# Beyond REM: A New Approach to the Use of Image Classifiers for the Management of 6G Networks
## 2 Related Work  
The transition in mobile network environments, specifically the shift towards 6G networks, necessitates adaptive decision support tools [1]. Radio Environment Maps (REMs) play a crucial role in cellular network management. However, the capacity of REMs to accommodate rapidly changing conditions has become increasingly strained [13]. Traditionally, REMs were generated through the deployment of numerous sensors across a specific area, which collected Received Signal Strength (RSS) measurements to create a coverage map. Unfortunately, this static approach was found to be insufficient for effectively capturing the dynamic nature of radio environments. Despite these limitations, REMs have proven to be key tools in efficient spectrum management [14; 15; 13], a necessity in light of the impending spectrum scarcity accompanying the rise of 6G networks. Achievements in REM accuracy have been realized by using synthetic images [16] and spatial statistics in combination with Bayesian hierarchical models [17; 18]. However, the inherent unpredictability and complexity of wireless environments continue to pose significant challenges. A broad spectrum of publications has focused on REM construction and implementation. Various machine learning (ML) mechanisms have been employed for purposes such as updating and construction, among others. These strategies include traditional clustering techniques such as K-means and DBSCAN [14; 13], Gaussian mixture models [15], and Bayesian networks [19]. Advanced strategies for REM updating, such as Siamese neural networks and attention mechanisms, have also been incorporated [20]. Table 1 offers a detailed survey of various ML-based strategies for REMs in current literature, summarizing the applications, data sources, and identified gaps in each method. \begin{table}
\begin{tabular}{l l l l l} \hline
**Reference** & **Method Proposed** & **Source of Information** & **Application** & **Gaps Identified** \\ \hline
[13] & K-means clustering, DBSCAN & RSS measures & Spectrum management & Dynamic nature of the wireless environment. \\ \hline
[14] & SVM, decision trees & RSS measures & Spectrum sharing & Heterogeneous nature of the wireless environment. \\ \hline
[15] & Gaussian mixture model & RSS measures & Spectrum sensing & Uncertainty of the wireless environment. \\ \hline
[20] & Siamese neural networks, attention mechanism & Image data & REM updating & Scalability of the algorithm. \\ \hline
[19] & Bayesian network & RSS measures & REM construction & Accuracy of the algorithm. \\ \hline
[16] & Genetic algorithm, DenseNet & Image data & Image classification & Robustness of the algorithm to noise. \\ \hline
[21] & Self-labeling & RSS measures & REM construction & Security of the algorithm. \\ \hline
[17] & Spatial statistics, Bayesian & RSS measures, CSI & REM construction & Temporal dynamics of the wireless environment. \\ \hline
[18] & Spatial statistics, Bayesian model & RSS measures, geolocation data & REM construction & Spatial correlation of the wireless environment. \\ \hline
[22] & Spatial statistics, Deep Neural Networks & RSS measures, signal strength data from multiple sources & REM estimation & Accuracy of the algorithm in different environments. \\ \hline
[23] & Artificial intelligence, Internet of Things, 5G & Smart grid & Next-generation smart grid & Cost-effectiveness of the solution. \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of works on Mt. for Radio Environment Maps. The scope of REM applications has recently been expanded, with a focus on optimizing network operations. Examples include the use of REMs in secrecy-energy efficient hybrid beamforming for satellite-terrestrial integrated networks [12], rate-splitting multiple access schemes for IoT support in satellite and aerial-integrated networks [24], and joint beamforming design for refracting Reconfigurable Intelligent Surfaces (RIS)-aided hybrid networks [25]. These studies highlight the versatile application of REMs across various network operations.