Efficiency Improvement of Cooperative Spectrum Detection in Cognitive Radio System [email protected] In this paper we presents a study of hard combining and decision fusion schemes for cooperative cognitive radio to solve the problem of inefficient use of radio frequency spectrum to attack the upcoming spectrum crunch issue the CR detection efficiency . Simulation comparison between hard combining and decision fusion schemes( AND ,OR, and K / N ) for cooperative cognitive radio are achieved. The hard combination and decision fusion schemes provides a good tradeoff between the detection performance . The OR – rule gives a better spectrum detection efficiency at high values of SNR and small values of Q m d with at a different values of Q f a and adaptive threshold (?) level . Key words: Energy Detection, cognitive radio (CR), Cooperative spectrum sensing , combining data fusion , ROC, Q d , Q m d ,Q f a , and threshold (?) I – Introduction The wireless communication systems and other applications uses the frequencies in the range of 3 KHz to 300 GHz ( Radio Spectrum) . The demand for wireless communication is increasing continuously and the radio spectrum has afinite resource .
Additionally, according to the statics of the Federal Communicatins Commission ( FCC ) , temporal and geographical variations in the utilization of assigned spectrum range from 15 to 85 percent . At the present time , it has become necessary to use the available spectrum more efficiently to upstay further growth of wireless communications..Consequently , cognitive radio is a revolutionary communication paradigm to solve the problem of inefficient use of radio frequency spectrum to attack the upcoming spectrum crunch issue . The temporally unused spectrum is referred to as a white space or spectrum hole . These frequency bands ( channels ) are assigned to specific system users called licensed users or primary users (PU) , and the assigned frequency bands are called licensed bands .
Cognitive radio users also called unlicensed or secondary users (SU), who can find unused authorized spectrum hole dynamically for its own use without causing any interference to primary users . Figure (1) : spectrum holes concept . So, primary users (PU) can be defined as the users who have the authorized license on the usage of a specific part of the spectrum. Secondary users can be defined as the users who have the conditional license and should not cause any interference to the primary users (PU) when using the idle channel. There are two major subsystems in a cognitive radio : – 1.
a cognitive unit that makes decisions based on various Inputs . 2. a flexible SDR unit whose operating software provides a range of possible operating modes . In ddition to a spectrum sensing subsystem is also often included in the architectural of cognitive radio to measure the signal environment to determine the presence of other users .
Cognitive radio technology will intelligently determine whether a certain part of the frequency spectrum is idle, or if it is being utilized. If the cognitive radio can successfully determine with a high degree of certainty that a specific part of the spectrum is being idle, it can then transmit on these frequencies without interfering with the licensed owner of the spectrum, thus achieving better spectral resource efficiency. Figure ( 2) : Mechanism of cognitive radio for efficient use of the available radio frequency spectrum The requirement of no interference is the key for developing of cognitive radio to invent fast and highly robust ways of determining whether a frequency band is available or being occupied. This is the area of spectrum sensing for cognitive radio. Spectrum sensing will be the backbone of any autonomous cognitive radio.
Therefore, more simple and reliable spectrum sensing technique is needed . Energy detector retrofit simplicity and serves as apractical spectrum sensing technique . To improve the spectrum sensing for cognitive radio network (CRN), cooperative spectrum sensing methodology is proposed to withstand some spectrum sensing drawbacks such as fading, shadowing, and receiver uncertainty problems . The aim of cooperation spectrum sensing is to improve the cognitive radio detection performance by taking advantage of the spatial diversity, in order to protect the primary user( PU ) against the interference , and reduce the probability of false alarm to get an efficient utilization of the spectrum holes . Figure (3):Cooperative spectrum Sensing showing drawbacks (Multi-path and Shadowing , receiver uncertainty )In a fading environment , the spectrum detection is braved by uncertainty due to the fading of the channel i.e.
the secondary user need to differentiate between a white space, where there is no primary signal, and a deep fade where it is detect the primary signal. Similar difficulties arises in the case of shadowing. To make a treatment for these issues, many different secondary users can cooperate with each other to detect the presence of primary user or signal . The advantage of diversity gain accomplished through cognitive users cooperation helps to improve fading and shadowing effects . Cooperative detection also helps in improving the detection performance . The CR users ( Receivers ) can measure signal properties and can even estimate what the CR system (Transmitter) meant to send , but it also should be able to tell the transmitter about how to change its waveform in ways that will suppress interference .
