Dynamic Spectrum Access Decisions. George F. Elmasry

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Figure 2.13 Cooperative spectrum sensing with MIMO DFC.

Notice in Figure 2.13 that all the nodes can have MIMO antennas (not just the DFC centralized entity). This model can work with a single‐input single‐output antenna for each node or a MIMO for each node. In either case, the centralized location must have a massive MIMO antenna in order to account for multipath fading.

      With this cooperative mode, the ROC decision‐making process explained in the next chapter is altered to a complementary receiving operating characteristics (CROC) decision‐making process, which takes into consideration the difference in spectral efficiency.

      2.4.6 Waveform Based Spectrum Sensing

      This spectrum sensing approach relies on pre‐knowledge of the signal to be sensed. Some commercial wireless signals use known synchronization patterns to align the receiving node processing to the received signal. These patterns can be exploited by the spectrum sensor to hypothesize the presence of the sensed signal. Signal synchronization patterns can include preambles, mid‐ambles, regularly transmitted pilot patterns, spreading sequences,¹⁵ etc.¹⁶ These patterns allow the spectrum sensor to correlate the received signal with a known copy of itself (it is essentially a form of coherent detection). This correlation process leads to a spectrum sensing result that outperforms energy detector based sensing. The reliability of the correlation process increases when the known signal length increases. Waveform‐based detection is used with known signals such as IEEE 802.11 signals.

      While the autocorrelation based signal detection explained in Section 2.4.2 can be influenced by noise and the time lag between the samples, waveform based spectrum sensing is only affected by the presence of noise as the signal patterns align with the correlation process. Chapter 3 shows how the decision‐making process of waveform based spectrum sensing may differ from that of simple energy detection spectrum sensing.

      2.4.7 Cyclostationarity Based Spectrum Sensing

With some commercial OFDM signals, waveforms are altered by the transmitter to add signatures in the form of cycle frequencies at certain frequencies. These signatures can increase the robustness against multipath fading. Spectrum sensors can leverage these features for signal sensing. These signatures introduce periodicity features. The introduced cyclic frequencies and the periodicity make the signal cyclostationary. Cyclostationary signals follow a spectral density (cyclic spectral density function, CSDF) that is leveraged by the detection process and is used to differentiate noise from the sensed signal. This differentiation happens because the modulated signal has cyclostationary characteristics while the noise has wide‐sense stationary characteristics with no correlation. Cyclostationarity characteristics can also be used for distinguishing among different types of sensed signal.

      Chapter 3 shows a type of cyclic autocorrelation function used with same‐channel in‐band signal sensing which estimates the noise spectral density separate from estimating the in‐band signal spectral density.

2.5 Euclidean Space Based Detection

      As explained in Chapter 1, DSA involves many factors other than spectrum sensing. When propagating spectrum sensing information to peer nodes or to a centralized location, the geolocations of the sensors must be attached to the spectrum sensing information. This allows a distributed or a centralized DSA process that fuses spectrum sensing information from different sensors to create a comprehensive view of spectrum use (spectrum map) in a given area of operation.¹⁷ This comprehensive view can find each sensed signal's area of coverage (AOC) to create spectrum opportunities based on locations as well as find directional beams that can show more spatial opportunistic use.

      2.5.1 Geographical Space Detection

When spectrum sensing information is propagated with location information defined by the sensor's latitude, longitude, and elevation, the fused information can create more spectrum use opportunities. The fusion process can show how at any given time, spectrum opportunity can be available in some parts of the area of operation while being fully occupied in other parts. The geographical space dimension helps the fusion process estimate propagation loss (path loss) in space to further ensure that spectrum reuse will not interfere with the sensed signals. Figure 2.14 shows an example of geographical separation creating opportunistic spectrum use in the case of a secondary user opportunistically using a primary user's spectrum. Figure 2.15 illustrates the case of a set of heterogeneous MANETs where DSA allows them to cooperatively and dynamically share a set of frequency bands (f₁, f₂, and f₃) and an area of operation indicated by the dashed rectangle while avoiding interference. Notice that the illustration in Figure 2.15 differs from dynamic use of frequency slots within a single network. With Figure 2.15, a centralized entity may be utilized to fuse spectrum sensing information collected from sensors dispersed geographically through the area of operation to dynamically use a set of predefined frequency bands.

Figure 2.14 Geographical separation creating opportunistic spectrum use for a secondary user.

Figure 2.15 Geographical separation creating the DSA of a limited set of frequency bands for a set of heterogeneous MANETs.

Figure 2.16 illustrates the advantage of having a centralized entity to arbitrate DSA. If the spectrum reuse decision is made at the local node, there remains the potential of interfering with a hidden primary user. This issue can occur if there is severe multipath fading or shadowing of the RF signal as it is emitted from the primary user while the sensor targeting the primary user's transmissions fails to sense the primary user signal. With Figure СКАЧАТЬ