# Propagation Attenuation Maps Based on Parabolic Equation Method +
## 2 Radio Environment Maps  
The REMs are a way of representing complex and multi-domain information for cognitive radio. By sharing and disseminating information contained in the REM, it is possible to provide cognitive functionalities such as situational awareness, deducing, learning, planning, and decision-making support. Consequently, the REM can be a database distributed among secondary users, centralized, or in a hierarchical topology where the central database interacts with local databases. REM are multi-domain databases containing terrain information, sensor data, simulation results of propagation models, regulations, and policies. PAMs are used in the planning of MANETs (also as cognitive radio networks) to increase the efficiency of such a network [37]. Using the measurements anywhere in the network area is impractical. Therefore, as part of the REM, spectrum measurements from available radio network nodes are collected. Then, data fusion is performed, i.e., available measurements are converted to estimate the level of interference in other, unexplored locations. The REM contributes to the development of cognitive mechanisms and the building of long-term knowledge. The REM introduces environmental awareness that is more difficult to achieve by the individual capabilities of individual network nodes. Therefore, the REM enables network nodes to be transformed into smart ones [22; 23]. REM facilitates the adaptation of the radio network to a new environment. Therefore, the REM is a promising concept for the efficient operation of a cognitive network without overloading the cognitive node and can be connected to a network-based cognitive engine. Due to the REM dissemination, an individual secondary user can get to know the radio environment much more than is the case with the limited sensing possibilities. In this case, it is possible to use cooperative techniques to interact with primary users, avoid the hidden nodes problem, and increase the entire system's efficiency. Due to its architecture, the REM can be broadly divided into global (G-REM) and local (L-REM). The REM complexity depends on the number of nodes and channels, the network coverage area, and the information granularity. REM synchronization is crucial for the accuracy and reliability of the information provided. Data must be effectively disseminated to avoid information that is obsolete or out-of-date. The basic functionality of the REM is to build a dynamic interference map for selected frequencies in every interesting place. For this purpose, maps are used that represent the coverage of a given area with a radio signal, called radio-frequency (RF) layers of the REM, or shortly RF-REM [19]. For example, it is achieved by spectrum measurements obtained from sensors (i.e., network nodes) at specific locations. Since it is impractical and often impossible to perform measurements anywhere in the area of network operation, the available measurements are used to estimate the level of disturbances in other locations. Another approach is to use propagation damping prediction methods. It is a universal solution that does not depend on the current location of the sensor and allows you to analyze the area in which the network did not work before. Hence, the use of different propagation models for this purpose is common practice. The methods of creating the RF-REM can be divided into three main categories [19]:  
* direct methods based on the interpolation approach, using spatial statistics and measurements of signal levels made in specific locations, make a direct estimation of the missing data (e.g., nearest neighbor, inverse distance weighting (IDW), and kriging);
* indirect methods based on the use of the TX location and its parameters as well as propagation models;
* hybrid methods combining both of the above approaches. The above groups of methods have their advantages and disadvantages and are characterized by different computational complexity.