# Spectrum Awareness for Cognitive Radios Supported by Radio Environment Maps: Zonal Approach +
## 2 Related Works
In the literature on this topic, REM architecture is described as hierarchical, encompassing at least two levels, namely local REMs and global REM [21; 22]. Local REMs are deployed on small areas, which enables us to limit the size of the database and the complexity of analysis. On the other hand, local REMs may include more detailed information on the radio environment. Global REM is supported by local REMs that provide data from many smaller areas. Global REM contains more complex radio environmental data and thus offers unique capabilities to the frequency management authority, e.g., detection of hidden terminals, localization of transmitters, and identification of those that are sources of interferences. REMs are sometimes seen as a field of application for Big Data Analytics to enhance the Spectrum Awareness. This is for two reasons. The first one is the size of the input data, which is provided by measurement equipment and which is required to create REMs. The  
Figure 1: The idea of sharing radio spectrum between primary users (PUs) and secondary users (SUs). second reason is the amount and complexity of the output data, particularly at the global REM level. In [23], the authors presented an estimation of the amount of data stored in the database for the area of 20 km \(\times\) 20 km with the terrain resolution of 20 m, under the assumption that the monitored band is limited to 120 MHz and measurements are taken with the step of 3 MHz. It was also assumed that the records of the radio environment state are done every 10 min and that each point on the map may be represented by 256 levels (8 bits). For such conditions, after 24 h of operation, there will be 5760 maps created, with the total size of 5.7 GB. Wider frequency range and higher resolution in both time and frequency domains will lead to the growth in the size of data by up to a million times. As was mentioned above, REM needs measurement data delivered by sensors deployed in the area for which the map is being developed. Sensors are generally named Measurement Capable Devices (MCDs) [24]. In typical applications, the role of MCDs can be performed by various devices with measurement capabilities, such as simple mobile phones, smart phones, notebooks, etc. However, there are specific systems operating in dedicated bands, e.g., search and rescue or military systems. In such cases, spectrum measurements can be performed by cognitive radios, dedicated receivers, Electronic Warfare (EW) systems or Intelligence, Surveillance, Reconnaissance (ISR) systems [25]. The problem of the sensor network density and the deployment of sensors has been studied in the literature on the topic. Our approach to the problem of deployment of sensors in such an area is partly convergent with the scheme proposed in [26], as we also take into account a small number of trusted sensors creating the core part of the sensor network. The assumption of the small number of sensors results from the fact that we aim to reflect a small-scale network with a limited number of measurement devices. The analysis presented in this paper is consistent with the methodology described in [27], i.e., some zones have to be occupied by sensors more densely. A slight difference is that for us, a constant number of sensors for a given scenario and a change of the location of some sensors are the only option possible. For this reason, we analyze the impact of the deployment of sensors on REM quality using selected interpolation techniques. The main task of REM is to present an electromagnetic environment with the highest achievable quality. In the literature on the topic, the quality of such a presentation is expressed with different metrics, which enables us to compare the estimated value with the real value. In [24; 28; 29], the authors proposed the Root Mean Square Error (RMSE), and the average RMSE (ARMSE) was applied in [30] as metrics computed for selected estimated values within the analyzed area.