Wireless Communication Has Been The Fastest Computer Science Essay

Published Date: 02 Nov 2017

1. Introduction

Wireless communication has been the fastest growing segment of the communications industry in the last decade. As a result, wireless systems have become ubiquitous with several applications (e.g., cellular telephony and wireless internet) and various devices (e.g., mobiles, laptops, and tablets). In addition, new applications like wireless sensor networks, automated factories, smart home appliances, remote telemedicine, and many more are emerging in the present generation. With the enormous growth in the number of various wireless systems and services, the availability of the better quality wireless spectrum has become severely limited.

In the present generation, the radio spectrum supports various areas that includes broadband mobile telecommunications, marine and aeronautical communications, medical and scientific research. Thus radio services and various communications are more important to social and economic development. Hence the spectrum is a scarce resource and it is very hard to find the spectrum for any new type of applications. Research is done in various countries that shows most of the radio frequency spectrum is utilized inefficiently. Utilization of the assigned licensed spectrum varies between 15% and 85%. Therefore the main problem is not the spectrum scarcity but the inefficient usage of the spectrum. Thus a new technology is required to enable better utilization of unused licensed spectrum.

Figure shows Spectrum utilization

Cognitive radio:

Cognitive Radio is one form of the wireless communication in which the transceiver detect the signal and its geographic location intelligently, whether communication channels are in use or not and simultaneously move into the channels that are vacant. To utilize the radio spectrum efficiently, Cognitive radio is considered as an emerging technology towards future wireless communications.

Figure shows Cognitive radio network

Cognitive radio has given as a solution to overcome the problem of the spectrum underutilization Mitola & Maguire (1999), Haykin (2005). The main idea under cognitive radio systems is to allow unlicensed users or cognitive users (those who have not paid for utilizing the electromagnetic spectrum) to transmit within a licensed band. In order to achieve this, cognitive users monitor continuously the spectrum and then detect a suitable frequency band that allows them to:

Transmit with or without the required minimum amount of interference to the licensed or primary users.

Achieve minimum QoS required for their specific application.

Share the spectrum with other unlicensed users or cognitive users.

[18317.pdf doc]

1.1.1 Definitions:

In general, Cognitive radio is the emerging next generation technology and has different meanings.

The term cognitive radio has been first introduced by Mitola(1999) as "an intelligent radio which is aware of its surrounding environment and capable of changing its behaviour to optimize the user experience".

Therefore a cognitive radio has three important characteristics: awareness, cognition, and adaptability.

Awareness is the ability of the radio to sense the signal, measure and be aware of its internal states and environment. A radio may exhibit different levels of awareness such as spectrum awareness, location awareness, user awareness, and network awareness, etc.

Cognition is the ability to process information, learn about the environment, and make decisions about its operating behaviour to achieve predefined objectives.

Adaptability is the capability of varying the operating parameters without modifying the hardware components. This type of capability enables this technology to adapt to the dynamic radio type of environment. [7]

The most important application in the cognitive radio technology is the Dynamic Spectrum access that overcomes the spectrum scarcity problem that was caused by the spectrum allocation and the underutilization of the spectral resources.

According to Haykin(2005), it can be defined as: "Cognitive Radio is defined as an intelligent wireless communication system that is aware of its surrounding environment and uses the methodology to learn from the environment and adapt its internal states to statistical variations in the incoming RF stimuli by making corresponding changes in certain operating parameters (e.g. transmit power, carrier frequency, and modulation strategy) in real time with two primary objectives in mind: One: highly reliable communication whenever and wherever needed, Second: efficient utilization of the radio spectrum".[1]

Cognitive radio offers a novel solution to overcome the underutilization problem by allowing an opportunistic access to the spectrum resources. According to Federal Communications Commission (FCC), it can be defined as "Cognitive Radio: a radio or system that senses its operational electromagnetic environment and can dynamically and autonomously adjust its radio operating parameters to modify interference, facilitate interoperability, and access secondary markets."[1]

1.1.2 Applications

Cognitive radio has more revolutionary applications apart from dynamic spectrum access. For example, cognitive radio may facilitate location services, seamless mobility, optimum performance, and coexistence of heterogeneous wireless systems.

