Spectrum Sensing Methods For Cognitive Radio

Essay, Pages 11 (2656 words)

Views

ABSTRACT

The Cognitive radio (CR) is an adaptive, intelligent radio and network technology that can automatically detect available channels in a wireless spectrum and change transmission parameters enabling more communications to run concurrently and also improve radio operating behaviour. The spectrum sensing problem is one of the most challenging issues in cognitive radio systems. In this paper, various spectrum sensing methodologies for cognitive radio is presented. Various aspects of spectrum sensing problem are studied from a cognitive radio perspective and multi-dimensional spectrum sensing concept is introduced.

Don't use plagiarized sources. Get your custom essay on

“ Spectrum Sensing Methods For Cognitive Radio ”

Get custom paper

NEW! smart matching with writer

Various sensing features of some current wireless standards are given.

Index Terms—Cognitive radio, spectrum sensing, multi-dimensional spectrum sensing, radio identiﬁcation.

INTRODUCTION

Cognitive radio is a wireless communication system that utilize the spectrum efficiently. It performs various operations such as analysis of radio channel and spectrum usage and then it intelligently decides how, when and in which band to transmit. The functionalities of cognitive radio include spectrum sensing, spectrum management, spectrum mobility, power control and routing.

Now-a-days, the need for higher data rates is increasing which leads to the transition from voice-only communications to multimedia type applications. Therefore, it becomes obvious that the current static frequency allocation schemes can’t accommodate the requirements of an increasing number of higher data rate devices. As a result, innovative techniques that can offer new ways of exploiting the available spectrum are needed.

The one main aspect of cognitive radio is related to autonomously exploiting locally unused spectrum to provide new paths to spectrum access. One of the most important components of the cognitive radio concept is the ability to measure, sense, learn, and be aware of the parameters related to the radio channel characteristics, availability of spectrum and power, radio’s operating environment, user requirements and applications.

In cognitive radio terminology, primary users can be deﬁned as the users who have higher priority or legacy rights on the usage of a speciﬁc part of the spectrum. On the other hand, secondary users, which have lower priority, exploit this spectrum in such a way that they do not cause interference to primary users. Therefore, secondary users need to have cognitive radio capabilities, such as sensing the spectrum reliably to check whether it is being used by a primary user and to change the radio parameters to exploit the unused part of the spectrum.

Spectrum sensing is the task of obtaining awareness about the spectrum usage and existence of primary users in a geographical area. This awareness can be obtained by using geolocation and database, by using beacons, or by local spectrum sensing at cognitive radios. When beacons are used, the transmitted information can be occupancy of a spectrum as well as other advanced features such as channel quality. Cognitive radio is considered a more general term that involves obtaining the spectrum usage characteristics across multiple dimensions such as time, space, frequency and code. It also involves determining what types of signals are occupying the spectrum including the modulation, waveform, bandwidth and carrier frequency.

MULTI-DIMENSIONAL SPECTRUM AWARENESS

The opportunity is deﬁned as “a band of frequencies that are not being used by the primary user of that band at a particular time in a particular geographic area” and only exploits three dimensions of the spectrum space: frequency, time, and space. There are other dimensions that need to be explored further for spectrum opportunity. For example, the code dimension of the spectrum space has not been explored well in the literature. Therefore, the conventional spectrum sensing algorithms do not know how to deal with signals that use spread spectrum, time or frequency hopping codes. If the code dimension is interpreted as part of the spectrum space, this problem can be avoided and new opportunities for spectrum usage can be created. Naturally, this brings about new challenges for detection and estimation of this new opportunity. Similarly, the angle dimension has not been exploited well enough for spectrum opportunity. It is assumed that the primary users and the secondary users transmit in all the directions. However, with the recent advances in multi-antenna technologies, e.g. beamforming, multiple users can be multiplexed into the same channel at the same time in the same geographical area. In other words, an additional dimension of spectral space can be created as opportunity. This new dimension also creates new opportunities for spectral estimation where not only the frequency spectrum but also the angle of arrivals needs to be estimated. The angle dimension is different than geographical space dimension. In angle dimension, a primary and a secondary user can be in the same geographical area and share the same channel. However, geographical space dimension refers to physical separation of radios in distance.

With these new dimensions, sensing only the frequency spectrum usage falls short. The radio space with the introduced dimensions can be deﬁned as “a theoretical hyperspace occupied by radio signals, which has dimensions of location, angle of arrival, frequency, time, and possibly others”. This hyperspace is called electro space, transmission hyperspace, radio spectrum space, or simply spectrum space by various authors, and it can be used to describe how the radio environment can be shared among multiple (primary or secondary) systems. It is of crucial importance to deﬁne an n-dimensional space for spectrum sensing. Spectrum sensing should include the process of identifying occupancy in all dimensions of the spectrum space and ﬁnding spectrum holes, or more precisely spectrum space holes. For example a certain frequency can be occupied for a given time, but it might be empty in another time. Hence, temporal dimension is as important as frequency dimension.

