Spectrum Sensing of OFDM Signals Utilizing Higher Order Statistics under Noise Uncertainty Environments in Cognitive Radio Systems
الموضوعات :MOUSUMI HAQUE 1 , Tetsuya Shimamura 2
1 - Department of Information and Communication Engineering, University of Rajshahi, Bangladesh
2 - Graduate School of Science and Engineering, Saitama University, Japan
الکلمات المفتاحية: Spectrum Sensing, Orthogonal Frequency Division Multiplexing, Skewness, Kurtosis, Cognitive Radio,
ملخص المقالة :
Cognitive radio (CR) is an important issue to solve the spectrum scarcity problem for modern and forthcoming wireless communication systems. Spectrum sensing is the ability of the CR systems to sense the primary user signal to detect an ideal portion of the radio spectrum. Spectrum sensing is mandatory to solve the spectrum scarcity problem and the interference problem of the primary user. Noise uncertainty consideration for orthogonal frequency division multiplexing (OFDM) transmitted signals in severe noise environments is a challenging issue for measuring the performance of spectrum sensing. This paper proposed a method using higher order statistics (HOS) functions including skewness and kurtosis for improving the sensing performance of a cyclic prefix (CP) based OFDM transmitted signal for noise uncertainty. The detection performance of OFDM systems is measured for various CP sizes using a higher order digital modulation technique over a multipath Rayleigh fading channel for low signal-to-noise ratio (SNR) cases. In the proposed method, the CP-based OFDM transmitted signal sensing performance is measured and compared with the conventional methods under noise uncertainty environments. Through comprehensive evaluation of simulation, it is demonstrated that the sensing performance of this method significantly outperforms conventional schemes in the case of noise uncertainty in severe noise environments.
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http://jist.acecr.org ISSN 2322-1437 / EISSN:2345-2773 |
Journal of Information Systems and Telecommunication
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Mousumi Haque1*,2, Tetsuya Shimamura2
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1.Department of Information and Communication Engineering, University of Rajshahi, Bangladesh. 2.Graduate School of Science and Engineering, Saitama University, Japan. |
Received: 03 Jun 2022/ Revised: 12 Nov 2022/ Accepted: 03 Dec 2022 |
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Abstract
Cognitive radio (CR) is an important issue to solve the spectrum scarcity problem for modern and forthcoming wireless communication systems. Spectrum sensing is the ability of the CR systems to sense the primary user signal to detect an ideal portion of the radio spectrum. Spectrum sensing is mandatory to solve the spectrum scarcity problem and the interference problem of the primary user. Noise uncertainty consideration for orthogonal frequency division multiplexing (OFDM) transmitted signals in severe noise environments is a challenging issue for measuring the performance of spectrum sensing. This paper proposed a method using higher order statistics (HOS) functions including skewness and kurtosis for improving the sensing performance of a cyclic prefix (CP) based OFDM transmitted signal for noise uncertainty. The detection performance of OFDM systems is measured for various CP sizes using a higher order digital modulation technique over a multipath Rayleigh fading channel for low signal-to-noise ratio (SNR) cases. In the proposed method, the CP-based OFDM transmitted signal sensing performance is measured and compared with the conventional methods under noise uncertainty environments. Through comprehensive evaluation of simulation, it is demonstrated that the sensing performance of this method significantly outperforms conventional schemes in the case of noise uncertainty in severe noise environments.
Keywords: Spectrum Sensing; Orthogonal Frequency Division Multiplexing; Skewness; Kurtosis; Cognitive Radio.
1- Introduction
Spectrum sensing in cognitive radio (CR) systems is an important issue in the modern era. The Federal Communications Commission (FCC) reported that some radio frequency bands are heavily used by licensed systems, but there are also many radio frequency bands that are only partly occupied [1]. CR is an approach for solving the scarcity problems of the frequency spectrum [2], [3]. In CR, the radio spectrum status is identified by the spectrum sensing. In recent years, numerous spectrum sensing methods have been proposed to solve spectrum sensing problems [4–6].
