Cooperative spectrumsensing algorithm in cognitive radio by simultaneous sensing and BER measurements
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Abstract
This paper considers spectrum utilization, the probability of detection in cognitive radio (CR) model based on cooperative spectrum sensing with both simultaneous adaptive sensing and transmission at a transmitting secondary user (TSU), and the bit error rate (BER) detection with variation checking at a receiving user (RSU). In this paper, a novel detecting model is proposed in the being considered scenario for the fullduplex TSU’s simultaneous sensing and transmitting. A spectrum sensing scheme with an adaptive sensing window is designed to improve the spectrum utilization with a high SNR. At RSU, the BER variation is used further to detect whether a PU is active or not. Data fusion based on the proposed adaptive sensing scheme and the BER detection is processed for better decison on the spectrum holes. Simulation results show that (1) simultaneous spectrum sensing with an adaptive window improves the spectrum utilization compared with a periodical sensing and (2) cooperative spectrum sensing with the BERassisted detection improves the probability of detection and spectrum utilization compared with the single simultaneous sensing at TSU.
Keywords
Cognitive radio Cooperative spectrum sensing Spectrum utilization Adaptive window BERassisted detection1 Introduction
Cognitive radio (CR) is an important strategy to enhance spectrum efficiency, allowing the secondary user (SU) to utilize the licensed spectrum of the primary user (PU) when PU is inactive. This kind of time slot is called as a spectrum hole [1, 2]. CR has two important functionalities: spectrum sensing and adaption [1]. Energy detection is conventionally used for spectrum sensing [3]. Traditionally, SU firstly detects the spectrum band using energy collection periodically. If a spectrum hole is found, SU will immediately utilize this time interval to transmit data by upconverting to the PU’s frequency band. Once SU senses the activity of PU, it will immediately stop transmitting and give the spectrum back to PU. Then SU keeps detecting the spectrum in its own period till the coming of next spectrum hole.
Different from the model described above where SU executes sensing only when it does not transmit data [4, 5], a couple of fullduplex spectrum sensing schemes have been proposed in which the SU can simultaneously implement transmitting and sensing whether PU is active or not. Researchers present a new design paradigm for future CR by exploring the fullduplex techniques to achieve the simultaneous spectrum sensing and data transmission in [6], published in a magazine to explore key research directions, and proposed an adaptive scheme to improve SUs’ throughput by switching between the “ListenandTalk” and “ListenbeforeTalk” protocols in [7]. Nontimeslotted CR has been investigated in [8] and [9] and the full duplex spectrum sensing scheme is presented for nontimeslotted cognitive radio networks in [8]. Afifi and Krunz [10] exploits selfinterference suppression for improved spectrum awareness/efficiency in simultaneous transmitandreceive mode. Results in [11] show the performance of antenna for the fullduplex transmission in CR. The possibility of extending fullduplex designs to support multiple input, multiple output (MIMO) systems using commodity hardware has been discussed in [12]. Tsakalaki et al. [13] describes the basic design challenges and hardware requirements that restrain CRs from simultaneously and continuously sensing the spectrum while transmitting in the same frequency band.
This paper also considers the fullduplex spectrum sensing and utilization in CR. The being considered CR in this paper consists of two SUs. One SU transmits data and another SU receives the data. The SU which is used to transmit the data is called TSU while the one receiving the data is called RSU. Both TSU and RSU are radio transceivers. In this paper, a novel detecting algorithm is proposed by combining an adaptive sensing in TSU and the BER detection in RSU, where a dedicated line is required for the transmission of the result of BER detection to the TSU and data fusion is processed in the TSU. The difference of our algorithm from the existing fullduplex cognitive radio lies in the adaptiveness of the sensing window, the feedback of BER detection and the data fusion of the sensing in TSU and the BER detection in RSU. One point to be noted is that we ignore the overhead effect in this paper because the data is less and negligible. The corresponding probability of detection as well as the false alarm are provided, on the basis of which the utilization of spectrum holes is mathematically derived. With the proposed spectrum detecting algorithm, a spectrum sensing scheme with an adaptive sensing window is designed to improve the spectrum utilization. Several schemes based on the adaptive sensing window have been proposed in literature [14, 15]. In this paper, the novel detection algorithm is followed by an adaptive spectrum sensing algorithm to provide an improved CR. Furthermore, in order to enhance the overall detection accuracy, this paper feeds back the detection results based on the estimated bit error rate (BER) by RSU to TSU. By data fusion, this information is combined with the detection algorithm using an adaptive sensing window at TSU. The combined detection algorithm provides a better probability of detection and consequently a higher spectrum hole utilization. Although there is a tradeoff between spectrum sensing and data transmission, it is also important to improve its spectrum utilization [16].
