# Automatic Clustering for Improved Radio Environment Maps in Distributed Applications  
[PERSON]  
1 Department of Computer Engineering and Networks, College of Computer and Information Sciences,  
Jout University, Saskata 72341, Saudi Arabia 1  
2 Department of Computer Science, College of Computer and Information Sciences, Jouf University,  
Sakaka 72341, Saudi Arabia 2  
3  
4  
5  
6  
7  
8  
9  
10  
1  
2  
1  
2  
1  
2  
2  
3  
4  
5  
6  
7  
8  
9  
10  
11  
1  
2  
2  
3  
5  
6  
7  
8  
9  
10  
11  
12  
Furthermore, it improves decision making and analysis in cognitive radio systems, especially in aspects related to resource allocation [14]. It also improves interference analysis and predictability of coverage opportunities in heterogeneous networks [5; 15; 16]. With regard to REM types, the proposed models in the literature are categorized into two types. The first type uses available prior knowledge of the environment, while the second type is based on measurement-based predictions [17]. As is evident, REM greatly contributes to the improvement of wireless communication. Edge computing is one of the most promising fields in next-generation technologies. It is considered to be a new paradigm that aims at addressing several challenges currently encountered by the traditional cloud computing paradigm (e.g., latency, scalability) [18]. It addresses scalability and latency limitations [19; 20]. It also addresses computation challenges, since edge computing aims at minimizing the process delay by bringing computation closer to data sources. Edge computing greatly depends on connectivity, especially wireless communication. Technologies such as 5G and 6G are considered essential to the continued progress of edge computing. This is due to the ability of wireless technologies to provide ubiquitous data access. Additionally, wireless technologies provide great support for heterogeneous devices and equipment, which is a key factor in enabling broader data exchange capabilities. Therefore, maximizing wireless capabilities and addressing coverage issues are much considered in research to improve the performance of distributed applications and edge computing [21]. Geolocalized radio measurements can be retrieved with the help of the MDT feature [10]. This has been possible since user equipment (UE) began including global positioning system information. Geolocalized measurements can be used with spatial interpolation techniques to construct an REM, which is measured by exploiting the collected readings gathered in a radio environment. The collected measurements are utilized to build an REM to characterize wireless channels. This is achieved through multiple propagation mechanisms [22; 23; 24; 25; 26; 27; 28]. In fact, these mechanisms affect the reference signal received power (RSRP), and may also affect the construction process for REMs. Multiple research works have used the well-known Kriging technique [29] for geostatistical interpolation [16; 30; 31]. The Kriging technique depends on exploiting shadowing and median path loss. It is assumed that the fast-fading communication signal is averaged out at the receiver [32]. The REM can be employed to enhance the quality of service (QoS) for UEs in 5G, and beyond, networks. It builds the whole coverage map along the UE paths by interpolating measurements made in different places. A Bayesian Kriging spatial interpolation technique [33; 34] was initially proposed for 3G. It targeted the prediction of the received signal code coverage [34]. It also initially worked for long-term evolution (LTE) RSRP coverage analysis [5]. Several research works have studied the main shortcomings in mobile and wireless networks. For example, the work in [35] showed that the relationship between logarithmic distance and RSRP values had an error of 8-9 dB in urban environments. It also showed that the error increased to around 15 dB in rural areas.