2.8 Performance Evaluation
2.8.2 Non-Cooperative Sensing Evaluation
In this subsection, the spectrum sensing is done locally. In a first step, we focus on the per- formance of the proposed detector in detecting vacant spectrum sub-bands in the PU band using the sliding window technique given by Figure 2.2. The validation of this detection mode is based on experimental measurements captured by EURECOM’s RF Agile Platform [51]. We select a sliding window sizeTsamples and slide the window over the spectrum band to obtain AIC values and Akaike weight values for each analysis windows. A time-lag sliding window of Lsamples was used to scan all the signal. The test statistic used in this case was given by (2.22) and (2.23).
In a second step, we evaluate the performance of the proposed detector in terms of PU presence detection using the binary hypothesis test given in (2.28). We use in this part the scenarios test described in Section 1.4.3 using the DVB-T OFDM system.
Sub-bands DetectionIn order to evaluate the performances of the spectrum sensing method in terms of spectrum holes detection, measurements by the RF Agile Platform at EURECOM are considered [51]. RF Agile Platform covers an RF band from 200MHz to 7.5GHz, with a maximum bandwidth of 20MHz. It is able to receive and transmit almost all the existing commercial radio access technologies. Concerning the transmitted power, the target is comparable to existing GSM terminals (+21dBm). On the receiver side, the noise figure is from 8 to 12dB, depending on the frequency band. The RF equipment include up to 4 antennas and 4 RF chains. In addition, it allows for experimenting with system on-chip architectures for wireless communications.
At first stage, we focus on GSM signals at carrier of953MHz with a bandwidth of7680MHz.
The received signal in the frequency domain is shown in Figure 2.3 (a). Time channel samples are stored in a vector of size p (with p equal to 20480 samples). Parameters vRice, σRice and σRayleigh are estimated overT = 533samples which correspond to the GSM bandwidth (equal to 200kHz). From Figure 2.3 (a), it is clear that only sub-bands around 950MHz, 951.5MHz, 954.5MHz and956MHz contain data. The remaining sub-bands are idle. Figure 2.3 (a) depicts Akaike weight values for Rice and Rayleigh distributions obtained from the GSM signal. These results demonstrate that the DAD detector estimates efficiently the distribution of the received signal. In fact, whenWRice → 1(orWRayleigh → 0) we show that the PU is present, otherwise (i.e.WRice →0), we show that the PU is absent.
At second stage, we considered a WiFi signal at the carrier of2430MHz. The size of the sli- ding window is around 500kHz. From Figure 2.3 (b) we can see that similar to the case of GSM signal, we obtain interesting results in terms of sub-bands detection for the proposed blind spec- trum sensing technique.
PU Signal DetectionWe analyze now the performance of the DAD detector, in comparison with detectors presented in Chapter 1, in detecting primary signals. We use here the binary hy- pothesis test given by (2.28). We choose proper performance criteria given by the probability of false alarmPF A and the probability of detectionPD, in the three proposed simulation scenarios presented in Subsection 1.4.3.
Figures 2.4 (a), (b) and (c) depict the detection comparison of the DAD detector with CD, ED and KLD detectors in the three proposed scenarios. From the simulation results, we see that the CD detector performs the best. Subsequent to the CD detector is the proposed DAD detector, with approximately 2dB reduced performance compared to the CD, and ED detector, approximately 3dB behind CD. The worst performance is obtained by the KLD detector, which shows a perfor- mance reduction of approximately 5dB compared to CD detector. The ROC curves in Figures 2.4 (d), (e) and (f) for all detectors can be observed to have very similar slopes. Hence, the proposed detector exhibit very interesting results in term of spectrum detection in a perfectly blind way.
Two things can be inferred from this. It is expected that if knowledge of signal parameters is provided, feature detectors are the optimal schemes for detecting the PU signal. These expectations are confirmed when considering the simulation results seen in Figure 2.4. As expected, the CD detector gives the best performance in the three scenarios cases. The other thing is to expect that the proposed DAD detector have best distribution estimation compared with the KLD. Recall that the KLD algorithm is based on the measurement of the distance between two probability distributions, the estimated received PU signal distribution and a generated Gaussian distribution. On the other hand, the DAD algorithm estimates distributions parameters directly from the received signal. This confirms that the proposed technique is the optimal for estimating the PU signal distribution.
When considering the simulation results for scenario 2 and scenario 3, another obvious fact is observed. It is clearly seen how introducing channel distortion in terms of multipath and sha- dow fading clearly deteriorates the detection performance. While the detection performance under AWGN dropped rapidly from 1 to PF A over a range of about 10dB, the slope of the detection curve falls off considerably slower, extending the SNR range of the drop to at least 30dB, espe- cially for scenario 3. Recall that the Rice factor for the multipath fading in scenario is K= 10, and that this corresponds to a very strong LOS component compared to the multipath components.
Hence the Rician multipath fading is expected not to cause significant performance degradation.
The shadow fading on the other hand, has a standard deviation of 12dB, and can be expected to decrease performance over a wide range of SNRs. This is clearly seen as the case in Figure 2.4 (c).
−150 −10 −5 0 0.1
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(c)PDvs. SNR : Scenario 3 (d) ROC curves : Scenario 1
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(e) ROC curves : Scenario 2 (f) ROC curves : Scenario 3
FIGURE2.4 – Performance evaluation of the DAD detector in terms of PU signal detection in non- cooperative way using an DVB-T OFDM primary user system : Probability of detection versus SNR curves withPF A = 0.05and ROC curves with SNR=−7dB, and, sensing time =1.12ms andp= 2048.
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DAD: 4 SUs DAD: 2 SUs DAD: 1 SU
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(a)PDvs. SNR : Scenario 1 (b)PD vs. SNR : Scenario 2
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DED−AIC: P D = 0.99 DED−AIC: PD = 0.90
(c)PDvs. SNR : Scenario 3 (d) SNR vs.M : Scenario 3
FIGURE 2.5 – Performance evaluation of the DAD detector in terms of PU signal detection in cooperative way using an DVB-T OFDM primary user system : Probability of detection versus SNR curves withPF A = 0.05and the required SNR versus the number of collaborating usersM.