Читать книгу Dynamic Spectrum Access Decisions - George F. Elmasry - Страница 57

Depending on the DSA design, local decision fusion can be communicated to distributed peer nodes or to a centralized arbitrator. Distributed and centralized techniques can estimate a more comprehensive spectrum map of the area of operation. Spectrum awareness can be local (node‐based), distributed (net‐based), or centralized (area‐of‐operation‐based).²³ Spectrum awareness can be expressed as a spatial map showing areas of interference per each frequency band used in the area of operation or can be potentially used.

Let us consider the local interference estimation exemplified in Figure 3.7 for a local node. Let us also consider that this type of directional interference estimation is shared between peer nodes in a distributed cooperative MANET. Each node can fuse the directional spectrum interference estimation communicated from multiple peer nodes to produce a cooperative spectrum map of the area of interference as shown in Figure 3.10. Notice how the estimated interference area can be larger than the actual interference area as spectrum directional fusion may draw a circle around the estimated overlaid triangles.²⁴ If this sensed frequency is the same frequency used by the MANET, cooperative decisions can be made to continue to use the frequency band or switch to a different frequency band based on the results of the decision fusion.

Figure 3.10 Cooperative distributed estimation of area of interference.

Figure 3.11 shows a case where a distributed cooperative MANET DSA technique estimated the interference area of the frequency band in use by the MANET, f₁. The same DSA technique also estimated the (probed) frequency band f₂, which is a potential frequency band to be used by the MANET. If the MANET trajectory²⁵ is as shown in Figure 3.11, the distributed cooperative decision may continue to use f₁ as the MANET will move away from the area where interference of f₁ is detected and avoid using f₂. This decision will occur because probing f₂ as a potential frequency to use informed the DSA technique that the MANET will encounter interference if it is to switch to f₂. The key point here is for the distributed cooperative MANET to overlay estimated interference areas of the frequency band in use with the estimated spatial areas of use of potential frequency bands and consider other factors such as trajectory to make the best spectrum use decision, which may include avoiding f₂ and switching to a third frequency f₃ or staying with f₁. Recall that a good DSA design will attempt to avoid the rippling of DSA decisions.

Figure 3.11 Cooperative estimation of overlay spatial use of different frequency bands.

A centralized arbitrator can start by using a pool of frequency bands to assign to different heterogeneous networks in a spatially separated manner, as shown in the previous chapter in Figure 2.15. Recall that the goal of the centralized arbitrator is to optimize the spatial use of spectrum resources. The challenge a centralized arbitrator faces with a case like Figure 2.15 is when a certain frequency is interfered with in a certain area, which may require tapping into a larger pool of frequency bands. Figure 3.12 shows how the frequency band f₃ can suffer from interference, as illustrated with the background large circle indicating the estimated inference area of frequency band f₃ forcing the central arbitrator to tap into an additional frequency band f₄, as indicated by the white circle.

Figure 3.12 A centralized arbitrator use of a larger frequency pool to overcome interference.

Decision fusion can occur at the local, distributed or centralized level. It all depends on the system under design. A good DSA design would create appropriate fusions at the appropriate level and share information in an optimal way to make the best use of spectrum resources. DSA is not a single solution to a single problem. Communication systems are complex and bounded by requirements, legacy systems interfacing with more up‐to‐date systems, and other dynamics that can influence the DSA design, information fusion, and decision making. The first three chapters of this book are intended to help the reader gain a broad understanding of DSA design challenges and how to approach DSA design for a given system. The next chapter covers examples of hybrid decision fusion cases and how decision fusion results can be leveraged for other cognitive capabilities such as reactive routing. Chapter 4 will give the reader an idea on how to design a hybrid DSA system while making the appropriate decision fusion local, distributed or centralized considering that DSA is part of the bigger goal of developing cognitive networks.