SPECTRUM SENSING METHODS FOR COGNITIVE RADIO

A number of different methods are proposed for identifying the presence of signal transmissions. In some approaches, characteristics of the identiﬁed transmission are detected for deciding the signal transmission as well as identifying the signal type. In this section, some of the most common spectrum sensing techniques in the cognitive radio literature are explained.

A. Energy Detector Based Sensing

Energy detector-based approach, also known as radiometry or periodogram, is the most common way of spectrum sensing because of its low computational and implementation complexities. It is more generic as receivers do not need any knowledge on the primary users’ signal. The signal is detected by comparing the output of the energy detector with a threshold which depends on the noise ﬂoor. Some of the challenges with energy detector-based sensing include selection of the threshold for detecting primary users, inability to differentiate interference from primary users and noise and poor performance under low signal-to-noise ratio (SNR) value. Moreover, energy detectors do not work efﬁciently for detecting spread spectrum signals.

B. Waveform-Based Sensing

Known patterns are usually utilized in wireless systems to assist synchronization or for other purposes. Such patterns include preambles, midambles, regularly transmitted pilot patterns, spreading sequences etc. A preamble is a known sequence transmitted before each burst and a midamble is transmitted in the middle of a burst or slot. In the presence of a known pattern, sensing can be performed by correlating the received signal with a known copy of itself. This method is only applicable to systems with known signal patterns, and it is termed as waveform-based sensing or coherent sensing.

C. Cyclostationarity-Based Sensing

Cyclostationarity feature detection is a method for detecting primary user transmissions by exploiting the cyclostationarity features of the received signals. Cyclostationary features are caused by the periodicity in the signal or in its statistics like mean and autocorrelation or they can be intentionally induced to assist spectrum sensing. Instead of power spectral density, cyclic correlation function is used for detecting signals present in a given spectrum. The cyclostationarity based detection algorithms can differentiate noise from primary users’ signals. This is a result of the fact that noise is wide-sense stationary with no correlation while modulated signals are cyclostationary with spectral correlation due to the redundancy of signal periodicities. Furthermore, cyclostationarity can be used for distinguishing among different types of transmissions and primary users.

D. Radio Identiﬁcation Based Sensing

A complete knowledge about the spectrum characteristics can be obtained by identifying the transmission technologies used by primary users. Such an identiﬁcation enables cognitive radio with a higher dimensional knowledge as well as providing higher accuracy. For example, assume that a primary user’s technology is identiﬁed as a Bluetooth signal. Cognitive radio can use this information for extracting some useful information in space dimension as the range of Bluetooth signal is known to be around 10 meters. Furthermore, cognitive radio may want to communicate with the identiﬁed communication systems in some applications.

In radio identiﬁcation based sensing, several features are extracted from the received signal and they are used for selecting the most probable primary user technology by employing various classiﬁcation methods. The features obtained by energy detector-based methods are used for classiﬁcation. These features include amount of energy detected and its distribution across the spectrum. Channel bandwidth is found to be the most discriminating parameter among others. For classiﬁcation, radial basis function (RBF) neural network is employed. These two features are fed to a Bayesian classiﬁer for determining the active primary user and for identifying spectrum opportunities. The standard deviation of the instantaneous frequency and the maximum duration of a signal are extracted using time-frequency analysis and neural networks are used for identiﬁcation of active transmissions using these features.

E. Matched-Filtering

Matched-ﬁltering is known as the optimum method for detection of primary users when the transmitted signal is known. The main advantage of matched ﬁltering is the short time to achieve a certain probability of false alarm or probability of miss detection as compared to other methods. In fact, the required number of samples grows as O(1/SNR) for a target probability of false alarm at low SNRs for matched ﬁltering. However, matched-ﬁltering requires cognitive radio to demodulate received signals. Hence, it requires perfect knowledge of the primary users signalling features such as bandwidth, operating frequency, modulation type and order, pulse shaping, and frame format. Moreover, since cognitive radio needs receivers for all signal types, the implementation complexity of sensing unit is impractically large. Another disadvantage of match ﬁltering is large power consumption as various receiver algorithms need to be executed for detection.

SPECTRUM SENSING IN CURRENT WIRELESS STANDARDS

Recently developed wireless standards have started to include cognitive features. Even though it is difﬁcult to expect a wireless standard that is based on wideband spectrum sensing and opportunistic exploitation of the spectrum, the trend is in this direction. In this section, wireless technologies that require some sort of spectrum sensing for adaptation or for dynamic frequency access (DFA) are discussed. However, the spectrum knowledge can be used to initiate advanced receiver algorithms as well as adaptive interference cancellation.

