Performance Analysis for AWGN and Rayleigh Fading Channel under Different AND & OR Fusion Rules

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Performance Analysis for AWGN and Rayleigh Fading Channel under Different AND & OR Fusion Rules

Abdullah Al Zubaer*1, Sabrina Ferdous*1, Rohani Amrin*2, Md. Romzan Ali*2, Md. Ariful Islam*2

*1Lecturer, Department of Computer Science and Engineering, Rabindra Maitree University, Kushtia, Bangladesh.

*1Lecturer, Department of Computer Science and Engineering, Rabindra Maitree University, Kushtia, Bangladesh.

*2Lecturer, Department of Information and Communication Technology, Rabindra Maitree University, Kushtia, Bangladesh.

*2Lecturer, Department of Electrical and Electronic Engineering, Rabindra Maitree University, Kushtia, Bangladesh.

*2Lecturer, Department of Electrical and Electronic Engineering, Rabindra Maitree University, Kushtia, Bangladesh.

Abstract: Cognitive radio which is a buzzword in radio technology world. The main features of this CK having low cost communication, restrict the interference of the unlicensed users, able to fill voids in the wireless spectrum, increase the spectral efficiency, able to make final decision about primary user weather present or not. The advantage is that this technology allows the CR users to co-operate by sharing their informations for detecting the primary users. However, the accurate detection factor will be compromised if the users experience deep shadowing or fading effect. Hard decision (OR rule and AND-rule) is also performed at fusion centre (FC) in CR. The main motive of this project is to investigate performance of co-operative spectrum sensing scheme which is considered as the key technology in CR. Finally, this study will also show how to promote sensing performance in AWGN and Rayleigh fading channels. The performance of this CR is evaluated in terms of the probability of Pmd and the probability of Pfa. Then, the report is compared between the theoretical value and simulated result. After that it describes the relationship between (SNR) and the detections. At last, the method, energy detection and simulation and result are discussed.

Keywords: Fusion rule, ROC curve, AWGN channel, Rayleigh fading, Complementary ROC, Simulation.

  1. INTRODUCTION

    Modern years show an extraordinary demand for wireless communications. Due to extending the field of wireless communication, this results in more spectrum resources being required to achieve our obligations. The allocation of the spectrum usually fixed for organization and Today's spectrum management is regulated by our government. However, this leads to a common problem with spectrum wastage [1]. Some licensed bands and organizations are found spectrum scarcity. The purpose of Cognitive radio has been proposed as a means to overcome spectrum scarcity and proper spectrum utilization in wireless communication. According to the Federal Communication Commission (FCC), many licensed users that are not properly utilized, remain indolent. The FCC also exposes that spectrum utilization hardly ever crosses 35% at any given time in the large populated urban areas [2]. The main concept behind the "Cognitive Radio (CR)" is to ensure the proper utilization of those underutilized spectrums without interfering with the licensed primary user (PU). The CR system allows the unlicensed users, also called secondary users (SUs), to use the temporarily unused spectrum that is not currently used by the licensed primary user (PU).

    Spectrum sensing is a fundamental component of the CR system. The spectrum sensing enables the ability to sense the presence of PU and the parameters related to the radio channel. There are several spectrum sensing techniques that have been used to determine the existence of PU, such as feature detection, energy detection and matched filtering detection. The energy detection technique is widely used because it does not require any prior knowledge about PU.As there are some fundamental characteristics of wireless channels, such as multipath fading, shadowing, and noise uncertainty, a CR system may unable to detect the presence of the PU. To address this problem, the cooperative spectrum has been proposed [3], with the collaborative decisions of several secondary users (SU) for spectrum sensing [4], and this is done in a fusion center (FC). The FC receives signals from unlicensed SUs and combines these signals to get the final decision about the presence of PU.

    There are, at present, three fusion rules to determine the availability of the free spectrum, they are: the AND-rule, OR-rule, and MAJORITY-rule. Among this three rules, energy detection gives improved sensing performance when using the OR-fusion rule.

  2. METHODOLOGY

    Let N denote the number of users sensing the PU. Each CR user makes its own decision regarding whether the primary user present or not, and forwards the binary decision (1 or 0) to fusion center (FC) for data fusion. The PU is located far away from all CRs. All the CR users receive the primary signal with same local mean signal power, i.e. all CRs form a cluster with distance between any two CRs negligible compared to the distance from the PU to a CR. For simplicity we have assumed that the noise, fading statistics and average SNR are the same for each CR user. We consider that the channels between CRs and FC are ideal channels (noiseless) [5].