Cognitive radio may provide location services by helping the user locate services like restaurants, car rental, train, flights, etc., when the person travels in a new country.3)

Cognitive radio may facilitate seamless mobility by automatically detecting and inter operating with different networks like WLAN, wireless metropolitan area network (WMAN), Bluetooth, etc.

Cognitive radio may be useful in obtaining optimum performance by optimizing spectrum usage, data rates, service cost, battery power minimization, etc.,. Coexistence of heterogeneous wireless systems in the same frequency bands (e.g., IEEE 802.15.4 Zigbee and IEEE 802.11 WLAN) results in severe interference caused by different power levels, asynchronous time slots, and incompatible MAC and physical layer protocols. This interference in turn severely degrades the performance of the coexisting wireless systems.

Cognitive radio can provide solutions to reduce the interference among the coexisting heterogeneous wireless systems and improve their performance.

1.1.3 Enabling technologies

Many technologies and practical considerations, which are highly multidisciplinary, need to come together to result in the cognitive technologies. There are a few enabling technologies that play an important role in cognitive radio systems: sensors, software technologies, and software defined radio.

Sensors are needed to create awareness about the environment. Some examples of sensors are RF receiver, microphone, camera, biometric scanners (fingerprint, iris, retina), global positioning system (GPS). Sensors such as microphone, camera, and biometric scanners can be used for user awareness, which is helpful in avoiding unauthorized access and providing user centric experience in a multiuser scenario. GPS enables several useful applications for a cognitive radio by providing the location awareness.

Software technologies, which are enabling cognitive radio, include policy engine, machine learning, and advanced signal processing, and networking protocols. The spectrum usage is regulated by the regulatory body and regulation policies may vary depending on country, time, software, and hardware developers. Policy engine helps in adhering to different regulations by having a library of policies in the form of downloadable software. Machine learning focuses on automatically learning and making intelligent decisions based on the available information. Examples of machine learning approaches are artificial neural networks, reinforcement learning, and genetic algorithms. Advanced signal processing approaches are required in cognitive radios for communications (e.g., modulation/demodulation, forward error correction, channel estimation, equalization, filtering) and sensor signal processing (e.g., spectrum analysis, feature extraction, pattern recognition, wavelet synthesis). Networking protocols enable cooperation between different SUs which has the potential of increasing the cognitive radio capability. Moreover they may help SUs to coexist with the PUs and the other SUs.

A software defined radio or SDR, is one type of radio communication system where the components such as amplifiers, filters, mixers, and detectors were implemented in the software using DSP (Digital Signal Processing). Hence modifying the program that was done in the software can change functionality of the cognitive radio. Therefore such flexible radio functionality allows the use of different wireless communication techniques in a single portable device making SDR a key enabling technology for cognitive radios. Some examples of commercially available SDR are Universal Software Radio Peripheral (USRP), USRP2, and FLEX-5000A.

1.1.4 Cognitive radio cycle:

Figure shows a simplified cognitive radio cycle.

Cognitive radio mainly describes four functional blocks. They are

Spectrum sensing- This determines the availability of the spectrum and the presence or absence of the Primary users (also known as licensed users).

Spectrum management- This is to analyse the time period about how well these spectrum holes are available for use to the secondary users (also called unlicensed users or cognitive radio users).

Spectrum sharing – Spectrum holes get distributed among these unlicensed users by considering usage cost.

Spectrum mobility- During the transition to better spectrum, this helps to obtain better requirements of the seamless communication.

By considering all these four functional blocks, Spectrum sensing plays an important role in establishing cognitive radio system.

In cognitive radio technology, a primary user (PU) is defined as a licensed user who has higher rights on particular part of radio spectrum. Various examples of licensed technology are global system for mobile communications (GSM), worldwide interoperability for microwave access (WiMax), and long term evolution (LTE). Secondary users (SU) are unlicensed cognitive users with lower priority. So whenever the primary user is not utilizing any spectrum, then the secondary user can be able to access the spectral resources.

However the Secondary User has to vacate the frequency band as soon as the PU becomes active so that negligible interference is caused to the PU. Such opportunistic access of the PU resources by the SUs is called as dynamic spectrum access.

