Saturday , August 18 2018

Analyzing the Performance of Spectrum Sensing Algorithms for IEEE 802.11 af Standard in Cognitive Radio Network*

Tanuja S. DHOPE (SHENDKAR)
Faculty of Electrical Engineering and Computing, University of Zagreb
Croatia

Dina SIMUNIC
Faculty of Electrical Engineering and Computing, University of Zagreb
Croatia

Antun KERNER
Ericsson-Nikola Tesla
Zagreb, Croatia

Abstract: In the entire world the wireless communication systems are represented by 2G and 3G systems with all stages of evolution towards 4G systems. The complexity of wireless networks requires a careful design, especially related to bandwidth and energy efficiency. Bandwidth efficiency is very important parameter, because it relates to frequency spectrum, which is a natural limited resource. The cognitive radio has been proposed as the future technology to meet the ever increasing demand of the radio spectrum by allocating the spectrum dynamically to allow unlicensed access on non-interfering basis. The digital dividend of 700MHz band (mainly used by TV broadcast services) opens the door for cognitive radio applications due to its excellent propagation characteristics compared to GSM 1800 MHz, 2.1 GHz or 2.5 GHz bands. In cognitive radio, spectrum sensing is the fundamental problem. In this paper we are analyzing the performance of spectrum sensing algorithms; energy detection and covariance absolute value utilizing TV white space for IEEE 802.11 af standard.

Keywords: Energy detection, covariance based detection, fading channels, spectrum sensing, cognitive radio, IEEE 802.11af.

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CITE THIS PAPER AS:
Tanuja S. DHOPE (SHENDKAR), Dina SIMUNIC, Antun KERNER, Analyzing the Performance of Spectrum Sensing Algorithms for IEEE 802.11 af Standard in Cognitive Radio Network*, Studies in Informatics and Control, ISSN 1220-1766, vol. 21 (1), pp. 93-100, 2012.

Introduction

Although spectrum is seen as a scarce natural resource, measurements show that often there are moments in time and space of the non-utilized spectrum by the allocated services. Therefore, it can be said that the spectrum is being used inefficiently [1], thus demanding dynamic allocation of spectrum instead of static [2][3]. Recently, there have been growing interests in cognitive radio, where secondary opportunistic radio exploits opportunistically spectrum left-overs- or so-called “White Spaces”, by means of knowledge of the environment and cognition capability, and adapts their radio parameters accordingly, [2][3][4][5]. The digital dividend of 700MHz band (mainly used by TV broadcast services) opens the door for cognitive radio applications due to its excellent propagation characteristics compared to GSM 1800 MHz, 2.1 GHz or 2.5 GHz bands. The upcoming IEEE 802.11af standard (known as super-Wi-Fi) is based on cognitive radio (CR) [6], utilizing the bandwidth within TV broadcast stations [7] [8] [9] [10].

CR makes opportunistic use of the spectrum by allowing unlicensed or secondary user (SU) to reuse the spectrum whenever the licensed or primary user (PU) is inactive. The SUs are required to perform the frequent spectrum sensing for detecting the presence of PUs with a high probability of detection and vacant the channel or reduce transmit power. For IEEE 802.11af standard, time to vacate band after PUs detection is 2s with 90% of probability of detection (Pd) and 10% of probability of false alarm (Pfa) at Signal-to-Noise ratio (SNR) level as low as -20dB along with geo-location accuracy of +/-50m [6][9][10]. Sensing a spectrum is a crucial task in CR. There have been several sensing methods, including the likelihood ratio test (LRT) [11], energy detection (ED) method [11] [12] [13], matched filtering (MF)-based method [11] and cyclostationary detection method [11], each of which has different requirements and advantages or disadvantages. Although LRT is proven to be optimal, it is very difficult to use, because it requires exact channel information and distributions of the source signal and noise. To use LRT for detection, we need to obtain the channels and signal and noise distributions first, which are practically intractable. The MF-based method requires perfect knowledge of the channel responses from the primary user to the receiver and accurate synchronization (otherwise, its performance will dramatically be reduced).