In other words , the cognitive radio users ( receivers ) needs to convert this information into a transmitted message to send back to the CR system ( Transmitter ) . Cognitive Radio Main Functions : – 1 – Spectrum Sensing : – This is the main function in CR to enable cognitive radio users (CRs) to detect the spectrum white space and occupy the vacant spectrum band and improve overall spectrum efficiency . 2 – Spectrum Management : – CR decide on the best spectrum band and the channels within the available bands to meet the QoS requirements over all available spectrum channels i.e It captures the best available vacant spectrum holes from detected spectrum holes. 3 -Spectrum Mobility : – Cognitive radio networks aim to use the spectrum dynamically by allocating the radio terminals ( CR users ) to operate in the greatest available frequency channels i.
e cognitive radio users are considered as guests on the spectrum. So , if a particular spectrum band is desired to assign for the primary user, the communication has to be continued in another idle spectrum band 4 -Spectrum Sharing : – Providing an efficient and fair dynamic spectrum allocation schemes to distribute the primary spectrum holes to the competitive secondary users . Cognitive Radio Cycle model : – The cognitive radio cycle process can be achieved through three steps : – 1 – Spectrum Sensing . 2 – Spectrum analysis .
3 – Spectrum Decision . Figure (4) : Cognitive Radio Cycle Model . Cognitive radio is one of the most promising solutions to spectrum-scarcity problem. In cognitive radio networks, the most crucial activity is the spectrum efficiency (SE) and energy efficiency (EE) In cognitive radio , the SU senses the spectrum to check the presence or absence of the PU signal depending on sensing parameters like signal-to-noise ratio (SNR), bandwidth, bit error probability, spectral efficiency, throughput, and spectral efficiency are beer in mind and studied. II – Spectrum Sensing Techniques There are several popular and top performing spectrum sensing algorithms for cognitive radio under low signal to noise ratio scenario, namely : – 1 – Cyclo-stationary Feature Detector ( CFD ) .
2 – Energy Detector ( ED ) . 3 – Matched Filter Detector ( MFD ) . The accuracy of sensing is call requirement to accurate sensing which provides successful access operation of CRN. For known condition, Energy Detection (symbolically expressed as ED) technique is the simplest way of sensing. The strength of received signal energy is based on threshold value of ED.
For greater energy of received signal compared to threshold value assumes the presence of a user, otherwise the physical channel is free. Spectrum sensing is to differentiate between two hypotheses , The primary user is present, hypothesis ( H1 ) . The primary user is absent, hypothesis ( H0 ) . = , = Where is the signal received by secondary user , is the primary user’s transmitted signal, is the additive white Gaussian noise (AWGN) , and is the amplitude gain of the Channel between the PU and the kth CR user. III – System Model Suppose a cognitive radio network, with number of cognitive users (K ) indexed by k = 1, 2. .
. K . Suppose each CR performs local spectrum sensing autonoumously by using N samples of the received signal and all cooperative CR users send their sensing results (m1, m2… mK) via the control channel. Consequently , the FC fuses the received local sensing information to make a final decision about the presence or absence of the PU . Here we express the SU received signal process in the form of and the ongoing SU received signal can be formulated as spu (t) is the PU transmitted signal, ssu (t) represents the leakage from the SU transmitted signal , hpu is the PU channel gain while hsu represents the SU leakage signal gain, and t is the time. 1 2An energy detector is employed in each SU to determine the state of the PU. The ED output statistic in each SU is given as , Where S is the number of averaged samples .
The ED output for both the hypotheses can be expressed as , In this paper, we will study three different hard combining and decision rules for CSS ( OR , AND , and K / N rules) , thus deducing the effects on the Detection Efficiency under certain conditions . A . AND _ Rule decides that the PU signal is present if all CR users have detected the PU signal.
The cooperative test using the AND rule can be formulated as follows : – Where K : Number of CR users The probability of detection and the probability of false alarm also formulated as follows : – Where is the final detection . The special case for the AND- – Rule corresponds to M = K = 1 – B . OR_ Rule : – The OR rule decides that the PU signal is present if any of the CR users detect the PU signal .