A SU can utilize opportunistically different spectrum holes corresponding to different PUs in order to satisfy its bandwidth requirement without causing any type of interference to the PUs as shown in Fig. 1.2.

Figure Secondary users access the spectrum opportunistically that is not used by the licensed users.

Spectrum sensing is an important issue to access the spectrum dynamically in cognitive radio technology. It is the task of observing the entire radio spectrum as well as identifying idle spectrum. It enables the Secondary Users to explore and exploit the unused PU spectrum. In addition to this, it is so crucial for adjusting the level of interference that caused to the Primary Users of the radio spectrum.

2. Aims and Objectives:

The main aim of this project is to examine the implementation of Singular Value Decomposition (SVD)- based approach in order to sense and detect whether the primary signal is present or not. SVD based signal detection is more efficient in sensing the signal without having any information about the properties of the transmitted signal. Also sensing Performance of SVD when compared with other techniques is simulated with the help of MATLAB.

The Objectives are

To achieve high probability of detection and low probability of false alarm with minimum information about the primary user signals.

To obtain better accuracy and lower complexity.

And also to find maximum Eigen value and minimum Eigen value from the received signal matrix. Also determine a Threshold value and compare with the ratio of maximum to minimum Eigen value in order to detect if the signal is present or not.

To increase the capacity and speed if the transmission data.

To improve the spectrum efficiency.

3. Background Research:

In CR network, the spectrum sensing play a major role to realize its potential of spectrum utilization in real environment since the sensed information is used to decide the operations of Cognitive Radio users. CR users perform the sensing of spectrum, and the sensed information is analysed to make better decision in accurate manner.

The main objective of new technologies is to increase the speed and capacity of the transmission data. Indeed, some important features and high performances are required:

Broadband hardware is required to use different spectrum bands and channels at the same time.

In order to develop various kinds of signals and modulations, flexible software is required.

To achieve these requirements, the concept of Cognitive radio was introduced. Sensing the spectrum became an important functionality for the detection of spectrum holes and to use frequency bands without causing any interference to the licensed users or primary users.

Implementation of a signal detector is a functional block having both system interface as well as user interface as shown in below figure. With the first interface, the user interacts with the detection method so as to set some parameters in order to change detection performances. Through the second interface, the radio system provides some data stream to the detection method and gives the required information to the user about the transmission conditions.

Figure shows Signal detector interfaces

The detection result obtained from the user interface passes to the radio system in order to enable Secondary user transmission.

Moreover the detection result is showed to the user for two reasons. The user can use the result for others applications; with the environment information and the detection result, the user can give new instructions to the system to improve the transmission performances. Therefore the core of the Signal Detector is the detection method. This algorithm should be flexible accurate and fast in execution, so the objective is to make a good trade-off between computational complexity and knowledge a-priori of the signals under test.

Important performance parameters which can be used to evaluate the sensing algorithms:

• False alarm probability: It is defined as the probability that the detector declares the presence of Primary User, when the PU is actually absent. False alarm probability is also called Type I error. If there are too many false alarms, the spectrum opportunities may be overlooked resulting in an inefficient spectrum reuse. Therefore controlling the false alarm probability is crucial for efficient spectrum usage.

• Missed detection probability: It is defined as the probability that the detector declares the absence of PU, when the PU is actually present. Missed detection probability is also called Type II error. Too many missed detections may lead to collisions of the PU and SU transmissions causing interference to the PU. Therefore controlling the missed detection probability is crucial for keeping the interference to the PU under the permissible limits. It should be noted that establishing distributions of decision statistics helps in controlling the probabilities of missed detection and false alarm.

• Sensing time: If the receiver chain is time-duplexed for reception and sensing, it is desirable that the sensing durations are shorter and the data transmission durations are longer. If the sensing time is too long, the data transmission duration reduces thereby reducing the throughput of the SUs.

• SNR: The SNR of the received PU signal at the sensor depends on the PU transmitted power and the propagation environment. The two error probabilities (Type I and II) are linked to each other through sensing time, SNR, and detection threshold. The detection performance improves with an increase in the SNR.