The rest of this chapter is organized as follows. Section 1 mentions the problems associated with recent developments in spectrum sensing. Section 2 describes the system model and the spectrum sensing procedure that is proposed in our novel P2P cognitive radio. Section 3 shows the energy detection algorithm at TSU and derives its corresponding probability of detection as well as false alarm, spectrum utilization and our proposed adaptive sensing algorithm. Section 4 describes the detection algorithm that is based on estimating the BER at RSU. Section 5 gives the derivations of spectrum utilization under periodical sensing, simultaneously sensing with fixed window and adaptive window, as well as cooperative sensing with simultaneous sensing and BER detection. Simulation results are reported in Section 6 followed by a conclusion in Section 7.
2 Problem statement
Most existing spectrumsensing technologies have two main problems. First, at TSU, periodical spectrum sensing cannot determine the periodical duration of spectrum sensing. Here, we consider simultaneous spectrum sensing and transmitting. Secondly, spectrum sensing at TSU often brings missdetection of PU signals when PU becomes a hidden node compared to TSU. Thus, we propose a novel BERassisted detection to improve the spectrum sensing.
2.1 Simultaneous sensing/transmitting at fullduplex TSU
2.2 Assisting detection based on BER At SU receiver
We have proposed the concept of cooperative spectrum sensing between TSU and RSU based on estimating the BER at RSU. This is useful because a PU transmitter sometimes becomes a hidden node compared to TSU which means that TSU cannot detect the existence of PU. There are two kinds of hidden nodes. The two cases are shown below.
2.2.1 Case I: TSU is out of the transmitting range of a PU transmitter
2.2.2 Case II: PU signal is hidden from TSU
From the two cases above, one can notice that it is necessary to assist spectrum sensing at TSU with additional spectrum sensing at RSU based on BER measurement. If the BER at RSU is large enough, the presence of PU is detected even if the received energy of PU at TSU is below the detection threshold.
3 System model
 Step I: TSU senses the PU spectrum by energy detection.

Step II: If TSU finds a spectrum hole, it awaits sensing of a spectrum hole at RSU by BER estimation. If not, it continues spectrum sensing by energy detection alone at TSU.

Step III: If RSU also detects the existence of a spectrum hole, TSU starts to transmit data. At the same time, it continues to sense the spectrum band to detect when PU becomes active.

Step IV: If TSU finds out that PU is active either by itself or with the help of RSU, it stops transmitting at once and goes back to Step I.
In the following section, we discuss spectrum sensing at TSU based on energy detection and at RSU based on BER estimation.
4 Energy detection at TSU
4.1 The energy detection algorithm and corresponding probability of detection at TSU
where y(n), 0≤n≤N−1, denotes the received signal at TSU, N is the number of samples, α p(n) denotes the PU signal at TSU, v(n) denotes the AWGN with zero mean and variance σ ^{2}, α represents the channel gain between PU transmitter and TSU which depends on their relative positions and surrounding environment. Hypothesis H _{0} indicates that PU is inactive while hypothesis H _{1} indicates that PU is active.
where f _{ s } is the sampling frequency at TSU and τ is the duration of W. In order to estimate the energy, TSU estimates the energy for a time duration τ, which corresponds to f _{ s } τ baseband samples.
