# Power Control Techniques for Interference Management-
## 3 Analysis and Findings
### Documents
#### 3.3.3 Power Control Technique Occurrences
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**Source** & **Year** & \begin{tabular}{c} **Taxonomy** \\ **(See Code) * \\ \end{tabular} & \begin{tabular}{c} **Technique** \\ **(See Code) ** **\end{tabular} & **Contributions** & **Limitations/Future Work** \\ \hline [PERSON] et al. [69] & 2020 & OPT/LP & DPC & \(\bullet\) Maximizes energy efficiency with a power optimization algorithm using Lagrangian duality. & \begin{tabular}{c} Future work may study a more realistic (nominar) energy \\ harvesting model and consider more cases and techniques, such as massive MIMO for multi-antenna situations. \\ A possible extension would be to study the optimization of the threshold for channel inversion to maximize the learning performance under privacy and power constraints. The study could be generalized to other network topologies, including multi-hop (D2D) networks. & \begin{tabular}{c} Antiother direction is to consider the implementation of digital \\ quantization introduces an additional privacy-preserving noise. \\ Future studies will seek to conduct a detailed analysis of the complexity of Inside-Out \\ Precoding (IOP) and design \\ them with less complexity and/or cost. \\ A further in-depth topology-aware study is proposed in the more generic setting of multi-radio, multi-channel wireless networks. Future studies may include (1) the development of optimization algorithms for non-orthogonal networks, (2) the alignment of mixed numerology structures with adaptive guard bands between consecutive numerologies to enhance the spectral efficiency of the system, and (3) employing different waveforms with different numerology parameters for 5G and beyond networks. \\ Future work may involve the evaluation of the influence of different numbers of D2D receivers. \\ \hline [PERSON] et al. [131] & 2019 & DPC & KMT & \(\bullet\) Improved energy efficiency and throughput are realized. & \begin{tabular}{c} Future work may involve the evaluation of the influence of different numbers of D2D receivers. \\ \end{tabular} \\ \hline [PERSON] et al. [131] & 2019 & DPC & KMT & \(\bullet\) Improved energy efficiency and throughput are realized. & \begin{tabular}{c} Future work may study a more realistic (nominar) energy \\ harvesting model and consider more cases and techniques, such as massive MIMO for multi-antenna situations. \\ A possible extension would be to study the optimization of the threshold for channel inversion to maximize the learning performance under privacy and power constraints. The study could be generalized to other network topologies, including multi-hop (D2D) networks. \\ Another direction is to consider the implementation of digital transmission, where quantization introduces an additional privacy-preserving noise. \\ Future studies will seek to conduct a detailed analysis of the complexity of Inside-Out \\ Precoding (IOP) and design & 2019 & H & IOP \\ \hline [PERSON] and [PERSON] [126] & 2018 & OPT/LP & DPC & \(\bullet\) Both primary and secondary users share the same licenced spectrum. & \
\begin{tabular}{c} Future studies will seek to conduct a detailed analysis of the complexity of Inside-Out \\ complexity of Inside-Out \\ Precoding (IOP) and design \\ them with less complexity and/or cost. \\ A further in-depth topology-aware study is proposed in the more generic setting of multi-radio, multi-channel wireless networks. Future studies may include (1) the development of optimization algorithms for non-orthogonal networks, (2) the alignment of mixed numerology structures with adaptive guard bands between consecutive numerologies to enhance the spectral efficiency of the system, and (3) employing different waveforms with different numerology parameters for 5G and beyond networks. \\ Future work may involve the evaluation of the influence of different numbers of D2D receivers. \\ \end{tabular} \\ \hline [PERSON] et al. [131] & 2019 & DPC & KMT & \(\bullet\) Improved energy efficiency and throughput are realized. &
\begin{tabular}{c} Future work may involve the evaluation of the influence of different numbers of D2D receivers. \\ \end{tabular} \\ \hline [PERSON] et al. [131] & 2019 & Taxonomy (Linear programming: LP; Dynamic power control: DPC; DL; machine learning: ML; optimization: OPT; heuristic: H) * Technique (deep learning: DL; particle swarm optimization: PSO; machine learning technique: ML; graph neural networks: GNNs; distributed joint interference management: IBM; optimal sequential quadratic programming: SQP; Lagrangian power optimization algorithm: LPOA; dual-based iterative algorithm: DBIA; Kuhn-Munkes technique: KMT; Inside-Out Precoding: IOP; Genetic Algorithm: GA; load balancing power control: LBPC; Energy-Efficient Multicast Scheduling: EMS; mixed-integer linear program: MILP; power control algorithm: PCA; mixed numerology: MN) **.** \\ \hline \hline \end{tabular}
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