• Detection range: It is the maximum distance between the sensor and the PU such that the detector should detect the PU reliably. Detection range depends on the detection performance of the detector, SNR at the receiver, sensing time and propagation environment. Spectrum sensing schemes should detect the PU signal reliably in low SNR regime as the PU receivers which are far away from the transmitter should not be interfered with. At the same time, the sensor should not be too sensitive to detect the PU signals with extremely low SNR values and well outside its interference range.

• Complexity and implementation issues: It is desirable to have simple and implementable sensing algorithms which are also energy efficient. Therefore estimating the hardware cost and energy efficiency through computational complexity of the algorithm is also important.

• Requirement on prior knowledge of PU parameters and noise distribution: More information about the PU and the noise distribution is known, the better the expected detector performance. For example, the PU signal may be deterministic or random. Similarly, we may have very specific information on statistical properties of noise (e.g., zero-mean complex white Gaussian noise with a known variance), or the knowledge of noise may be very vague (e.g., the noise distribution may be symmetric and unimodal.

• Detecting different PU waveforms: Ability to detect different PU waveforms is a desirable property as ideally one will want a single detector which can reliably detect all kinds of PU signals. Some detectors can detect many different PU signal types whereas some detectors are tuned for a specific waveform of a specific PU signal and cannot be used for other waveforms. For example, energy detector can be used to detect all kinds of PU waveforms.

4. Literature Review

A considerable amount of literature has been published on cognitive radio and dynamic spectrum access. Many studies such as [20] and [37] introduce important research issues related to cognitive radio. Since spectrum sensing is the _rst component of cognitive radio, much research has been conducted to evaluate and compare the performance of signal detection techniques in order to _nd the best candidate technique that meets spectrum

sensing requirements. In [29, 28, 38, 27, 8], several spectrum sensing techniques are sur-

veyed and compared. According to these studies, energy detection method is the most

common detection technique because of its simplicity. However, it cannot detect signals

with low SNR.

Xuping and Jianguo have studied the e_ects of noise uncertainty and fading on the

performance of ED [18]. They show that ED is not reliable for detecting low power signals,

and it is not robust against deep fading and shadowing. Consequently, they propose a

distributed cooperative spectrum sensing that improves detection reliability. An experi-

mental study of an adaptive energy detection model is presented in [39]. The proposed

model adapts the window size to detect narrow signals. In [17], Ye et al. introduce an SS

model based on ED that estimates the noise power to be used to set the threshold.

Cabric et al. [31] conduct an experimental study to evaluate the energy detector and

matched _lter. Their results show that energy detection is vulnerable to noise uncertainty

while matched _lter is vulnerable to frequency o_set. In their study, they propose collabo-

rative detection method based on ED to enhance detection reliability. In [40], Bhargavi and

Murthy evaluate and compare the energy detector, matched _lter and two cyclostation-

ary feature detectors, based on spectral correlation density (SCD) and magnitude squared

coherence (MSC). The results and their analysis show that MSC cyclostationary feature

detection outperforms the other techniques in low SNR environments

[detection 1 /pdf doc]

4.1 Spectrum hole concept:

Since most of the spectrum is already assigned, the most important challenge is to share the licensed spectrum without interfering with the transmission of other licensed users as shown in Figure 6. The cognitive radio enables the usage of temporally unused spectrum, which is referred to as spectrum hole or white space. If this band is further used by a licensed user, the cognitive radio moves to another spectrum hole or stays in the same band, altering its transmission power level or modulation technique to avoid interference as shown in Figure 6

Figure shows the spectrum hole concept

CR is designed to identify the spectrum holes in the licensed spectrum bands. A spectrum hole is defined as a licensed spectrum band that can be used by CR users without interfering the primary or licensed users.

Generally spectrum holes can be broadly divided into two categories: temporal spectrum holes and spatial spectrum holes, which are shown in Fig. 7(a) and 7(b), respectively.

Figure spectrum holes for secondary communication a) temporal spectrum hole b) spatial spectrum hole

A temporal spectrum hole means that there is no primary transmission over the spectrum band of interest during the time of sensing (over a reasonable period). Hence, this band can be utilized by CR users in the current time slot. For the temporal spectrum holes, as indicated in Fig. 7(a), the secondary users are located in the coverage area of the primary transmission. Consequently, it is relatively easy to detect the presence or absence of the primary user activity since CR users only need to have a similar detection sensitivity as regular primary receivers and, more importantly, identifying the presence of a primary signal is much easier than demodulating and decoding it. Therefore, spectrum sensing in this case does not pose demand on signal processing.