where \(\gamma _{1} = \frac {\alpha ^{2}{\sigma _{p}^{2}}}{\sigma ^{2}}\) is the SNR under hypothesis H _{1}, \(\alpha ^{2}{\sigma _{p}^{2}}\) denotes the power of the received PU’s signal, \(\gamma _{2} = \frac {{\sigma _{s}^{2}}}{\sigma ^{2}}\) is the SNR under H _{2}, \({\sigma _{s}^{2}}\) denotes the power of the received SU’s signal, and γ _{3} is the SNR under H _{3}, which can be obtained as \(\gamma _{3} = \frac {{\sigma _{s}^{2}}+\alpha ^{2}{\sigma _{p}^{2}}}{\sigma ^{2}}=\gamma _{1}+\gamma _{2}\).
where λ _{1} is the assumed threshold value which needs to be selected appropriately and Q(·) represents the QFunction.
From Eq. (12), it is concluded that the threshold values λ _{1} and λ _{2} depend on σ ^{2} and on the SNRs: γ _{1}, γ _{2} and γ _{3}, but not on the length of the sensing window W.
4.2 Discussion of power attenuation between PU and TSU
where \(PL\left (d \right)=d^{{g_{2}}/{10}} \) and \(\varphi =10^{{g_{1}}/{10}}\phantom {\dot {i}\!}\). When the distance d becomes large, the value of α ^{2}decreases. For Case I in Fig. 2, the path loss P L(d) is large when TSU is out of the transmission range of PU transmitter. Thus, the power gain α ^{2} decreases to the point that TSU cannot detect the presence of PU. In Fig. 3, the value of φ is small because of obstructions between PU and TSU. Thus, the power gain α ^{2} is too small for PU signal to be detected.
4.3 Antenna cancellation technique in duplex TSU at P2P cognitive radio
In the P2P cognitive radio, because of the power attenuation over the radio channel between PU and TSU, the power of the transmitted signal s(n) at TSU, i.e., from its own transmit antenna, is much larger than the received signal at TSU from PU α p(n). This makes it difficult to realize a full duplex operation at TSU because of this large power difference. It is highly possible that TSU cannot detect the energy of the weak received PU signal unless special care is undertaken. One way is to decrease such a power difference by making α p(n) and s(n) of the same order of magnitude.
According to [18], a technique called antenna cancellation can be used for fullduplex operation. It combines the existing RF interference cancellation with digital baseband cancellation to reduce selfinterference. Selfinterference cancellation aims at decreasing the power difference between α p(n) and s(n). In Fig. 7, the value of s(n) is decreased to the same energy level as α p(n) using antenna cancellation technique. Thus, a fullduplex operation is enabled and TSU is able to detect the presence of PU while it is transmitting signals. In other words, once the energy α p(n) is close to that of s(n), transmitting will not affect the detection of PU.
4.4 Spectrum sensing with adaptive window

Step I: initialization  Let W=W _{ max } so that TSU can detect a real spectrum hole with high probability.

Step II: active PU  If W>W _{ min }, assign W=W−W _{ min } to reduce the possibility of missing spectrum holes with a small duration; if W≤W _{ min }, assign W=W _{ min }.

Step III: inactive PU  Assign W=W _{ max } to enhance the probability of detecting the coming PU.
5 BER assisting detection at RSU
5.1 Novel TSU and RSU modules
In order to accomplish the proposed BERassisted spectrum detection scheme, a new TSU architecture as well as a new receiver architecture is proposed based on using a dedicated control channel.
