Dynamic Spectrum Access Decisions. George F. Elmasry

the enemy is using “follower jammers”. These jammers can switch to the new frequency band and continue to jam the signal over the new frequency. The follower jammers control loop will always be faster than the DSA control loop. For that reason, once the military communications DSA technology identifies the interference signal as a jamming signal, relying on waveforms with antijamming capabilities is a better option than DSA. The mix of spread spectrum and frequency hopping is one common approach in creating antijamming capabilities in military communications.

      22 22 Notice this important characteristic of DSA decision making versus symbol decoding decision making. First we projected the signal‐in‐space into one direction with positive values on one axis for energy detection. The detected energy is still considered a vector because we collect a large sample and we look for characteristics such deviation in the sample points and as such we still treat the sample points as a vectors. Now with adding a fusion process to estimate interference directions, we have created a different vector space affected by the RF neighbors' directions not the signal‐in‐space. We are using the term MVn because we want to preserve the fact that the energy detection projected into a vector in one dimension and then another spatial dimension is added.

      23 23 As Part 2 of this book presents, spectrum awareness can be more than these three cases. Distributed cooperative techniques between network gateways can be added as well as proxy of a centralized arbitrator by a gateway node.

      24 24 Estimating the interference source geographical boundaries is an important aspect of decision fusion.

      25 25 A trajectory is the general direction of the movements of all the nodes in the MANET.

      26 26 The false negative rate is complementary to detection. The probability of TP or detection is one minus the probability of false negative. Thus, the false negative axis can lead to a probability of detection axis whereas the false positive axis leads to a probability of false alarm axis.

      27 27 Notice that as the ROC curve approaches the perfect curve, the area under the curve approaches 1. The area under the curve maybe used to indicate if one ROC curve is better than another. Notice that the area under the random curve is 0.5.

Chapter 4 Designing a Hybrid DSA System

The previous chapter covered the ROC model and emphasized two distinct DSA ROC models. The simplest ROC model covered the cases similar to that of a secondary user hypothesizing the presence or absence of a primary user's signal in order to use a spectrum band opportunistically.¹ The second ROC model covered the same‐channel in‐band spectrum sensing which hypothesizes if the signal used for communications is suffering from interference by another signal or not. The previous chapter also showed how local decision fusion can add other dimensions to the spectrum sensing hypotheses such as the spatial dimension. The previous chapter also introduced some of the decisions that can be made locally and some of the decisions that can be made in a distributed cooperative or centralized manner.

      As alluded to in the previous chapter, the DSA design may not stop at the local decision fusion and the solution may rely on cooperative distributed decision fusion or the use of a centralized spectrum arbitrator. Decision fusion can be made locally, in a distributed way and/or in a centralized arbitrator. This chapter covers the DSA design approaches that need to be thought of in cognitive networks, taking into consideration a variety of reasons to include the optimization of control traffic volume, the speed of making DSA decisions, the interdependency between DSA decisions and other cognitive networking processes, and the need for the different hierarchies of DSA decisions to work in harmony.

      To make the best case for using a hybrid DSA design, this chapter uses examples from military communications systems where spectrum access needs to be more dynamic and the networks are heterogeneous and hierarchical. This book makes the case for considering hybrid DSA design in most applications. The second part of the book emphasizes approaches that can be common between different applications and areas where the DSA design approach may differ. In the second part of the book, Chapter 5 emphasizes the concept of developing DSA capabilities as a set of cloud services available at the different network hierarchical entities, which further emphasizes the hybrid DSA design consideration. Chapter 6 focuses on dynamic spectrum management for commercial cellular 5G systems. The cellular 5G dynamic spectrum management design is also a hybrid approach. Chapter 8 covers the inclusion of co‐site interference mitigation as a subset of DSA cloud services, which also emphasizes the need for hybrid DSA design approaches.

4.1 Reasons for Using Hybrid DSA Design Approaches

The case of designing DSA solutions for a mix of heterogeneous hierarchical MANETs is the best example that can be used to illustrate the reasons for using a hybrid DSA design. Figure 4.1 shows examples of the trade space that can face the design of DSA capabilities. With heterogeneous hierarchical MANETs, there is a need for a centralized spectrum arbitrator and there could be good design reasons for creating local, distributed cooperative and centralized DSA techniques. As Figure 4.1 shows, moving DSA decisions more towards distributed and centralized decision fusion can increase the bird's eye view (the overarching) understanding of the spectrum use map in the entire area of operation. The tradeoff here is the possibility of increasing the DSA control traffic volume and the increase in decision response time. Obviously, there is a trade space and one must be cognizant of the impact of this trade space depending on the specifics of the cognitive networking system under design.

Figure 4.1 Trade space to be considered for hybrid DSA decision fusion.

      Example

      In a cognitive networking system we are faced with the following:

      1 Control traffic volume is not an issue because we have an abundance of bandwidth.

      2 DSA decision time can be long because the systems can create stable topology due to limited or no mobility.

      3 The design is required to avoid processing at the lower hierarchy network nodes because of limited processing capabilities and power constraints.

      In this specific example, the design may consider moving the DSA computational complexity more towards centralized decision making. However, in most systems, one will have to consider a hybrid approach in light of the trade space illustrated in Figure 4.1. Even when designing a distributed cooperative DSA system for a single network, there is a room to consider a mix of local and distributed cooperative decision fusion.

      There is no magic bullet that suits every communications system when it comes to designing a hybrid DSA system. There is a trade space that can lead to designing different decision fusion techniques at the different hierarchical entities of the system. However, there are guidelines the design can follow. It is always better to increase the bird's eye view of the spectrum map, it is always better to reduce DSA traffic volume, and decision response times must be appropriate and must meet the system's requirements. The following section presents decision fusion cases that can be helpful to the reader to reach a good design approach for the different types of cognitive wireless networking systems.

      DSA design has to create metrics that evaluate the performance of the system in real time and through post‐processing, as covered in Chapter 5. Considering Figure 4.1, it is easy to conceptualize creating a metric for decision time trade space to be “response time” in microseconds, milliseconds or seconds depending on the system. It is also easy to conceptualize creating a metrics for DSA control traffic volume trade space to be “control