Hence, the cooperative test using the OR rule can be formulated as follows : – Where is the final decision ,The special case for the OR- – Rule Corresponds to the case M = 1 = 1 – ( ) C . K / N _ Rule The third rule is called majority or Voting rule that decides the presence of the PU signal if at least M of K users have detected with 1 ? M ? K , and is formulated as : – A majority decision is a special case of voting rule for M = K/2 The probability of detection Qd and probability of false alarm Qf are defined as : – = 1 – ( )D . The main parameters that controls the performance of the CR Spectrum Sensing • Signal – to – Noise Ratio ( SNR ) • Probability of Correct Detections Qd { decision , Y = H1 | H1 } Qd { decision , Y = H0 | H0 } • Probability of False Alarm : – Qfa { decision , Y = H1 | H0 } • Probability of Miss-Detection : – Q m d { decision , Y = H0 | H1 } • Total Error Rate : – Q e = Q f a + Q m d • Threshold ( ? ) . • Number of CR users . IV – Simulation and Analysis of the Results In this paper we was proposed a coop – -erative hard combining and decision rules to improve the CR detection efficiency . There are three hard combining and decision based cooperative sensing rules are used . The OR rule gives a decision H1 when at least one of CR users detects PU signal . The AND rule gives a decision H1 if all CR users send their status detection as bit – 1 as a local detection of the PU .
The K / N rule gives a decision H1 if at least half of CRs Local detection status is bit – 1 Each CR user makes its own decision with respect to the presence or absence of the PU and sends the one bit decision status (1 or 0) to the FC or cooperative groups to make data fusion. Our simulations was made for Cognitive Radio Network with cooperative Seven Secondary Users ( K = 7 SU s ) . AWGN channel also proposed for our simulations .
A SNR ranges from (-18 dB to – 6 dB , -12 dB to 0 dB , and -10 dB to 2 dB ) with Qfa = 0.01 . Also SNR ranges from (-18 dB to -6 dB , -16 dB to -4 dB , -14 dB to -2 dB , -12 dB to 0 dB , and -10 dB to 2 dB ) with We are using QPSK modulation for test with modulation index m = 6 , with a number of simulations n = 2000 for each value of SNR ,and Number of Samples / Signal N = 1500 . At Figure ( 7 ) : SNR ( -10 dB to 2 dB) Vs Q d , at Q f a = 0.01 0.01 . Figure ( 5 ) : SNR ( -18 dB to -6 dB) Vs Q d , at Q f a = 0.01 Figure ( 6 ) : SNR( -12 dB to 0 dB ) Vs Q d , at Q f a = 0.
01Figure ( 5 ) , Figure ( 6 ) , and Figure ( 7 ) shows the receiver operating characteristic (ROCs) curves for the hard decision fusion rules ( AND , OR , and K / N) and non-cooperative through energy detector and with a Qfa is 0.01 . The last three(ROCs) curves shows that the probability of detection increases as the SNR increased . Also the (ROCs) curves shows that the “OR” rule detection performance is the best one for spectrum detection than the other hard decision fusion rules. Also, the majority or “HV” hard decision fusion rule is lower in the detection efficiency than the ” OR – Rule ” , but is better than the ” AND – Rule ” , and the ” AND – Rule ” is better in performance than non-cooperative CR users .
Consequently , we will made the same simulation at the same conditions , but with probability of false alarm Qfa is 0.1 (Qfa increased ) . Figure ( 8 ) : SNR ( -18 dB to – 6 dB) Vs Q d , at Q f a = 0.1 . Figure ( 10 ) : SNR ( -10 dB to 2 dB) Vs Q d , at Q f a = 0.1 It is clear from (ROC s) curves in ( figures 8 , 9 , and 10 ) that the probability of detection Qd increases by increasing the probability of False Alarm Qfa in the same time of increasing the SNR . We can note that from (ROC) curve in figure ( 5 ) with SNR range ( -18 dB to -6 dB) at the point of SNR = – 10 dB with Q f a = 0.
