Читать книгу Dynamic Spectrum Access Decisions - George F. Elmasry - Страница 55

With same‐channel in‐band sensing, the sensed signal can be associated with an RF neighbor in a MANET without the need to use sectored antennas. The sensing node may have information about the geolocation of its RF neighbors. This will allow a local decision‐making process to map interference to its RF space. Consider Figure 3.7 where node 0 is the sensing node and there is an external RF emitter of the same sensed frequency, as illustrated by the small black circle. This emitter can be covering the gray circular area but the sensor may perceive the direction of the emitter as shown by the dashed triangle. Spectrum sensing information from RF neighbors 2 and 3 may indicate the presence of an interfering signal while spectrum sensing information from RF neighbors 1, 4, 5 and 6 may not indicate the presence of interfering signal.

Figure 3.7 Interference from some RF neighbors.

Identifying the presence of w(n) from Equations (3.15) and (3.17) with regard to certain RF neighbors can allow the local node to fuse information per RF neighbor and estimate the direction of the emitter of the interfering signal even though the MANET nodes are using omnidirectional antennas. This is particularly important in military cognitive MANET that attempts to create and update an RF spatial map in real time. The outcome of the local decision fusion can mean any of the following actions:

1 Choose different local route tables to avoid routing over the direction where interfering is present19

2 Increase the transmission power in the direction of the interfering signal

3 Have the entire MANET switch to a different waveform type that can overcome the type of detected interference20

4 Have the entire MANET switch to a different frequency band to avoid interference.21

The key concept here is that local decision fusion for same‐channel in‐band spectrum sensing can be critical in identifying the direction of interference and can be critical in making some local routing decisions as well as assisting distributed and centralized decision fusion processes reach more optimal decisions. The probability of detection and the probability of false alarm of this local decision fusion process corresponding to Equations (3.15) and (3.16) can be expressed as:

3.19

3.20

where M_Vn is a vector normalization of the different vectors M from Equation (3.2) for the different RF neighbors when the transmitting signal is detected.²²

The probability of detection and the probability of false alarm of this decision fusion process corresponding to Equations (3.17) and (3.18) can be expressed as:

3.21

3.22

where M_Vn is a vector normalization of the different vectors M from Equation (3.2) for the different RF neighbors when the transmitting signal is not detected.

Notice in Equations (3.19,3.20,3.21,3.22) that the term λ_Evn is used to indicate both vector normalization of the different RF neighbors decision thresholds but also indicates the adaptation of the decision threshold to the dynamics of the MANET where factors such as transmission power, geolocation of RF neighbors, terrain, rain, and fog are used by the machine learning process to adapt the decision threshold.

In a hybrid distributed cooperative MANET system that uses local fusion and distributed decisions, the MANET nodes would share information that includes the result of hypothesizing the presence of interference and the direction of the detected interference such that an accurate spectrum map can be made available to every node in the network. With hybrid heterogeneous network systems that use centralized spectrum managers, the centralized spectrum manager can create the most accurate spectrum map of the area of operation based on the fusions and decisions done at all the networks and also based on its own further decision fusion that can further fine‐tune the direction and boundaries of interference based on how each network perceives interference directions.