The energy detection based spectrum sensing method utilizes the energy of the received primary signal [7–9]. The performance of the energy detection method is not very poor for low signal-to-noise ratio (SNR) cases. Cyclostationary features have been used for detecting the signal for detecting the primary user [10]. When the primary user signal is used for sensing, matched filter detection provides the best sensing performance [11], [12]. However, spectrum sensing in the presence of noise uncertainty is not considered, and the computational complexity is very high.
The correlation based spectrum-sensing methods are very popular due to their low computational complexity and provide good performance over the fading channel [13]. The autocorrelation based spectrum sensing is classified into this category. The time domain autocorrelation property of a cyclic prefix (CP) based orthogonal frequency division multiplexing (OFDM) primary user signal was used for spectrum sensing [14–16]. The spectrum sensing of an OFDM signal under noise uncertainty conditions for low SNR cases is challenging. Conventional autocorrelation based methods utilize the knowledge of the CP for spectrum sensing [14], [15]. However, in practice, this is very difficult in the real cases. In addition, in [16] CP unknown case was considered. The noise uncertainty is not considered and the detection performance of OFDM transmitted signals is unsatisfactory in severe noise environments.
Higher order statistics are useful in digital signal processing, communication systems, signal detection, and a variety of other applications [17–20]. The higher order statistics, including third order statistics and fourth order statistics, are utilized for sensing OFDM transmitted signals, where the noise uncertainty cases are not considered for OFDM sensing [21]. Although some recent works considered spectrum sensing in noise uncertainty environments, the sensing performance is not very good in severe noise environments [22], [23]. However, the sensing of OFDM signals in noise uncertainty environments for low SNR cases is very important [23]. For these reasons, the major limitations of the existing spectrum-sensing methods result in their poor spectrum sensing performance for low SNR cases in noise uncertainty environments. Therefore, we were motivated for sensing OFDM transmitted signals in the case of noise uncertainty in severe noise environments.
The proposed method utilizes higher order statistics for sensing OFDM signals in noise uncertainty environments. The skewness calculation is utilized for sensing OFDM systems in noise uncertainty environments. Moreover, the kurtosis function is used for further sensing performance improvement of OFDM signals under noise uncertainty in severe noise environments. In the proposed method, the detection performance is evaluated for various CP sizes of OFDM systems under 64-QAM over multipath fading channels with additive white Gaussian noise (AWGN) in noise uncertainty environments. The proposed spectrum sensing method is compared with conventional methods [22], [23] over multipath fading channels under the effect of noise uncertainty. When the noise uncertainty effect is taken into account, the performance of OFDM detection improves dramatically when using skewness based spectrum sensing in severe noise environments. Furthermore, the sensing capability of OFDM transmitted signals increases markedly by utilizing kurtosis based spectrum sensing for low SNR cases under noise uncertainty.
The major contributions of this paper are as follows:
• Firstly, the skewness calculation is used for spectrum sensing under the effect of noise uncertainty. Furthermore, the proposed method is compared for skewness based sensing with and without noise uncertainty cases.
• Secondly, the proposed method is investigated for kurtosis function based spectrum sensing under two scenarios (with and without noise uncertainties).
• Thirdly, we have compared our proposed method with conventional methods [22], [23] in noise uncertainty environments. Simulation results demonstrated that our proposed skewness and kurtosis based spectrum sensing methods significantly improve the sensing performance.
The rest of the paper is arranged as follows. Section 2 presents a brief overview of the related work in spectrum sensing. The problem formulation of spectrum sensing is provided in Section 3. Section 4 represents the methodology of spectrum sensing. The transmitted OFDM signal is used as a primary user in the proposed method which is discussed in Section 5. The detailed explanation of the proposed method is presented in Section 6. Section 7 describes the performance evaluation of the proposed method by simulation results. Finally, the conclusion of this paper is drawn in Section 8. The notation employed in this paper is summarized in Table 1.
