Читать книгу Dynamic Spectrum Access Decisions - George F. Elmasry - Страница 44

Regardless if one is building spectrum sensing capabilities from the bottom‐up or utilizing an existing spectrum sensing technology, one must understand the different spectrum techniques that can be used as reviewed in this chapter. The decision‐making process using spectrum sensing information is covered in the next chapter.

When designing a system that uses DSA, one may build the best spectrum sensing capabilities and choose the best spectrum sensing hardware and configure it for the appropriate bands to sense and use the sensing parameters appropriately; however, if some critical factors are ignored, the design can be way suboptimal. One of the critical factors worth mentioning at the conclusion of this chapter is making sure a concept called blanking signal is applied. On a platform that has both a communications waveform and a spectrum sensor, it will be important for the communications waveform to inform the spectrum sensor of transmission time intervals. During these intervals, the spectrum sensor should refrain from collecting spectrum sensing information since spectrum sensing will be dominated by the emitted communications signal. Even if sensing is at a different frequency band from the transmission band, frequency domain harmonics can impact the sensing accuracy, as explained in Chapter 8.

Another critical factor worth mentioning is the relationship between dwell time and the signal characteristics. When performing same‐channel in‐band sensing, many aspects of the signal characteristics are known. For example, a frame transmitted over the air can have a preamble. The presence of the preamble can inform the energy detection process that the sensed energy level includes the presence of the communications signal. The absence of the preamble can inform the energy detection process that the sensed energy level is for noise or noise plus interfering signal. One can map dwell time to the frame time, taking multiple samples from the frame, or one can sample multiple frames in tandem with a larger dwell time. This can yield a good estimation of the noise floor when sensing the in‐band frequency. With augmented sensors, performing energy detection is separate from the demodulation process. Cyclostationary characteristics can also help define a dwell time that informs the energy detection process whether or not the sensed energy level includes the presence of the sensed signal. Having a dwell time that can allow the energy detection process to overlay noise, in‐band signal, and interfering signal can lead to a lower probability of misdetection and a lower probability of false alarm.

There are other critical factors to consider as discussed in the following chapters and introduced in Chapter 1, such as abstraction, the value of same‐channel in‐band sensing, making the best out of local, distributed, centralized, and hybrid decisions, the reduction of spectrum sensing control traffic, the powerful features of augmented sensing, and the role of policies and security with DSA systems.