# 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
In [137], the authors investigated resource management in UDNs based on single-carrier OFDMA in an uplink scenario. A conflict-graph strategy based on machine learning that uses the uplink SINR and RB allocation data was proposed. Simulation results showed that the proposed strategy is both practical and precise. Therefore, this strategy was able to be implemented with network auto-adjustment and optimizing intelligent RA. Yet, the effect of CCI in this strategy was found to be severe due to the reuse of the RBs, resulting in throughput degradation. In [138], the authors investigated a joint RA and SIC in UDNs based on NOMA in a two-tier downlink scenario. An interference management strategy was proposed that involves joint optimization of clustering, sub-channel allocation, and SIC. The simulation results indicated that the average capacity and spectrum efficiency for the proposed strategy increased significantly as compared with optimal Femto base station sub-channel allocation (OFBSSA) and cluster-based Femto base station sub-channel allocation (CFBSSA) strategies. However, the average capacity of the proposed system decreased significantly due to many reasons such as an increase the co-tier interference, the number of FBS users in overlapping areas, and interference among users. In [139], the K-mean clustering algorithm was applied for determining the optimum number of clusters to increase network capacity while considering frequency reuse usage and inter-cluster interference. The BSs and UEs were distributed randomly throughout the cluster using the PPP scenario. The simulation results indicated that the best number of clusters is around 13, and it was discovered that the operating frequency band has the greatest influence on the optimum number of clusters. However, when a certain threshold was crossed, the inter-cluster interference increased with the increment of the number of clusters. Nonetheless, when the number of clusters increases, the interference also increased, and this led to a decrease in the channel capacity of the proposed system. In [140], the authors investigated the interference management problem for two-tier UDNs in the uplink scenario. A cross-tier cooperation load-adapting interference management (CCLA-IM) distributed strategy was suggested to minimize ICI by RA optimization between users in UDNs. The simulation results stated that the suggested strategy provides superior performance in terms of SE, SBSs throughput, EE, and ICI allocation whereas users' density and traffic loading were altered in UDNs. Nevertheless, the increased number of users who share the same bandwidth decreases the density of SBSs because of the increased uplink interference from the surrounding users. Furthermore, the increment number of users per SBS led to an increase in the mutual interference among various users served by various SBSs, which caused a decrease in the average EE of the proposed system. In [141], the authors investigated the coordinative interference management in UD-SCN based on OFDMA in a two-tier downlink scenario. A new and simple-to-implement interference reduction technique that depends on a hierarchical clustering algorithm (HCA) between SBSs to calculate the member pairs was proposed. The simulation results indicated that the proposed scenario could increase the data rate of the network by 422.13 % for a network of 100 cells as compared with the non-cooperative scenario in a UD-SCN. Furthermore, it was especially suitable for hyper-dense deploying networks of SBSs. Yet, the effect of CCI in this technique was found to be severe due to the sub-channel being allocated to more than one SUE, resulting in data rate degradation in the proposed system. Power-Domain approaches  
In [142], a new non-cooperative game theory-based interference mitigation strategy for uplink power allocation was proposed to mitigate the ICI and optimize energy efficiency in the uplink mm-wave UDN multicarrier system.