A spatial spectrum hole exists when the spectrum band of interest is occupied by the primary transmission only in a restricted area; hence, this band can be utilized by CR users well outside this area. In contrast with the utilization of temporal spectrum holes, secondary users utilizing spatial spectrum holes work outside the coverage of the primary transmission, as indicated in Fig. 7(b). Since there are no primary receivers outside the coverage area, secondary communication over the licensed band is allowed if only the secondary transmitter does not interfere with the primary transmission and reception within the coverage area. To accomplish this, the secondary transmitter has to successfully detect the presence of the primary signal at any location where the secondary transmission may cause intolerable interference to the possible nearby primary receiver.[1]

Frequency spectrum holes are defined as a frequency band in which a secondary user can transmit without interfering with any primary receivers across all frequencies.

It is possible to identify three different types of spectrum holes in space, which are defined as the following:

Black spaces are occupied by high-power local interferers most of the time.

Grey spaces are partially occupied by low-power interferers.

White spaces are free of radio interferences (excluding ambient noise), which is generated by natural and artificial forms of noise. The artificial form of noise can be generated as broadband thermal noise, transient reactions or impulsive noise.

4.2 Spectrum sensing:

In CR network, the spectrum sensing play a major role to realize its full potential of spectrum utilization in real environment since the sensed information is used to decide the operations of CR users. CR users perform the sensing of spectrum, and the sensed information is analyzed to make wise decision in timely and accurate manner. The main requirements of spectrum sensing is shown in the figure

Figure shows the spectrum sensing requirements

The spectrum sensing function enables the cognitive radio to adapt to its environment by detecting spectrum holes. The most efficient way to detect spectrum holes is to detect the primary users that are receiving data within the communication range of a cognitive radio user. In real time, however, it is difficult for a cognitive radio to have a direct measurement of a channel between a primary receiver and a transmitter. Thus, the most recent work focuses on primary transmitter detection based on local observations of cognitive radio users. Generally, the spectrum sensing techniques can be classified as transmitter detection, cooperative detection, and interference-based detection.

Figure spectrum sensing structure of cognitive radio network

Each CR user individually performs spectrum sensing. In the centralized systems, all the sensing results are reported to the central CR device that makes a decision on the presence of a primary user. BS is the coordinating central CR device, which selects CRs to perform sensing and makes the final decision. PU is a primary user. CR, also named sensor, is a secondary user when performs collaborative sensing. All CR users and a BS forming a circular cell are deployed randomly in a range.

Hence, spectrum sensing must be made in with respect to time, frequency, space, code and angle of transmission to detect unutilized frequencies.

4.2.1 Issues and Challenges in spectrum sensing:

In the cognitive radio network, the most fundamental and problematic functionality is to design an efficient spectrum sensing technique. These Spectrum sensing techniques differ in various parameters such as accuracy, computational cost, complexity, reliability and speed. Further it is very difficult for these sensing approaches to achieve better performance level with respect to all these spectrum sensing parameters. Hence a trade-off is required to achieve overall sensing results. Some of the challenges that make spectrum sensing a challenging task are:[7]

4.2.1.1 Channel Uncertainty

In wireless communication networks, uncertainties in received signal strength arises due to channel fading or shadowing which may wrongly interpret that the primary system is located out of the secondary user’s interference range as the primary signal may be experiencing a deep fade or being heavily shadowed by obstacles. Therefore, cognitive radios have to be more sensitive to distinguish a faded or shadowed primary signal from a white space. Any uncertainty in the received power of the primary signal translates into a higher detection sensitivity requirement.

Figure 3 shows the trade-off between spectrum sensing time and user throughput.[7]

Figure shows the trade-off between spectrum sensing time and user throughput

4.2.1.2. Noise Uncertainty

The detection sensitivity can be defined as the minimum SNR at which the primary signal can be accurately (e.g. with a probability of 0.99) detected by the cognitive radio and is given by the equation

Where N is the noise power, Pp is transmitted power of the primary user, D is the interference range of the secondary user, and R is maximum distance between primary transmitter and its corresponding receiver.

