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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:

      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.

      3.3.1.2 Local Decision Fusion with Directional Energy Detection

      While Section 3.3.1.1 showed how the same‐channel in‐band ROC model can grow from the two‐threshold model to adding the RF neighbor dimension, this section shows that for the simple energy detection case illustrated in Figure 3.1 one can add the directionality dimension if the spectrum sensor is able to use a multisector antenna. Note that the single‐threshold simple energy detection model, which can be utilized by an augmented sensor, has no consideration of an RF neighbor as the same‐channel in‐band case does. Directional energy detection can be done by a secondary user that has a directional antenna and can transmit directionally and thus would sense the primary user signal directionality as well as the primary user signal energy level.

Let us illustrate this case with the 12‐sector antenna layout shown in Figure 3.8 where each sector is a 30° angle. Each sector senses energy independently from other sectors. The spectrum sensor would apply a single threshold ROC model per each sector and hypothesize the presence of a primary user per each sector independently.