01 :- the Q d – OR = 83 % , Q d – K/N = 44 % , Q d – AND = 20 % , Q d – Non-cooperative = 9 % Also , We can note that from (ROC) curve in figure ( 8 ) with the same SNR range ( -18 dB to – 6 dB) as in fig. ( 5 ) at the point of SNR = – 10 dB , but with Q f a = 0.1 : – the Q d – OR = 99 % , Q d – K/N = 88 % , Q d – AND = 77 % , Q d – Non-cooperative = 68% So,it is obvouies that the Q d increases by increasing the Q f a and SNR.
Consequently the ” OR – Rule ” gives abetter efficiency of the spectrum sensing detection . Figure ( 9 ) : SNR ( -12dB to 0 dB ) Vs Q d , at Q f a = 0.1From the last results we can confirm the relation between the Q d , Q f a , and SNR and their effets on the spectrum detection efficiency . Also we know that there a relation between the Q d and Q m d as follows : – Q m d = 1 – Q d Q d have a relation to Q f a , and SNR hence there should arelation between Q m d and Q f a as mentioned in eq(.) So , we will simulate the effect of the relation between Q f a , Q m d for the hard decision fusion rules ( OR , AND , and K / N ) and Non-cooperative CR network , and their effets on the spectrum detection efficiency . To simulate the relation between Q m d and Q f a . we analyze the spectrum detection efficiency under the target of the probability of miss -detection and probability of false alarm at ( K = 7 SU s ) SNR = – 10 dB , time bandwidth factor U = 100 , and probability of false alarm is used from 0.
01 to 1 by increasing 0.01 and AWGN channel considered . Figure ( 11) : Q f a Vs Q m d , at SNR = -10dB So, from Figure ( 9 ) , we can note that the OR rule gives a minimum Q m d Versus values of Q f a when compared to the other cooperative spectrum sensing techniques ( K / N or Majority rule , AND rule ) . So the OR rule is best among hard combination data fusion for cooperative spectrum sensing in Cognitive Radio and gives the better performance than ( K/N or Majority rule , AND rule). Since the aim of cognitive radio cooperative spectrum sensing is to improve the detection performance and protect the primary user( PU ) against the interference which results from the large values of Q m d , and reduce the probability of false alarm to get an efficient utilization of the spectrum holes . Consequently , we want to keep the probability of missed detections Q m d very low, so the probability of false alarms Q f a increases and this would result in low spectrum utilization.
This Parallelize a low probability of false alarms would result in high missed detection probability which increases the interference to the primary users. This trade-off has to be carefully considered. Then the threshold is set in order to achieve a constant level of false alarm to satisfy the condition of minimum Q m d . Then the threshold level (?) is raised and lowered during detection in order to maintain acceptable level of probability of false alarm Q f a . Consequently we can formulate this meaning as Adaptive Threshold – Constant False alarm Rate (AT -CFAR) detection .Figure ( 12 ) : Threshold (?) Vs Q f a Number of Samples / Signal N = 1000 , Noise only received i.
e PU – absent , Q f a = 0.01 : 0.01 : 1 It is clear that from ROC for Threshold (?) Vs Q f a The Q f a decreases as the the Threshold level (?) Increased . So , our reults satisfy the proper condition for designing a cognitive radio network with cooperative spectrum sensing with high spectrum detection with minimum interference and more efficient spectrum utilization . V – Conclusion In this paper we was presented a study and simulation of various effective cognitive radio hard combining cooperative spectrum sensing techniques and signal detection base on hard decision combining technique in data fusion centre compared with non-cooperative one . In cooperative technique, OR and AND , and majority or “HV” rules are employed and evaluate the system performance by using probability of detection (Q d) ,Q f a , Q m d , and SNR . We was proved that by simulation the probability of detection of hard combining cooperative spectrum sensing techniques better performance compared with non-cooperative one , and increases as the SNR increased . The (ROCs) curves shows that the “OR” rule detection performance is the best one for spectrum detection than the other hard decision fusion rules.
Also, the majority or “HV” hard decision fusion rule is lower in the detection efficiency than the ” OR – Rule ” , but is better than the ” AND – Rule ” , and the ” AND – Rule ” is better in performance than non-cooperative CR users . Also ,it is obvouies that the Q d increases by increasing the Q f a and SNR. Furthermore, a minimum of 7 cooperated users relatively in cognitive radio system can achieve optimal value of probability of detection. However, it depends on the threshold (?) value used in spectrum detection .