2- Related Work
Spectrum sensing detects the primary user’s transmitted signals in the CR systems. In the modern era, spectrum sensing for OFDM signals is very important issues in CR systems [24], [25]. The spectrum sensing method for OFDM signals under consideration of noise uncertainty is a severe challenging issue. Several spectrum sensing methods considering noise uncertainty have been proposed and investigated in recent years. Energy detection is a very popular technique that is used for spectrum sensing considering the noise uncertainty [26]. The spectrum sensing performance was very good for low SNR cases. However, OFDM systems were not considered, and only the AWGN channel was considered in this proposed method. In addition, recently, single and double threshold energy detection algorithms were implemented in [27], and a Frequency domain Goodness of Fit Test (FGoF) based spectrum sensing method was proposed to detect primary user signals [28]. The primary user detection performance was not very good in severe noise environments considering noise uncertainty and OFDM detection was not considered in those methods.
An Improved Energy Detection (IED) algorithm was proposed for spectrum sensing with an experimental hardware setup [29]. However, in low SNR environments, spectrum sensing performance degrades significantly due to noise uncertainty. In the presence of noise uncertainty, an adaptive double threshold based spectrum sensing method was recently proposed [22]. The sensing performance for low SNR cases was not very satisfactory using this method. In that case, the OFDM primary user transmitted signal was not considered. Effective energy detection was proposed in [23] to overcome the problem of OFDM signal detection considering noise uncertainty cases. However, the OFDM transmitted signal performance is not very good for large values of noise uncertainty in severe noise environments. In this paper, the proposed method improves the detection performance of the OFDM transmitted signal under both noise uncertainty and low SNR cases.
3- Problem Formulation of Spectrum Sensing
The principle of spectrum sensing is where the primary user transmitter sends data to the primary user receiver in their allotted licensed radio frequency spectrum band, while a pair of secondary users also intend to access the spectrum at the same instant. Spectrum sensing should be performed to detect the presence of the primary user receiver present within the coverage of the secondary user transmitter to protect the primary user transmission.
Fig. 1: Spectrum sensing principle [30].
Fig. 1 shows the principle of spectrum sensing [30]. Noise uncertainty is a very important challenge for the spectrum sensing methods. In a practical scenario, determining the noise power is very difficult. It needs to be estimated that it may contain calibration errors due to changes in thermal noise. Therefore, it is necessary to have more sensitive spectrum sensing under noise uncertainty environments in the practical situation.
In this paper, the primary user transmitted signal is an OFDM signal received at the spectrum sensing receiver. The spectrum sensing of OFDM signals is very important for modern broadcasting applications. Furthermore, spectrum sensing for OFDM signals in severe noise environments under consideration of noise uncertainty cases is challenging for modern and forthcoming wireless communication systems.
4- Methodology of Spectrum Sensing
In the proposed spectrum sensing method, there are two hypotheses that are defined by two states such as idle state, , and active state, , of the primary user in the spectrum sensing model. The secondary user’s receiver evaluates a test statistic, , based on its observed signal and compares it with a specific threshold, , to decide the situation between the two hypotheses.
The two hypotheses are given by
(1)
(2)
The spectrum sensing performance can be characterized using some important parameters. The probability of detection, , indicates the primary user correctly detects its active mode [12] as
(3)
Table 1: Notation used in this paper.
Parameter | Definition | |
| Probability of detection | |
| Probability of false alarm | |
| Probability of miss detection | |
| Idle state | |
| Active state | |
| Test statistics | |
| Test statistics using skewness | |
| Test statistics using kurtosis | |
| Threshold | |
| Noise variance | |
Ω | Noise Uncertainty | |
| CP Size | |
s | Number of OFDM Symbols | |
| Skewness function | |
| Kurtosis function |
Parameters | Types | |
OFDM System | WLAN | |
Digital Modulation | 64-QAM | |
FFT Size | 64 | |
Number of OFDM Symbols | 150 | |
Noise Uncertainty | 0.5 dB and 1 dB | |
CP Size | 1/4, 1/8, 1/16 and 1/32 | |
Channel | Multipath Rayleigh Fading with AWGN Channel | |
SNR Range | -35 dB to 0 dB | |
Higher order statistics | Skewness and Kurtosis | |
Number of Iterations | 1500 |
Spectrum Sensing Method | SNR (dB) |
|
Proposed (Skewness) | -10 | 1 |
Proposed (Kurtosis) | -26 | 1 |
Adaptive Double Threshold [22] | -5 | 1 |
Energy Detection [23] | -4 | 1 |