5.1.1 Proposed architecture for TSU
5.1.2 Proposed architecture for RSU
5.2 Modulation assumption
where \(AP_{1}(t) = \left \{\begin {array}{lcr} A && \text {Sending a bit ``0''} \\ A && \text {Sending a bit ``1''} \end {array} \right.\) for TSU signal,
while \(AP_{1}(t) = \left \{\begin {array}{lcr} B && \text {Sending a bit ``0''} \\ B && \text {Sending a bit ``1''} \end {array} \right.\) for PU signal, where A and B are determined by their own transmit power and their propagation attenuation, f _{ c } is the carrier frequency and v(t) is the continuoustime white Gaussian noise with its discrete form v(n) in Eq. (1), with a zero mean and a variance σ ^{2}. It is also assumed that the probability of transmitting a bit “0” or “1” is equal for both TSU and PU, and that coherent detection is used at RSU.
5.3 BER with/without a PU signal
where the optimal decision threshold T=0 is used.
where \(AP_{2}(t) = \left \{\begin {array}{lcr} A \pm B && \text {when TSU sends a} ``0'' \\ A \pm B && \text {when TSU sends a} ``1'' \end {array} \right.\).
5.4 Detection algorithm and probability of detection at RSU
Usually, a reliable communication system has a relatively low BER, e.g., lower than 10^{−3} level, [19], so \(Q\left (\frac {A}{\sigma }\right)\) in Eq. (20) must be small. By looking at the Qfunction table, \(\frac {1}{2} \left [Q\left (\frac {A+B}{\sigma }\right) + Q\left (\frac {AB}{\sigma }\right) \right ]\) in Eq. (26) is much higher than \(Q\left (\frac {A}{\sigma }\right)\) in Eq. (20). So, intuitively, the change of BER could be used for detecting the spectrum hole.
5.4.1 Method I
One must note that a higher \(\hat {P}_{e}\) leads to a larger probability of detection \(P_{d_{3}}\) and a smaller probability of false alarm \(P_{f_{3}}\).This makes sense because a higher \(\hat {P}_{e}\) results in a bigger difference with P _{ e }. It should be noted that when \(\hat {P}_{e} >> P_{e}\), the optimal threshold value \(T\approx \frac {\hat {P}_{e}}{2}\).
5.4.2 Method II
which show the same performance as Method I.
5.5 Probability of detection based on a cooperative scheme between TSU and RSU
When TSU receives the BER which is estimated at RSU, it will make the final decision of whether PU is active or not based on a threshold. Here, we can obtain the probability of detection based on such a cooperation between TSU and RSU. The condition for cooperative detection is that the spectrum hole is firstly detected at TSU. If PU is sensed by TSU, the training sequence will not be transmitted to RSU.
In Eqs. (35) and (36), \(P_{d_{1}}\) represents the probability of detection in hypothesis H _{1} at TSU while \(P_{d_{2}}\) is the probability of detection in hypothesis H _{3} at TSU. \(P_{d_{3}}\) denotes the probability of detection based on BER estimation at RSU. All of these parameters have been discussed in the previous sections.
According to the derivation from Eqs. (33)–(38), we can show that the new spectrum sensing scheme improves the probability of detection. It decreases the interference from SU. However, it also brings an increase in the probability of false alarm which might decrease the spectrum utilization.
6 Spectrum utilization
6.1 Case I: ideally no noise or negligible noise
6.1.1 Ideally no noise or negligible noise in periodical spectrum sensing
where f _{ sens } is the frequency of periodical spectrum sensing. It is a fixed value for a CR spectrum sensing system. N _{ i } is the sensing instants in the ith spectrum hole.
One must note that f _{ sens } W≤1 because the sensing period \(\frac {1}{f_{sens}}\) is always greater or equal to the length W of the sensing window. It is therefore reasonable to assume that when the sensing frequency f _{ sens } increases, the duration of data transmission decreases.
where W is a fixed value when the licensed channel is sensed by a sensing window with a fixed sized. In Eq. (47), one can conclude that the utilization of the spectrum decreases when the size of the sensing window becomes larger. This result makes sense because the wasted time when the spectrum is not used is equal to the size of the sensing window during the sensing stage.
