# Coexistence in Wireless Networks: Challenges and Opportunities
## 5 Interference and Mitigation Techniques
A reliable method for mitigating interference between IMT-advanced and FSS in the 3400-4200 and 4500-4800 MHz bands is suggested in [106], employing null steering MU-MIMO SDMA. In order to produce spatial nulls towards FSS Earth stations, the current precoding matrix is altered. Through simulation, a numerical method for determining the interference power at FSS stations from IMT-Advanced base stations is derived and put into practice, greatly speeding up the solution process. The method's efficiency was evaluated across varying separation distances and FSS Earth station orientations under both co-channel and adjacent channel sharing scenarios. Deep Q-learning-dependent transmission power control, which dynamically modifies a system's transmission power depending on current interference, is one of the newer methods for mitigating interference in dynamic environments [107]. This strategy has been especially successful in High Altitude Platform Stations (HAPS), where intelligent power adaptation is necessary for spectrum sharing with terrestrial and non-terrestrial networks (NTNs). This technique balances interference levels and increases system throughput by optimizing power allocation through the use of reinforcement learning. Message Passing (MP)-based distributed Multi-User Detection (MUD) is a recent development that improves decoding efficiency and reduces interference in satellite communication networks [108]. In situations requiring Inter-Satellite Links (ISLs), where effective detection algorithms are needed to handle signal distortions and multi-user interference, this technique is very pertinent. MP-based MUD improves detection accuracy while lessening the computational load on individual satellite nodes by utilizing distributed processing. Static frequency division is the strategy used in traditional spectrum allocation techniques, which frequently leads to inefficient use and increased interference. By dynamically reassigning frequency bands between Terrestrial Networks (TN) and Non-Terrestrial Networks (NTN) in response to current interference circumstances and spectrum demand, Reverse Spectrum Allocation solves this problem [57]. This method improves overall coexistence efficiency, reduces spectral congestion, and permits smooth spectrum sharing. The bar graph with the interference reduction is shown in Figure 9. CNN-based denoising offers the highest interference reduction (95%) but demands high computational resources, making it ideal for radar and wireless systems. Techniques like beamforming and null steering provide strong performance (90-92%) for high-precision applications, while adaptive power control (75%) suits low-complexity scenarios like cellular networks. Coexistence in the communication network was analyzed and studied, and the research advantages and limitations are tabulated in Table 2. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Proposed Technique/Algorithm** & **Advantages** & **Limitations** & **References** \\ \hline Deep interference mitigation and denoise real-world FMCW radar signals & CNN was used & To evaluate and optimize based on real data & [9] \\ \hline Co-channel coexistence analysis between 5G & Parameters considered are height of station, distance, and antenna patterns & Specific to high frequency of 40 GHz & [18] \\ \hline Interference mitigation using adaptive beamforming with the RLS algorithm & Minimum distance to mitigate the interference & Only the distance parameter is considered & [72] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the coexistence and interference mitigation techniques.