Читать книгу Dynamic Spectrum Access Decisions - George F. Elmasry - Страница 19
1.4 The Involvedness of DSA Decision Making
ОглавлениеFigure 1.4 shows how the DSA decision‐making process has to consider many factors. Notice that Figure 1.4 does not attempt to put the DSA decision‐making process in the context of a cognitive engine, which can be in a cognitive radio or a cognitive network node. Rather, the figure shows the place of the DSA decision‐making process independent of how it is implemented and without reference to implementation.
Figure 1.4 The involvedness of the DSA decision‐making process.
As Figure 1.4 shows, in addition to spectrum sensing information, the DSA decision‐making process involves the following:
1 1‐ Spatial and time information. Geolocation information is critical to most DSA decision‐making processes. Cooperative distributed agents have to pass geolocation information to each other for spatial reference. As for time, sensing information has to have time reference so that the DSA decision‐making agent can utilize the most relevant measurements and estimate tendencies.
2 2‐ Traffic demand. As mentioned above, the DSA decision‐making process should be fair in terms of considering traffic demand. More spectrum resources can be dynamically allocated where there is more traffic demand.
3 3‐ Constraints. These include security constraints, rules, and policies. It is critical for the DSA decision‐making process to adhere to security constraints. For example, encryption of geolocation information can be a security constraint. Secondary user rules are one example of DSA rules. Chapter 5 is dedicated to DSA as a set of cloud services that relies on policies, rule sets, and configuration parameters to constrain the DSA cognitive engine evolver. DSA has to evolve within the intended use of the managed networks. In military communications, it is critical to reflect the commander's intent in the DSA policies, rule sets, and configuration parameters in order to meet the mission needs.
4 4‐ Other information. This can include external systems and their use of spectrum, and terrain information. Terrain information can help the DSA decision‐making process differentiate between interference from other systems versus degraded signal due to terrain effects as well as evaluating a spectrum assignment before it is executed.
The DSA decision‐making process can use two important categories of information in its information repository among other information categories. The first category of information is the spectrum resources pool, which can be changed dynamically with time. The DSA decision‐making process uses and updates the spectrum resources pool according to spectrum assignments. The second category of information is the knowledge base obtained from fusing spectrum sensing information. A good design of DSA as a set of cloud services should be able to use objective DSA metrics to measure the effectiveness of past decisions and should be able to adapt future decisions based on the behavior measured through the DSA metrics. Adapting future decisions will rely on ubiquitous changes in policies, rule sets, and configuration parameters, as further explained in Chapter 5.
The cognitive engine implementation of the DSA decision‐making process can perform many simultaneous tasks. Consider, for example, the presence of different propagation models within the information repository. A process within the cognitive engine can use geolocation information and terrain data as shown in Figure 1.4 while analyzing spectrum sensing information to decide the best propagation model that can be used at a given time. As geolocation information is updated, another part of the terrain data may be used, which may require changing the propagation model. The cognitive engine continues to recommend the best propagation model to be used to analyze the effectiveness of a potential spectrum assignment before it is assigned. This makes the DSA decision‐making process able to have a level of confidence for any spectrum change and be able to recommend the best spectrum assignment in cases where more than one option of spectrum assignment can be considered.