6.1.2 Ideally no noise or negligible noise when sensing and transmitting at the same time
Similar to traditional periodical spectrum sensing, the duration D _{ i } of a spectrum hole, at a full duplex TSU, can be regarded as a random variable foll an exponential distribution with an assumed mean μ whose cumulative distribution function (CDF) and probability density function (PDF) are shown in Eqs. (40) and (41).
where \(\bar {T}_{H}^{d} = \bar {T}\) and \(\bar {D}_{H} = \mu \). In Eq. (52), W represents the size of the first sensing window in one spectrum hole. The utilization of the spectrum also decreases when the size of the sensing window W becomes larger. The size of the first sensing window is adaptive and changeable. Its range, W _{ adaptive } should be W _{ min }<W _{ adaptive }<W _{ max }. The aim of having an adaptive window is to decrease W and improve spectrum utilization. It regulates the tradeoff between the probability of detection and spectrum utilization because the probability of detection increases with W, while spectrum utilization decreases with W.
6.2 Case II: noisy environment
In general, there is nonnegligible noise which increases the probability of false alarms. False alarms cause spectrum holes not to be used. Thus, spectrum utilization is affected by the probability of false alarm.
where \({T}_{i}^{loss}\) denotes the wasted durations in the ith spectrum hole h _{ i } which are caused by false alarms while \(\bar {D}_{H} = \mu \), \(\bar {T}_{H}^{d} = \bar {T}\bar {T}^{loss}\), \(\bar {T}^{loss}\) is defined as the expected value of the wasted spectrum duration \(E\{T_{i}^{loss} \}\) in ith spectrum hole.
6.2.1 Noisy environment in periodical spectrum sensing
Here, W is the size of the spectrum sensing window. Its value is W=W _{ max }. This is because, in our adaptive window algorithm the size of the sensing window does not change when PU is inactive.
From Eq. (59), one can conclude that spectrum utilization \(\eta ^{period}_{noise}\) decreases when the probability of falsealarm \(P_{f_{1}}\) increases. On the other hand, the utilization \(\eta ^{period}_{noise}\) becomes lower when the size of the sensing window W becomes larger which implies that Eq. (59) makes sense.
6.2.2 Noisy environment when sensing and transmitting at the same time
where N _{ duplex } represents the number of sensing times in transmitting and sensing stage. In Eq. (60), \(E\{T_{i}^{loss}  \text {sensing stage} \}\) is the expected value of the wasted spectrum durations during the spectrum sensing in the sensing stage. Its expression is shown in Eq. (57). The sensing stage occurs once at the beginning of the spectrum hole.
In Eq. (60), \(E\left \{T_{i}^{loss} \left  \text {sensing and transmitting stage}\right.\right \}\) denotes the expected value of the wasted spectrum durations during the transmitting and sensing stage.
By comparing Eq. (57) with Eq. (61), one can see that the only difference between \(E\left \{T_{i}^{loss} \left  \text {sensing stage} \right.\right \}\) and \(E\{T_{i}^{loss}  \text {transmitting and sensing stage}\}\) is that the probability of false alarm in hypothesis H _{0} is different from the corresponding false alarm in H _{2}.
In Eq. (65), when the probabilities of false alarm \(P_{f_{1}}\) and \(P_{f_{2}}\) increase, the utilization \(\eta _{noise}^{duplex}\) becomes smaller. On the other hand, the utilization η also becomes lower when the size of the sensing window W becomes larger. Thus, we can conclude that Eq. (65) makes senses.
6.3 Spectrum utilization in cooperative spectrum sensing between TSU and RSU
From Eqs. (68) and (69), we can conclude that the utilization of the spectrum depends on the probability of cooperative falsealarm \(P_{f_{2}}^{coop}\). The loss of spectrum \(\bar {T}^{loss}\) becomes larger when the cooperative probability of falsealarm at the “transmitting and sensing” stage \(P_{f_{2}}^{coop}\) increases. This is reasonable because the “transmitting and sensing” stage occupies most of the spectrum hole for a CR full duplex system. If \(P_{f_{2}}^{coop}\) increases, it implies that the CR transmitter will spend more time on spectrum sensing instead of sensing and transmitting. In other words, some of the spectrum hole is missed without transmitting data at TSU. Thus, it is reasonable to assume that the utilization of the spectrum \(\eta _{noise}^{coop}\) decreases with the increase in \(P_{f_{2}}^{coop}\) in Eq. (69).
