# Power Control Techniques for Interference Management-
## 3 Analysis and Findings
### Documents
#### 3.3.2 Summary of Retrieved Articles
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**Source** & **Year** & **Taxonomy (See Code) * & **Technique (See Code) ** & **Contributions** & **Limitations/Future Work** \\ \hline [PERSON] et al. [79] & 2021 & DL/OPT & GNNs & \(\bullet\) & A scalable learning-based method is developed to solve radio resource management problems in dense wireless networks. & Future work may investigate the distributed deployment of message-passing graph neural networks (MPGNNs) for radio management in wireless networks and extend the theoretical results to more general application scenarios. The existing work is limited to two NOMA user scenarios. & Addressing power allocation and spectrum efficiency. & not be enough to connect many IoT devices in the network. However, when many NOMA-enabled IoT devices are connected, interference occurs. \\ \hline [PERSON] et al. [81] & 2018 & H & GA & \(\bullet\) & Achieves high-performance video streaming. & The fluctuation constraint has not been considered in the optimization problem to reduce the complexity of GAs.The \\  & 2018 & H & GA & \(\bullet\) & Achieves high QoE at high energy efficiency while limiting co-channel interference over D2D communications in 5G networks. & The bit-length constraint has not been considered in the optimization problem to reduce the complexity of GAs.The \\ \hline [PERSON] et al. [82] & 2018 & OPT/LP & DPC & \(\bullet\) & The users’ mobility issue for multi-cell NOMA systems is left for future work. \\ \hline [PERSON] et al. [83] & 2018 & H & LBPC & \(\bullet\) & Achieves high energy efficiency between D2D communications and concurrent. & The users’ mobility issue for multi-cell NOMA systems is left for future work. \\ \hline [PERSON] et al. [84] & 2018 & H & EMS & \(\bullet\) & Develops a load balancing method to perform user association and power updates for each base station. & Future work may consider load balancing in uplink heterogeneous cellular networks. \\ \hline [PERSON] et al. [84] & 2018 & H & FUN & FUN & FUN \\ \hline [PERSON] and [PERSON] [85] & 2018 & ML & MLT & \(\bullet\) & Lowers transmission power at the nodes to reduce energy consumption and interference. & This work could be extended to perform a more extensive comparative analysis to identify and deficiencies in the simulator. \\ \hline [PERSON] et al. [86] & 2018 & OPT/LP & MILP & \(\bullet\) & The proposed algorithms could be deployed to address power for all users in a wireless network. & The proposed algorithms could be deployed to address power for all users in a wireless network. & The study analyzed interference among cooperative small cells. \\ \hline [PERSON] et al. [87] & 2019 & OPT/LP & PICA & \(\bullet\) & Future work may investigate the distributed deployment of message-passing graph neural networks (MPGNNs) for radio management in wireless networks and extend the theoretical results to more general application scenarios. The existing work is limited to two NOMA user scenarios. & The study analyzed interference among cooperative small cells. \\ \hline [PERSON] and [PERSON] [89] & 2019 & OPT/LP & PSO & \(\bullet\) & Guarantees the minimum data rate requirement of WiFi users by managing interference to improve the overall throughput of both cellular and D2D users. \\ \hline \hline \end{tabular}
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Table 12: _Cont._  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Source** & **Year** & **Taxonomy (See Code) * & **Technique (See Code) ** ** & **Contributions** & **Limitations/Future Work** \\ \hline [PERSON] et al. [66] & 2018 & OPT/LP & DPC & & \(\bullet\) Finds an optimal power allocation strategy using a distributed algorithm for multi-cell networks. & Future work may consider synchronizing the DL and UL slots and the users’ non-ideal energy harvesting characteristics. \\ [PERSON] [91] & 2018 & OPT/LP & DPC & \(\bullet\) Achieves a higher sum rate and individual rates in NOMA than OMA.