The above equation suggests that in order to calculate the required detection sensitivity, the noise power has to be known, which is not available in practice, and needs to be estimated by the receiver. However the noise power estimation is limited by calibration errors as well as changes in thermal noise caused by temperature variations. Since a cognitive radio may not satisfy the sensitivity requirement due to an underestimate of N, ymin should be calculated with the worst case noise assumption, thereby necessitating a more sensitive detector.[7]

4.2.1.3. Aggregate Interference Uncertainty

In future, due to the widespread deployment of secondary systems, there will be increased possibility of multiple cognitive radio networks operating over the same licensed band. As a result, spectrum sensing will be affected by uncertainty in aggregate interference (e.g. due to the unknown number of secondary systems and their locations). Though, a primary system is out of interference range of a secondary system, the aggregate interference may lead to wrong detection. This uncertainty creates a need for more sensitive detector, as a secondary system may harmfully interfere with primary system located beyond its interference range, and hence it should be able to detect them.[7]

4.2.1.4. Sensing Interference Limit

Primary goal of spectrum sensing is to detect the spectrum status i.e. whether it is idle or occupied, so that it can be accessed by an unlicensed user. The challenge lies in the interference measurement at the licensed receiver caused by transmissions from unlicensed users. First, an unlicensed user may not know exactly the location of the licensed receiver which is required to compute interference caused due to its transmission. Second, if a licensed receiver is a passive device, the transmitter may not be aware of the receiver. So these factors need attention while calculating the sensing interference limit.[7]

4.3 Spectrum Sensing Algorithms

4.3.1Matched Filter

A Matched Filter is a linear filter and for a given input signal it is designed in such a way that it maximizes the output Signal to Noise ratio. When the information about the primary user signal is known to the cognitive radio user (secondary user), the optimal method of detection in the presence of stationary Gaussian noise is the matched filter since it maximizes the received signal to noise ratio (SNR).

Block diagram of matched filter detection:

Sample at t = T

Matched Filter

Threshold detection

Received Signal

Y Decision

Figure shows the block diagram of matched filter detection

Matched filtering requires the cognitive radio in order to demodulate the received signals. Therefore, it requires information of the primary users signalling characteristics such as modulation type, frame structure, operating frequency, bandwidth, and pulse shaping, etc.

So if the information of the primary user signal is not correct, then the matched filter detection performs inaccurate results. However, since all the wireless networks systems have preambles, synchronization word, pilot, these can be used for coherent detection.

The operation of the matched filter is same as the correlation in which unknown signal gets convolved with the filter whose impulse response is the mirror and time shift in the reference signal.

Matched Filter detection operation is expressed as:

Where ‘x’ is unknown signal and ‘h’ is the impulse response of the matched filter which

is matched to the reference signal in order to maximize signal to noise ratio.

Based on the threshold value, the received signal can detect the primary user signal. If the signal level is more than threshold, then it detects the signal.

Advantages: In order to achieve given missed detection and false alarm probabilities, sensing time is relatively very low when compared with other techniques. And also since the matched filter detector is a linear filter, it is easy to implement. In order to achieve high processing gain, it requires less detection time.

Disadvantages:

This detection requires complete information of every primary user signal. If the characteristics of the primary user signal are not accurate, then this method performs poor detection.

Implementation of the sensing unit is impractically very complex because this cognitive radio needs receivers for all the signal types.

This type of detection can be used to detect only one type of Primary user signal since it is specific to a particular Primary user signal. And since coherent detection is done, so perfect synchronization is required.

Because of the frequency selective channels and synchronization errors, the received signal gets distorted thereby reducing the performance of matched filter detection.

The main disadvantage is it requires high power consumption as various receiver algorithms need to be executed for the detection.

4.3.2Energy detection

Another technique for detecting the primary user signal is energy detector based spectrum sensing. If the receiver does not provide sufficient information about the primary user signal like if the power of the random Gaussian noise is only known to the receiver, then the energy detector is the optimal way for spectrum sensing.