In addition, according to Eqs. (68) and (69), we can also conclude that spectrum utilization \(\eta _{noise}^{coop}\) is larger when the training sequence W _{ ts } that is used in BER estimation has a larger duration. However, it is possible that the longer length of the training sequence causes interference to PU especially at the end of a spectrum hole when PU might become active.
7 Numerical analysis and simulation results
7.1 Parameters
7.1.1 Basic parameters for the simulation
RF simulation parameters
RF parameters  Value  

Bandwidth B  5MHz  
Noise spectrum density V(f)  −174dBm/Hz  
Noise factor NF  7dB  
Noise power σ ^{2}  −100dBm  
SNR of PU signal at detector γ _{1}  −20∼10dB 
7.1.2 Parameters for performance evaluation
The probability of detection P _{ d } of a spectrum hole is an important factor when evaluating the performance of the proposed spectrum sensing algorithm. It is used to weigh the ability for TSU to avoid interfering with PU when PU is active. It is necessary to measure P _{ d } at TSU and RSU. That is why we need to obtain the probability of cooperative detection as well.
On the other hand, the probability of false alarm detection P _{ f } is another important factor when PU is inactive. It affects the utilization of the spectrum η, which is another parameter when evaluating the performance of the proposed system. The utilization of the spectrum is also another parameter that plays a fundamental role in a CR system.
7.2 Probability of detection
In the simulations, we examine the probability of detection at TSU first. As previously discussed, there exist two kinds of probabilities of detection and probabilities of false alarm: \(P_{d_{1}}\), \(P_{f_{1}}\) at “sensing" stage and \(P_{d_{2}}\), \(P_{f_{2}}\) at “sensing and transmitting” stage. Because the selection of the detection thresholds λ _{1} and λ _{2} is based on Eq. (12), the value of \(P_{d_{1}}\) and of \(P_{d_{2}}\) increase while the value of \(P_{f_{1}}\) and \(P_{f_{2}}\) decrease. Thus, when we evaluate the detection performance at TSU, we must examine the probability of detection \(P_{d_{1}}\) and \(P_{d_{2}}\) instead of \(P_{d_{1}}\), \(P_{d_{2}}\), \(P_{f_{1}}\), and \(P_{f_{2}}\).
In Fig. 14, increasing n implies that the power of the PU signal becomes larger relative to that of the TSU signal. In this case, PU is easier to be detected which causes \(P_{d_{3}}\) to increase. Actually, the ratio n between the PU signal power and the SU signal power can influence the experiment substantially. From Fig. 14, the simulation results are better than theory. This is reasonable because the theoretical results are based on statistical assumptions while each instant of BER detection is carried out in a discrete and independent fashion in the simulations. When n decreases from 2 to 0.5. The difference between the simulation results and the theory becomes smaller. Regular power control technology can force A=B. In the next simulation, we assume that the power of the SU signal is the same as the power of the PU signal. By comparing \(P_{d_{3}}\) in Fig. 14 with \(P_{d_{1}}\) and \(P_{d_{2}}\) in Fig. 12, one can see that the detection \(P_{d_{3}}\) at RSU is greater than the detection at TSU in theory when A≥B It is helpful when a missed detection occurs at TSU such as in case I and case II in section 1.2.
7.3 Probability of false alarm
7.4 Spectrum utilization
In this section, we discuss the spectrum utilization by periodical spectrum sensing at TSU, simultaneously sensing/transmitting, and cooperative spectrum between TSU and RSU.