A. IEEE 802.11k

A proposed extension to IEEE 802.11 speciﬁcation is IEEE 802.11k which deﬁnes several types of measurements. Some of the measurements include channel load report, noise histogram report and station statistic report. The noise histogram report provides methods to measure interference levels that display all non-802.11 energy on a channel as received by the subscriber unit. AP collects channel information from each mobile unit and makes its own measurements. This data is then used by the AP to regulate access to a given channel. The sensing information is used to improve the trafﬁc distribution within a network as well. WLAN devices usually connect to the AP that has the strongest signal level. Sometimes, such an arrangement might not be optimum and can cause overloading on one AP and underutilization of others. In 802.11k, when an AP with the strongest signal power is loaded to its full capacity, new subscriber units are assigned to one of the underutilized APs. Despite the fact that the received signal level is weaker, the overall system throughput is better thanks to more efﬁcient utilization of network resources.

B. Bluetooth

A new feature, namely adaptive frequency hopping (AFH), is introduced to the Bluetooth standard to reduce interference between wireless technologies sharing the 2.4GHz unlicensed radio spectrum. In this band, IEEE 802.11b/g devices, cordless telephones, and microwave ovens use the same wireless frequencies as Bluetooth. AFH identiﬁes the transmissions in the industrial, scientiﬁc and medical (ISM) band and avoids their frequencies. Hence, narrow-band interference can be avoided and better bit error rate (BER) performance can be achieved as well as reducing the transmit power. By employing AFH, collisions with WLAN signals are avoided in this example. AFH requires a sensing algorithm for determining whether there are other devices present in the ISM band and whether or not to avoid them. The sensing algorithm is based on statistics gathered to determine which channels are occupied and which channels are empty. Channel statistics can be packet-error rate, BER, received signal strength indicator (RSSI), carrier to-interference-plus-noise ratio (CINR) or other metrics. The statistics are used to classify channels as good, bad, or unknown.

C. IEEE 802.22

IEEE 802.22 standard is known as cognitive radio standard because of the cognitive features it contains. The standard is still in the development stage. One of the most distinctive features of the IEEE 802.22 standard is its spectrum sensing requirement. IEEE 802.22 based wireless regional area network (WRAN) devices sense TV channels and identify transmission opportunities. The functional requirements of the standard require at least 90% probability of detection and at most 10% probability of false alarm for TV signals with 116dBm power level or above. The sensing is envisioned to be based on two stages: fast and ﬁne sensing. In the fast sensing stage, a coarse sensing algorithm is employed, e.g. energy detector. The ﬁne sensing stage is initiated based on the fast sensing results. Fine sensing involves a more detailed sensing where more powerful methods are used. Several techniques that have been proposed and included in the draft standard include energy detection, waveform-based sensing cyclostationary feature detection, and matched ﬁltering. A base station (BS) can distribute the sensing load among subscriber stations (SSs).The results are returned to the BS which uses these results for managing the transmissions.

Another approach for managing the spectrum in IEEE 802.22 devices is based on a centralized method for available spectrum discovery. The BSs would be equipped with a global positioning system (GPS) receiver which would allow its position to be reported. The location information would then be used to obtain the information about available TV channels through a central server. For low-power devices operating in the TV bands, e.g. wireless microphone and wireless camera, external sensing is proposed as an alternative technique. These devices periodically transmit beacons with a higher power level. These beacons are monitored by IEEE 802.22 devices to detect the presence of such low-power devices which are otherwise difﬁcult to detect due to the low-power transmission.

CONCLUSIONS

Spectrum is a very valuable resource in wireless communication systems, and it has been a focal point for research and development efforts over the last several decades. Cognitive radio, which is one of the efforts to utilize the available spectrum more efﬁciently through opportunistic spectrum usage, has become an exciting and promising concept. One of the important elements of cognitive radio is sensing the available spectrum opportunities. In this paper, the spectrum opportunity and spectrum sensing concepts are re-evaluated by considering different dimensions of the spectrum space. The new interpretation of spectrum space creates new opportunities and challenges for spectrum sensing while solving some of the traditional problems. Various aspects of the spectrum sensing task are explained in detail. Several sensing methods are studied and collaborative sensing is considered as a solution to some common problems in spectrum sensing. Pro-active approaches are given and sensing methods employed in current wireless systems are discussed. Estimation of spectrum usage in multiple dimensions including time, frequency, space, angle, and code; identifying opportunities in these dimensions; and developing algorithms for prediction into the future using past information can be considered as some of the open research areas.