    Assuming independent decisions, the fusion problem where k out of N CR users are needed for decision can be described by binomial distribution based on Bernoulli trials where each trial represents the decision process of each CR user. With a hard

    decision counting rule, the fusion center implements an nout-of-M rule that decides on the signal present hypothesis whenever at least k out of the N CR user decisions indicate H1 . Assuming uncorrelated decisions, the probability of detection at the fusion center is given by

    d ,i

    d ,i

    N N l

    N l

    Pd l

    Pd ,i

    1 P

    (1)

    l k

    Where Pd ,i is the probability of detection for each individual CR user?

    1. Logical AND-Rule

      In this rule, if all of the local decisions sent to the decision maker are one, the final decision made by the decision maker is one [6]. The fusion centers decision is calculated by logic AND of the received hard decision statistics. Cooperative detection performance with this fusion rule can be evaluated by setting k=N in eq. (4.1).

      Pd , AND

      N

      P

      P

      d ,i

      (2)

    2. Logical OR-Rule

      d ,i

      d ,i

      In this rule, if any one of the local decisions sent to the decision maker is a logical one, the final decision made by the decision maker is one [7]. Cooperative detection performance with this fusion rule can be evaluated by setting k=1 in eq.

      Pd ,OR

      1 1 P N

      (3)

    3. OR fusion rule

    d ,i

    d ,i

    In this rule, if any one of the local decisions sent to the decision maker is a logical one, the final decision made by the decision maker is one. Cooperative detection performance with this fusion rule can be obtained as [9],

    Pd ,OR

    1 1 P N

    (4)

    Input

    Squaring

    2

    2

    Device Integrator

    1

    1

    t

    t

    dr

    dr

    Y

    t

    t

    T

    T

    1 2

    Noise Pre-filter

    Noise Pre-filter

    yt

    T t T

    T t T

    Figure-1: Energy Detection

    y

    t T

    r dr

  3. DISCUSSION AND SIMULATION RESULTS

    Networks of cooperative energy detectors in the AWGN and Rayleigh fading channels are consideed where AND & OR fusion rule are simulated. For the hard decision, we present in following figures the Complementary ROC curves of the AND and the OR rule, and compare it to the detection performance of a single CR user. For the simulations, we consider 4 CR users.

    Figure-2: Complementary ROC under AWGN Channel for AND & OR Fusion Rule (SNR=10dB, u=5)

    Figure-2 shows complementary ROC under AWGN channel for AND &OR fusion rule where signal to noise ratio 10dB and time bandwidth product u =5.

    Figure-3: Complementary ROC under Rayleigh Fading Channel for AND & OR Fusion Rule (SNR=10dB, u=5)

    Figure-3 shows complementary ROC under Rayleigh fading channel for AND & OR fusion rule signal to noise ratio 10dB and time bandwidth product u =5.AND rule has lower probability of false alarm, but also has lower probability of detection ( Pd ). On the other hand, OR rule has high probability of detection ( Pd ) but also has high probability of false alarm. Now, OR and

    AND rule are studied. We define

    Pfa

    values from 0.01 to 1 with increasing 0.1. The information of local detection from each

    cognitive radio users are forwarded to data fusion centre and combined to obtain final decision. The simulation is performed by using probability of detection as a metric at different SNR values are shown in Figure-4, figure 5.6.4, Figure-6 and Figure-7. We assume 4 CR users are collaborated to detect primary user signal.

    Figure-4: Complementary ROC under AWGN Channel for AND Fusion Rule (CR=4, u=5)

    Figure-4 shows complementary ROC under AWGN Channel for AND Fusion Rule where Cognitive Radio User 4are considered for simulation.

    Figure-5: Complementary ROC under AWGN Channel for OR Fusion (CR=4, u=5)

    Figure-5 shows complementary ROC under AWGN Channel for OR Fusion (CR=4, u=5) are considered for simulation.

    Figure-6: Complementary ROC under Rayleigh Fading Channel for AND Fusion Rule (CR=4, u=5)

    Figure-6 shows Complementary ROC under Rayleigh Fading Channel for AND Fusion Rule (CR=4, u=5) are considered for simulation.

    Figure-7: Complementary ROC under Rayleigh Fading Channel for OR Fusion Rule (CR=4, u=5)

    Figure-7 shows Complementary ROC under Rayleigh Fading Channel for OR Fusion Rule (CR=4, u=5) are considered for simulation.