7.4.1 Simulation results for periodical sensing and simultaneous sensing
When the SNR is relatively low (e.g., the SNR is from −20 to −5 dB), in either of the two spectrum sensing algorithms, the utilization of spectrum becomes higher with the increase of W from W _{ min } to W _{ max }. This is reasonable because the probability of false alarm decreases and the probability of detection increases when W increases.
When the SNR is greater than 0 dB, the spectrum utilization of periodical sensing \(\eta _{noise}^{period}\) becomes a constant value equal to 60 % regardless whether W is W _{ max } or W _{ min }. In this case, the probability of false alarm is always 0 and the probability of detection is always 1 no matter how large the sensing window W is. \(\eta _{noise}^{period}\) depends on the ratio of sensing window size to sensing period f _{ sens } W which is a constant.
7.4.2 Simulation on simultaneous sensing with adaptive window
Once again, the simulation results are better than theory because the expected value of the wasted duration \(T_{i}^{loss}\) in Eqs. (57) and (61) is larger than the one we obtain in the simulations. This is because it is less possible for false alarm to occur twice or more. The wasted duration in the simulations is mostly W or 2W which is less than \(E\left \{T_{i}^{loss} \left  \text {sensing stage} \right.\right \}\) and \(E\left \{T_{i}^{loss} \left  \text {sensing stage and transmitting stage} \right.\right \}\).
7.4.3 Simulation on cooperative spectrum sensing
Figure 19, indicates that the spectrum utilization is improved by using an adaptive window. In this case, spectrum utilization approaches 100 % when the SNR is between −5 and 10 dB. However, the spectrum utilization is still low when the SNR is between −20 and −10 dB. It is because we use energy detection to implement spectrum sensing at TSU. Energy detection has a poor detection performance when the SNR is low(i.e., it causes the increase of the probability of false alarm and the decrease of the probability of detection). In order to improve the probability of detection and consequently spectrum utilization, cooperative spectrum with BER estimation is introduced.
8 Conclusions
In this paper, a cooperative spectrum sensing between TSU and RSU is implemented in CR. Our novel adaptive spectrum sensing scheme improves the spectrum utilization. Both the theoretical analysis and simulations show that the usage of an adaptive window improves the spectrum utilization from 96.72 to 99.6 %. Furthermore, BERassisted detection greatly helps the adaptive spectrum sensing. Simulation results demonstrate that cooperative spectrum sensing can offer a better performance. It increases the utilization of the spectrum from around 44 % to around 48 % in the simulations and increases spectrum utilization from around 37 % to around 43 % in theory when SNR is −20dB.
Notes
Acknowledgements
A short version of this paper was presented in IEEE PIMRC 2014 [20].
References
 1.W Krenik, A Wyglinski, L Doyle, Guest editorialcognitive radios for dynamic spectrum access. IEEE Commun. Mag. 5(45), 64–65 (2007).CrossRefGoogle Scholar
 2.S Haykin, Cognitive radio: brainempowered wireless communications. Selected Areas Commun. IEEE J. 23(2), 201–220 (2005).CrossRefGoogle Scholar
 3.JE Salt, HH Nguyen, Performance prediction for energy detection of unknown signals. Vehicular Technol. IEEE Trans. 57(6), 3900–3904 (2008).CrossRefGoogle Scholar
 4.W Cheng, X Zhang, H Zhang, Full duplex wireless communications for cognitive radio networks (2011). arXiv preprint arXiv:1105.0034.Google Scholar
