Dynamic Spectrum Access Decisions. George F. Elmasry

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СКАЧАТЬ changes and as the estimated received signal power is changed.¹⁵ Notice that if the communications waveform has an adaptable power control feature, knowledge of the signal transmission power, the distance between the transmitter and the sensor, and the terrain type can help decide where λ₂ changes adaptively.

      Equations (3.15)–(3.18) and Figure 3.6 explain an overlay concept of the noise, in‐band signal, and interfering signal. If the noise floor estimation is accurate, the sensor can subtract w(n) from Equations (3.11)–(3.14). If the sensor has information regarding the transmission power of the in‐band signal, the distance to the emitter and terrain information, then s(n) can be estimated, allowing the sensor to hypothesize the presence of an interfering signal in a close to optimal way.¹⁶

      It is critical to understand the importance of collecting large samples by the spectrum sensor to make the ROC model viable in implementation. More importantly, noise and the interfering signal are manifested not by a simple increase in energy detection level, but by an increase in the variance of the collected samples. Relying on a small sample can lead to suboptimal results as the set of small samples can be misleading. Estimating the deviation in the energy samples is what accurately reflects the impact of noise and interfering signals and what should be used for dynamically adapting the thresholds.

3.3 Decision Fusion

      The ROC model implementation at the sensing node could be the first step towards making spectrum sensing decisions. The next step is referred to as decision fusion (DF), which uses the ROC model hypotheses outcome to make more comprehensive spectrum sensing decisions. This section presents local, distributed, and centralized decision fusion approaches to help the reader decide the most suitable place to make a DSA decision in a hybrid DSA system.

      3.3.1 Local Decision Fusion

With a spectrum sensor performing a simple energy detection decision, this may be the end of the decision‐making process that can be made locally. The local decision fusion process would rely on the local hypotheses that differentiate if the frequency band being sensed is occupied or not. If a hypothesis is persistent for the presence or absence of a signal, the decision fusion will turn the hypotheses into a decision. However, if an augmented sensor is able to utilize a multisector antenna or antenna arrays, there could be further fusion steps before making a decision. An example of a further fusion step is to identify the direction of the interfering signal when the local process hypothesizes the presence of interference relying on the difference in the energy received per sector. This case is covered in Section 3.3.1.2 The more common case to perform further local decision fusion is for the same‐channel in‐band sensing in a MANET where the reception of the sensed communications signal can be mapped to an RF neighbor. This can make the spectrum sensor in the MANET node able to create a more detailed spectrum map (i.e., identify interference directionality) without using sectored antennas,¹⁷ as explained in Section 3.3.1.1

      Notice that if the local fusion process stops without further fusion of spectrum sensing information, the higher hierarchical levels of decision making (e.g., distributed cooperative or centralized decision fusion) can make DSA decisions that are more optimum than that of the local decision‐making process. With hybrid DSA designs, fusion at the lower hierarchical level can always help reduce control traffic volume and make the overall decision‐making process more accurate even if the final decisions are left for the higher hierarchical level.¹⁸

      3.3.1.1 Local Decision Fusion for Same‐channel in‐band Sensing

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

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