# Automatic Clustering for Improved Radio Environment Maps in Distributed Applications
## 3 Related Work  
REMs play an important role in enhancing the QoS for UEs in 5G and IoT environments [44]. This was first introduced by [45], and aimed to spatially interpolate geolocation measurements to construct a whole coverage map. Interpolation techniques are mainly divided into deterministic and statistical approaches [46]. For deterministic interpolation,  
Figure 1: Multi-layer communication in edge computing-enabled applications. the geolocated measurements are combined to predict the corresponding value at a target location. In fact, larger weights are attributed to the measurements that are closest to the location of interest. However, this approach does not differentiate between the contributions of different measurements taken at the same distance from the target location. Several research works have proposed improvements to the construction of REMs as they are key features for network planning and optimization of radio resources [32]. However, most of the proposed prediction techniques aim to construct only one model to construct an REM for the whole area of interest. Various works use the Kriging technique [47] as a prediction technique. This approach aims at observing the weights of the attributes in relation to neighboring observations. The overall objective of the observation is to enhance the prediction of the RSRP at an unobserved location. A Bayesian Kriging spatial interpolation technique [33] was first utilized in older generation wireless networks. It was used in 3G for predicting the received signal code power coverage [34]. It was also used in LTE for RSRP analysis of the wireless coverage [5]. The performance of the fixed rank Kriging technique has been studied for cellular networks. In fact, this later technology was also applied in these networks [3, 48]. The use of Kriging and its derivatives showed good performance in terms of prediction accuracy. With the arrival of 5G, the use of narrow beams, steered simultaneously to different UEs, improves spectral efficiency through spatial multiplexing. To this end, a grid of beams (GoB) focused toward UEs is generated with the deployed antenna elements in both vertical and horizontal directions [49]. To construct an accurate REM in the context of 5G, and in different contexts and environments in the area of interest, being able to distinguish between heterogeneous environments is required. In fact, estimating a common path loss exponent for an entire area produces errors in the constructed REM. Hence, it is necessary to use automatic clustering to distinguish between different environments by grouping locations that exhibit similar propagation characteristics. This leads to better prediction of the propagation characteristics of other locations within the same cluster. In this work, we aimed to show the gains offered to RSRP prediction by the use of automatic clustering. In [37], the authors propose a clustering mechanism to classify different wireless signal coverage based on a fixed threshold. This latter requires human intervention for each propagation environment. In practice, this involves a considerable amount of time, which results in additional deployment costs. To solve the problem, an automatic clustering technique can be deployed at the edge computing unit. A variety of clustering techniques are proposed in the literature. The KMC is a very popular method as it is simple to implement and has been applied in various applications [50]. Given the above reviews, it can be seen that the process of building REM can be challenging. It can also be seen that even if the above works address REM building issues, they involve a trade off between complexity and scalability. On the other hand, the proposed work in this paper provides a low-complexity approach to construct more accurate REM, based on an automatic clustering technique. The approach also considers scalability, as it reduces the REM computation process.