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Figure 1.1 Hybrid DSA decision making.

As the reader proceeds through this book, the notional view in Figure 1.1 will be seen as an oversimplification. It is meant to introduce a concept. The reader will see how in a set of heterogeneous MANETs, network gateways can perform a global cooperative distributed decision‐making protocol that is different from the distributed cooperative decision making within a network (local to a network).

      Using machine learning based engines to make these decisions can also be local, distributed, centralized or hybrid implementation. The designer of a DSA system should not limit machine learning approaches to a specific area although the design has to keep in mind that machine learning techniques should be used when they are likely to produce better decisions than stochastic model decisions. The design of a point‐to‐point link operating at a cutoff threshold of a signal‐to‐noise interference ratio (SNIR) relying on a local spectrum sensing to avoid using a jammed frequency may not need a machine learning technique. This is because the cutoff SNIR is based on the physical layer stochastic models and the best machine learning approach will perform as good as a stochastic decision‐making process in this simple case. There is a golden rule regarding the use of machine learning approaches: If a stochastic model gives the same performance as a machine learning technique, why complicate a design? The design should consider the stochastic model. As a rule of thumb, machine learning based techniques perform better as the number of factors contributing to a decision increases and as the uncertainty and change of behavior of the formed network change based on many surrounding factors. In most cases, DSA design should use a cognitive engine approach while relying on stochastic models for processing the raw physical layer metrics.

      Sharing information in a distributed or centralized manner combines spectrum sensing information results from a much larger sample of measurements than a local node has. This information sharing can reduce noise uncertainty and overcome other signal distortion challenges. The downside of sharing sensing information is the need to develop mechanisms for information sharing that minimize bandwidth consumption from spectrum sensing information control traffic. The design has to consider a tradeoff between the gain obtained from DSA capabilities and the loss of bandwidth used by the DSA control traffic.

Figure 1.2 shows a conceptual view of the DSA key processes regardless of how and where it is implemented. Local, distributed, and centralized aspects of the DSA processes have to follow the theme of observe, orient, decide, and act. The observation process can include obtaining spectrum sensing information coupled with position location information [e.g., global positioning system (GPS) locations]. The orientation process can include processing of local spectrum sensing information and adding time stamps to the spectrum sensing messages distributed to peer agents and to the centralized arbitrator. The orientation process can also include fusing spectrum sensing information received from peer agents to gain a more comprehensive view of the spectrum use status. The decision process can be local distributed or centralized decisions and it can include frequency band change or changing the communications mode of a waveform.⁴ The action process has to ensure synchronization of frequency change such that all the nodes in the network switch to the new frequency seamlessly and without losing any OTA transmitted frames.