# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 4 Interference in B5G Networks
### Unmanned Arial Vehicle (UAV)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Issue** & **Methodologies** & **Advantages** & **Limitations/Future Work** & **Ref.** \\ \hline Increase the network capacity while considering frequency reuse usage and inter-cluster interference in UDN environment. & K-mean clustering algorithm. & Maximize the network capacity and mitigate inter-cluster interference. & When the number of clusters increased, the interference also increased, and this led to a decrease in the channel capacity of the proposed system. & [139] \\ \hline Minimize ICI by resource allocation optimization between users in UDNs. & Cross-tier cooperation load-adapting interference management & Enhance SE, SBSs throughput, EE, and ICI allocation. & Increasing the number of users who share the same bandwidth decreased the density of SBSs, which decreased the user SINR. & [140] \\ \hline Decrease co-tier interference and maximize the data rate of the system in the UD-SCNs scenario. & -Hierarchical clustering algorithm (HCA) with distinctive forms and a hierarchical clustering approach. & Suitable for hyper-dense deploying networks of SBSs. & 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. & 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. & 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. & [141] \\ \hline Mitigate ICI and optimize energy efficiency in the uplink mm-wave UDN multicrarrier system. & Noncooperative game theory-based interference mitigation strategy in low-complexity stair water-filling (SWF) scenario. & Improve EE performance while maintaining an acceptable SE performance with low computational complexity. & Each SUE selected its own PA strategy depending on the assumption of optimizing its EE without considering the influence of other SUEs, resulting in maximizing power consumption. & Each SUE selected its own PA strategy depending on the assumption of optimizing its EE without considering the influence of other SUEs, resulting in maximizing power consumption. & [142] \\ \hline Mitigate ICI, accommodate additional UEs, and decrease the outage ratio of the system. & Completely distributed self-learning interference minimization (SLIM) scenario for independent networks. & Mitigate the interference and reduce power consumption while consumption while guaranteeing UEs’ QoS for autonomous UDNs. & When increasing the number of users, the ICI increased as well, and the system becomes overloaded, leading to maximizing the outage ratio. & By increasing the number of users, the ICI increased as well, and the system becomes overloaded, leading to maximizing the outage ratio. & [143] \\ \hline Optimize a UDN’s downlink throughput. & Novel metric technique with an appropriate degree of Special Spectrum Reuse (SSR). & Achieving an optimal trade-off between reusing the spectrum and interference to provide high throughput. & When increasing the number of SCs, the outage threshold increased. This resulted in a decrease in the throughput. & [144] \\ \hline Address traffic uncertainty while achieving the trade-off between load balancing and the probability of transmit in UDNs. & Chance constraint programming (CCP) with distributed sub-optimal user association and BS activation strategy based on the Markov approximation framework. & Capable of suppressing severe interference and using the density of BSs to achieve superior load balancing performance. & A significant time delay could be observed for the proposed system if there is a high traffic level. & [145] \\ \hline Minimize the effect of interference while maximizing the SINR in SC-UDN. & A user-centric adaptive small-cell (SC) clustering strategy relying on an enhanced K-means algorithm. & Capable of dynamically adjusting the number and size of SC clusters in response to the user’s SINR and effectively reducing the complexity. & An efficient radio resource allocation scheme based on a clustering strategy for UDNs could be designed. & [146] \\ \hline \hline \end{tabular}
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