# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 4 Interference in B5G Networks
### Ultra-Dense Networks (UDNs)
#### 4.3.3 Related Work in UDNs
The simulation results demonstrated that the suggested strategy considerably improves EE performance while maintaining an acceptable SE performance as compared to previous iterative water-filling strategies. Moreover, the suggested strategy offered a low computational complexity. However, each SUE selected its own PA strategy depending on the assumption of optimizing its EE without considering the influence of other SUEs, which caused an increase in power consumption. In [143], the authors investigated interference mitigation in downlink indoor coverage scenarios with autonomous UDN deployment. A completely distributed self-learning interference minimization (SLIM) scenario for independent networks under a model-free multi-agent reinforcement learning (MARL) structure was suggested for mitigating ICI, accommodating additional UEs, and decreasing the outage ratio of the system. The simulation results demonstrated that SLIM outperforms several existing known interference coordination schemes in mitigating interference and reducing power consumption while guaranteeing UEs' QoS for autonomous UDNs. Nonetheless, by increasing the number of users, the ICI increased as well, and the system became overloaded, leading to maximizing the outage ratio. Spatial-Domain approaches  
In [144], the authors considered a HetNet where a macro-cell layer provides essential service and coverage was overlayed by an ultra-dense layer of SBSs. A novel metric technique was proposed to optimize a UDN's downlink throughput with an appropriate degree of special spectrum reuse (SSR). The simulation results demonstrated that a UDN must achieve an optimal trade-off between reuse of the spectrum and interference to provide high throughput and low outage performance. Nevertheless, when the number of SCs increased, the outage threshold also increased. This is mainly due to the increasedusers' number in the outage, specifically for the total reuse. This resulted in a decrease in the throughput. In [145], the authors adopted chance constraint programming (CCP), in which occasional violations of the load threshold at BSs were permitted, and they introduced a control parameter, called risk level, to address traffic uncertainty while also achieving the trade-off between load balancing and the probability of constraint violation. The numerical results stated that the suggested strategy is resistant to traffic uncertainty. Furthermore, it was able to suppress severe interference and use the density of BSs to achieve superior load balancing performance compared to existing benchmark systems. However, a significant time delay could be observed for the proposed system if there was a high traffic level, in which each BS was compelled to offload the traffic from other BSs. This caused an increase in the congestion risk due to the arrival of burst traffic. In [146], the authors investigated the small-cell clustering in UDN based on orthogonal frequency division multiplexing (OFDM) in a two-tier downlink scenario. A user-centric adaptive small-cell (SC) clustering strategy relying on an enhanced K-means algorithm was presented to decrease interference in UDN. The simulation results demonstrated that the proposed approach is capable of dynamically adjusting the number and size of SC clusters in response to the user's SINR and effectively reducing the complexity associated with the clustering process. It is important to mention that the radio RA strategy was not considered in this strategy, which has a significant effect on the optimal fairness among the users. Table 4 summarizes the related works of UDNs discussed in this section.