    Figure-4, Figure-5, Figure-6 and Figure-7 describes the probability of missed detection by employing AND and OR rule. As shown in the figure that OR rule has better probability of detection than AND rule.

    From Figure-4 to 7, it is clear that as probability of false alarm ( Pfa ) is low, OR fusion rule has better probability of detection ( Pd ) than AND fusion rule. As probability of false alarm ( Pfa ) increases OR and AND fusion rules show similar results. At SNR values less than 10dB OR fusion rule exhibits better detection performance than AND fusion rule.

    As shown in following figures, the OR rule has better detection performance than the AND rule, which provides slightly better

    performance at low Pfa than the OR, because the data fusion center decide in favor of H1 when at least one CR user detects

    the PU signal. However in the AND rule, to decide of the presence of a primary user, all CR users must detect the PU signal.

    From the figures, the OR fusion rule shows a better performance compared to AND fusion rules. This is attributed to the fact that OR decision fusion rule involves result of a minimum of a single user out of K energy detector nodes to declare the availability or presence of a PU. Though AND fusion rule indicates a slightly better performance at low Pfa , as compared to the OR rule, as seen from the figure. Since the OR combining rule minimizes communication overhead – attributed to its property of

    sending a minimum of a single decision to the FC, this fusion rule will be adopted in the rest of the analysis for cooperative

    users in the various channel models under consideration.

  4. CONCLUSION

We have studied the spectrum sensing performance in cooperative and non cooperative environment in both non fading AWGN channel and Rayleigh fading channel. We have used energy detection method to detect the PU. We also used OR fusion rule here. We have seen that the energy detection performs better in the non-fading channel over fading channel in non cooperative

environment. We also observed that, when CSS was considered, sensing performance improved over a Rayleigh fading channel compared to AWGN channel.

REFERENCES

  1. Federal Communications Commission, November 2002, "Spectrum policy task force report. ET Docket 02- 135.

  2. FCC, ET. "Docket No 03-222 Notice of proposed rulemaking and order." (2003): 1-21.

  3. Cabric, Danijela, Shridhar Mubaraq Mishra, and Robert W. Brodersen, 2004, "Implementation issues in spectrum sensing for cognitive radios." Signals, systems and computers, 2004. Conference record of the thirty-eighth Asilomar conference on. Vol. 1. IEEE.

  4. Lu, et al. "Ten years of research in spectrum sensing and sharing in cognitive radio." EURASIP Journal on Wireless Communications and Networking 2012.1 (2012): 28.

  5. Urkowitz, Harry. "Energy detection of unknown deterministic signals." Proceedings of the IEEE 55.4 (1967): 523-531.

  6. Hossain, Mohammad Alamgir, Md Shamim Hossain, and Md Ibrahim Abdullah, 2012 "Cooperative spectrum sensing over fading channel in cognitive radio." International Journal of Innovation and Applied Studies 1.1: 84-93.

  7. Digham, Fadel F., Mohamed-Slim Alouini, and Marvin K. Simon. 2007, "On the energy detection of unknown signals over fading channels." IEEE transactions on communications 55.1: 21-24.

  8. P. Verma, B. Singh (2427 September, 2014), Throughput analysis in cognitive radio networks, International Conference on Advances in Computing, Communications and Informatics (ICACCI), New Delhi, India.

  9. Z. Shi, K. The (2013), Energy-efficient joint design of sensing and transmission durations for protection of primary user in cognitive radio systems, IEEE Communications Letters, Volume 17, Issue 3, pp. 565568, DOI: 10.1109/LCOMM.2013.012313.122442.

  10. C. Sun, W. Zhang, K. B. Letaief (1115 March, 2007), Cooperative spectrum sensing for cognitive radios under bandwidth constraints, Wireless Communications and Networking Conference, Kowloon, China.

Abdullah Al Zubaer*1

AUTHORS PROFILE

Md. Romzan Ali*2

Lecturer,

Department of Computer Science and Engineering Rabindra Maitree University,

Kushtia, Bangladesh.

Sabrina Ferdous*1

Lecturer,

Department of Computer Science and Engineering Rabindra Maitree University,

Kushtia, Bangladesh.

Rohani Amrin*2

Lecturer,

Department of Information and Communication Technology

Rabindra Maitree University, Kushtia, Bangladesh.

Lecturer,

Department of Electrical and Electronic Engineering

Rabindra Maitree University, Kushtia, Bangladesh.

Md. Ariful Islam*2

Lecturer,

Department of Electrical and Electronic Engineering Rabindra Maitree University,

Kushtia, Bangladesh.

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