Energy detection method is a semi-blind detection which requires knowledge of noise power only for signal detection. In [12], sets of receiver operating characteristic (ROC) curves are drawn for several time-bandwidth products for spectrum sensing using energy detection but not specified exactly which type of signal is used. In [11], Blindly combined energy detection method which uses the spatial correlation of received signals based on energy detection, is analysed for independent and identically distributed source signal which is FM modulated wireless signal operated in vacant TV channels with a bandwidth less than 200 kHz, the flat-fading and the multipath fading channel. The experimental study for spectrum sensing in 2.4 GHz ISM band over 85 MHz of bandwidth using energy detection for sine wave carrier and QPSK sensing is evaluated in [14]. In the same reference minimum detectable signal levels set by the receiver noise uncertainties are measured. The cyclostationary detection method needs to know the cyclic frequencies of the primary users, which may not be realistic for many spectrum reuse applications. Furthermore, this method demands excessive analogue-to-digital (A/D) converter requirements and signal processing capabilities [11]. In the references: [11] [15] [16], a new spectrum sensing algorithm based on covariance of the received signal called as Covariance Absolute Value (CAV) is proposed. CAV is a blind detection method, uses space-time signal correlation for signal detection which does not require any knowledge of noise and signal power. The covariances of signal and noise are generally different, which can be used in detection of PU.

But CAV method is very sensitive to signal correlation.

In [13], a new method called as ‘Hybrid Detection method’ is proposed which takes the advantage of ED and CAV by utilizing ED method in low correlation and CAV method in high correlation. Further the simulation results for hybrid detection method is studied in detail in [13] and [17].The hybrid detection method shows the better performance compared to energy detection and covariance method.

In [15] [16], the performance of ED and CAV is evaluated for Advanced Television Systems Committee (ATSC) of 6MHz bandwidth for the narrow-band signal of a wireless microphone, by analysis of multiple antennas. In [15], ROC curve is analyzed for wireless microphone signal for outdoor environment with Rician channel, further Pd versus the smoothing factor is studied for a wireless microphone signal. Also in [15] Pd versus different values of SNRs are studied for ATSC signal with single and multiple antennas which is concluded by the effect of time variant channel on the detection of ATSC signal. But in [15]-[16] the ROC curve and the effect of time-variant Rayleigh channel using ED and CAV spectrum sensing algorithms on detection of ATSC signal has not been studied. Further CAV method for DVB-T standard for 8 MHz bandwidth has not been evaluated.

Since cognitive radio is an emerging technology, the performance of the spectrum sensing algorithm needs to be analyzed under conditions of different fading channels.

The goal of this paper is to present an analysis of energy detection and covariance based detection for DVB-T standard for 8MHz bandwidth in 2k mode used for mobile reception of standard television. This paper focuses on analyzing two algorithms under conditions of AWGN channel, Rayleigh and Rician fast and slow time variant fading channels using time-variant Jakes’ model based on probability of detection under varying SNR conditions, Pd versus Pfa for different SNRs, Pd versus smoothing factor, Pd versus overall correlation coefficient and receiver operating characteristic (ROC) curve for DVB-T standard.

The organization of the paper is as follows. The overview of ED and CAV is made in section 2. Simulation is performed in section 3 for terrestrial digital video broadcasting based on the two methods, followed by conclusions in section 4.

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https://doi.org/10.24846/v21i1y201211

* A shorter version of this paper has been submitted to the 35th Jubilee International Convention MIPRO 2012, May 2012, under the title “Hybrid Detection Method for Spectrum Sensing in Cognitive Radio”[17] and further a modified version of this paper has also been submitted to the same scientific event under the title “Performance Analysis of Covariance Based Detection in Cognitive Radio”[19].