Читать книгу Dynamic Spectrum Access Decisions - George F. Elmasry - Страница 67

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 traffic volume” in bps or packets per second. The design has to also create metrics to evaluate the bird's eye view trade space. The reader can refer back to Chapter 1, Section 1.2 for the comprehensive DSA design aspects and see how the bird's eye view considerations can lead to creating metrics that evaluate this trade space, which may include the following:

1 Measuring DSA decisions' rippling. This metric can measure how long a theater‐based DSA decision can last before another decision has to be made. A good centralized spectrum arbitrator should make DSA decisions that last.

2 Adapting to traffic demand. If spectrum resources are allocated more where traffic demand is high, this will result in higher throughput efficiency of the heterogeneous networks and this higher throughput efficiency can be manifested in established network performance metrics such as packet or message completion ratio, packet delay, packet delay variation (jitter), and packet loss.

3 Avoiding hidden nodes. A bird's eye view capability should overcome the hidden node phenomena and reduce the likelihood of mistakenly using a primary user's spectrum. This metric can be the measuring of the number of complaints from a primary user.example 2

By the end of this chapter, the reader should realize that Figure 4.1 oversimplified the trade space areas in order to illustrate the most important aspects of this trade space. Other factors such as the used antennas' technologies, the use of licensed spectrum, unlicensed spectrum or a mix of both, and the interactions between the cognitive DSA process and other cognitive processes the system uses add more dimensions to this trade space.