, AI has emerged as a promising direction for developing adaptive, self-organizing IM strategies in B5G systems. AI methods, like machine learning (ML) [145], DL [146], and RL [147], can make smart choices by learning from past and current data, which helps better manage interference between different layers of the system. Various AI-driven IM methods have been proposed in the literature. These approaches target RA, POC, UA, and BF in highly dense and heterogeneous environments. RL-based algorithms can dynamically adapt RA policies based on environmental feedback. DL models can recognize complicated interference patterns, and supervised learning algorithms have been used to improve link adaptation when the channel and interference conditions are known. [PERSON] et al. [148] proposed a Q-learning-based RL technique to manage interference in UAV-assisted HetNets. The results demonstrated a significant improvement in SE and adaptability compared to traditional schemes. In [149], the authors introduced a multi-agent Q-learning framework for MOS and RA in D2D-enabled HetNets, which effectively mitigated interference in dynamic environments. A federated deep reinforcement learning (FeDRL)-D2D approach is developed in [150] to manage energy-efficient IMI in D2D \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 project the transmitted signal over the desired area and align most of the interference onto predefined interference areas at the interfered receiver end. & ICI subspace coordination algorithm for MIMO communication. & Maximizing the ST by 28\% compared with traditional algorithm. & An ultra-reliable, low-latency service scenario will be considered. & [134] \\ \hline To minimize the effect of ICI and maximize end user throughput. & Adaptive fractional fairness. & Maximizing the SINR and throughput performance of the end users while ensuring fairness preservation. & \(\bullet\) & CoMP will be used for edge cell users. & [135] \\ \hline To mitigate the effect of International jammers and ICI in UL HetNets. & A strategy for mitigating proactive interference based on RFA. & Improving the QoS of the proposed scenario. & \(\bullet\) & UA decoupling with RFA will be considered to improve the UL coverage. & [136] \\ \hline \multirow{4}{*}{To mitigate the effect of ICI in UDNs.} & A hybrid ICIC scheme based on power and channel allocation. & \(\bullet\) & Maximizing the \(\bullet\) & The RT deployment of the proposed scenario will be considered. & [138] \\ \cline{1-1}  & & & proposed system. & be considered. & \\ \cline{1-1}  & & & delay and latency. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._assisted sixth generation (6G) networks, demonstrating enhanced EE and reduced co-tier interference. [PERSON] et al. [151] proposed a ML-based solution to address UA and inter-beam PA in mm-Wave B5G networks. The suggested model enhanced the total system sum rate and minimized interference. A hybrid ML algorithm is presented in [152] for joint link adaptation and POC in interference-limited networks, resulting in improved ST and interference resilience. In [153], the authors applied DL-enhanced virtual RA for vehicular B5G networks to support low-interference and QoS-guaranteed slicing. Their solution effectively maintained service isolation while minimizing resource collisions. [PERSON] et al. [154] proposed a semi-distributed RL and matching game-based method for RA in D2D communications. The proposed scheme showed reduced interference and improved SE.