 5.W Afifi, M Krunz, in Dynamic Spectrum Access Networks (DYSPAN), 2014 IEEE International Symposium On. Adaptive transmissionreceptionsensing strategy for cognitive radios with fullduplex capabilities, IEEE, Tyson’s Corner, Virginia, USA, (2014), pp. 149–160.Google Scholar
 6.Y Liao, L Song, Z Han, Y Li, Full duplex cognitive radio: a new design paradigm for enhancing spectrum usage. Commun. Mag. IEEE. 53(5), 138–145 (2015).CrossRefGoogle Scholar
 7.Y Liao, T Wang, L Song, Z Han, in Global Communications Conference (GLOBECOM), 2014 IEEE. Listenandtalk: fullduplex cognitive radio networks, IEEE, Austin, USA, (2014), pp. 3068–3073.Google Scholar
 8.W Cheng, X Zhang, H Zhang, in MILITARY COMMUNICATIONS CONFERENCE, 2011MILCOM 2011. Full duplex spectrum sensing in nontimeslotted cognitive radio networks, IEEE, Baltimore, USA, (2011), pp. 1029–1034.Google Scholar
 9.W Cheng, X Zhang, H Zhang, in Proceedings of the 3rd ACM Workshop on Cognitive Radio Networks. Imperfect full duplex spectrum sensing in cognitive radio networks (ACMNew York, USA, 2011), pp. 1–6.CrossRefGoogle Scholar
 10.W Afifi, M Krunz, in INFOCOM, 2013 Proceedings IEEE. Exploiting selfinterference suppression for improved spectrum awareness/efficiency in cognitive radio systems, IEEE, Turin, Italy, (2013), pp. 1258–1266.Google Scholar
 11.E Ahmed, A Eltawil, A Sabharwal, in Antennas and Propagation Society International Symposium (APSURSI), 2012 IEEE. Simultaneous transmit and sense for cognitive radios using fullduplex: A first study, IEEE, Memphis, USA, (2012), pp. 1–2.Google Scholar
 12.JI Choi, SK Hong, M Jain, S Katti, P Levis, J Mehlman, in Signals, Systems and Computers (ASILOMAR), 2012 Conference Record of the Forty Sixth Asilomar Conference On. Beyond full duplex wireless, IEEE, Pacific Grove, California, USA, (2012), pp. 40–44.Google Scholar
 13.E Tsakalaki, ON Alrabadi, A Tatomirescu, E De Carvalho, GF Pedersen, Concurrent communication and sensing in cognitive radio devices: challenges and an enabling solution. Antennas Propag. IEEE Trans. 62(3), 1125–1137 (2014).CrossRefGoogle Scholar
 14.D Treeumnuk, DC Popescu, in Communications (ICC), 2012 IEEE International Conference On. Energy detector with adaptive sensing window for improved spectrum utilization in dynamic cognitive radio systems, IEEE, Ottawa, Canada, (2012), pp. 1528–1532.Google Scholar
 15.TS Shehata, M ElTanany, in Information Theory, 2009. CWIT 2009. 11th Canadian Workshop On. A novel adaptive structure of the energy detector applied to cognitive radio networks, IEEE, Ottawa, Canada, (2009), pp. 95–98.Google Scholar
 16.YC Liang, Y Zeng, EC Peh, AT Hoang, Sensingthroughput tradeoff for cognitive radio networks. Wireless Commun. IEEE Trans. 7(4), 1326–1337 (2008).CrossRefGoogle Scholar
 17.DR Joshi, DC Popescu, O Dobre, et al, Gradientbased threshold adaptation for energy detector in cognitive radio systems. Commun. Lett. IEEE. 15(1), 19–21 (2011).CrossRefGoogle Scholar
 18.JI Choi, M Jain, K Srinivasan, P Levis, S Katti, in Proceedings of the Sixteenth Annual International Conference on Mobile Computing and Networking. Achieving single channel, full duplex wireless communication, ACM, New York, USA (ACMNew York, USA, 2010), pp. 1–12.CrossRefGoogle Scholar
 19.TT Ha, Theory and Design of Digital Communication Systems (ACM, New York, 2010).CrossRefGoogle Scholar
 20.Y Lu, D Wang, M Fattouche, in Personal, Indoor, and Mobile Radio Communication (PIMRC), 2014 IEEE 25th Annual International Symposium On. Novel spectrum sensing scheme in cognitive radio by simultaneously sensing/transmitting at fullduplex tx and ber measurements at rx, IEEE, Pittsburg, USA, (2014), pp. 638–642.Google Scholar
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