The radio environment map (REM) has been proposed as a vehicle of network support for cognitive radio. It’s an abstraction of real-world radio scenarios that characterizes the radio environment of cognitive radios in multiple domains, such as geographical features, regulation, policy, radio equipment capability profile, and radio frequency (RF) emissions. The REM, which is essentially an integrated spatiotemporal database, can be exploited to support cognitive functionality of the user equipment, such as situation awareness (SA), reasoning, learning, and planning, even if the subscriber unit is relatively simple. The REM can also be viewed as an extension to the available resource map (ARM), which is proposed to be a real-time map of all radio activities in the network for cognitive radio applications in unlicensed wide area networks (UWANs).
From the cognitive radio user’s point of view, the network support to the cognitive radio can be classified into two categories: internal network support and external network support. The internal network refers to the radio network with which the cognitive radio is associated. Along with various communication services, the internal network can provide some cognitive functionality as well.
The external network refers to any other networks that can provide meaningful knowledge to support the cognitive functionalities of the radio. For example, a separate sensor network could be dedicated to gather information for cognitive radio networks. Both internal and external networks can contribute to building up the REM and can be employed in a collaborative way. For instance, location information needed for a cognitive radio can be obtained either from internal network support through a network-based positioning method for indoor scenarios, or from external network support through the global positioning system (GPS) for outdoor scenarios.
Just as how a city map helps a traveler, the REM can help the cognitive radio to know the radio environment by providing information on, for example, spectral regulatory rules and user-defined policies to which the cognitive radio should conform; spectrum opportunities; where the radio is now and where it is heading; the appropriate channel model to use; the expected path loss and signal-to- noise ratio (SNR); hidden nodes present in the neighborhood; usage patterns of PUs (primary users) and/or secondary users (SUs); and interference or jamming sources.
The REM plays an important role in the cognition cycle of cognitive radio, as illustrated in the figure below;
Both direct observations from the radio and knowledge derived from network support can contribute to the global and/or local REM. The radio’s environment awareness can be obtained from direct observation, such as spectrum sensing, and/or from the REM. Reasoning and learning help the cognitive radio to identify the specific radio scenario, learn from past experience and observations, and make decisions and plans to meet its goals. The global REM and/or the local REM should be updated once action is taken or scheduled by the radio to keep the REM’s information current. The underlying techniques to support a REM include, but are not limited to, database design, database management, database transactions (such as query, search, and update), and data mining.
The REM contains information at multiple layers, as illustrated in the figure below;
By integrating various databases, the REM enables or supports cognitive functionality for radios with different levels of intelligence. The REM helps a cognitive radio to be aware of situations and make optimal adaptations according to its goals; for legacy or hardware reconfigurable radios, the REM facilitates smart network operations by providing cognitive strategies to the network radio resource management control. Just like the city map that is informative to every traveler, no matter whether driving a car or taking the bus, the REM is transparent to the specific radio access technology (RAT) to be employed regardless of whether the subscriber radio is cognitive or not. With the help of the REM, a radio can become cognitive of performance metrics, the application, topology, and network (routing), as well as the medium access control (MAC) and physical (PHY) layers of communication stacks under different and varying radio environments. The REM can support various network architectures: centralized, distributed, or heterogeneous networks, or even point-to-point communications. It can also support collaborative information processing among multiple nodes for obtaining comprehensive awareness. With the REM, a cognitive radio can choose an access network based on cost, data rate, spectral efficiency, and many other performance metrics. The optimal adaptation is subject to the constraints of various radio scenarios, such as available services, available spectrum, user policy, and the capability of the radio equipment.