In this method, signal detection is done by comparing with the energy of the signal with a threshold value.

This threshold value depends on the noise floor and can be estimated based upon it.

By measuring the energy of the received signals in a specific band, this method of detection detects Primary user signals.

This method requires less information regarding signal bandwidth and carrier frequency.

Digital Implementation of Energy detector can be done in two different methods.

Energy detector using Welch Periodogram Method:

Block Diagram:

Figure Energy detector using Welch Periodogram Method

Algorithm Steps:

In this method of approach, the input signal selects the required bandwidth by Band pass filter and then it is sampled.

This sampled signal is converted from analog to digital and then Fast Fourier Transform (FFT) is applied.

The output of the Fast Fourier Transform process is squared and averaged it to get test statistics.

Test statistic is given by

According to this test statistic of the energy detector and by comparing it with threshold value, presence or absence of the primary user (PU) signal in a particular band can be detected.

Based on the channel conditions, the threshold value can set either to fixed or variable. If the size of FFT increases, then frequency resolution gets improved so that it is easy to detect the narrowband signals. And also if the averaging time decreases, it improves the SNR so that Noise power gets reduced.

Energy detector with Analog Pre Filter and Square Law Device:

Block diagram:

Figure Implementation of energy detector with analog pre filter and square law device

Algorithm Steps:

In this approach as shown in figure 13, the signal is pre-filtered before passing to A/D.

After converting from analog to digital, then the signal is passed to square law device so that it gets squared and averaged to get the energy of the signal.

And then it is compared with threshold value to detect the presence or absence of primary user signal.

Advantages: Since it has low computational and execution complexity, this method of detection is considered as most common way of signal detection. Unlike in matched filters and other techniques, the received signal in this detection does not require any kind of prior information of the primary user signals. Its implementation is also simple.

Disadvantages:

Sensing time is very high in order to achieve given probability of detection.

Detection performance gets affected by the uncertainty of the noise power.

This method of detection cannot be used to differentiate primary signals from the Cognitive radio. So Cognitive radio users need to be synchronized and refrained from transmissions in Cooperative sensing.

It can be used to detect Spread spectrum signals.

In a fading environment, achieving better performance is highly difficult.[5][6]

4.3.3Feature detection:

This detection approach measures certain features of the Primary user signal such as

Cyclostationary feature. With this technique, noise can be differentiated from the modulated signal and hence it can detect primary user signals even with low SNR.

Block diagram:

Figure shows Cyclostationary feature detection

The modulated signals are coupled with pulse trains, hopping sequences, sine wave carriers, repeated spreading, or cyclic prefixes. This exploits the periodicity in the received signal in order to detect the presence of primary users. Because mean and autocorrelation of the modulated signals exhibit periodicity, they are considered as Cyclostationary.

At the receiver, this periodicity is introduced in the signal format so as to exploit it for certain parameter estimation like direction of arrival and carrier phase.

These features are identified by analysing a spectral correlation function. The main advantage of this spectral correlation function is that it can differentiate noise from the modulated signals. This is because noise is treated as wide-sense stationary signal with no correlation.

In this method of detection, cyclic spectral correlation function (SCF) is used for detecting the signals that are in a given frequency band. This cyclic spectral correlation function can be calculated as

Where R yy(Ʈ) is the cyclic auto correlation function, α is the cyclic frequency.

If cyclic frequency i.e., α=0, then it gives Power spectrum density (PSD) of the signal.

If the signal is in the given frequency band, this technique produces a peak in cyclic SCF that implies presence of the primary user.

If there is no peak that implies the given frequency spectrum band is idle or there are no primary users in active state at a given time and location.

Based upon this observation, Cognitive radio users detect the status of the primary users in the particular frequency band.

Advantages: Cyclostationary feature detector can perform better than the energy detector in eliminating against noise due to its robustness to the uncertainty in noise power. This technique differentiates Cognitive radio transmissions from all types of Primary user signals.

Disadvantages: This type of detection requires a high detection time and the computational

Process is so complex.

Eigenvalue – based detection

In this method of detection, the ratio of eigenvalues of the covariance matrix of the received signals is determined.

There are two different types of eigenvalue based detection method. They are maximum- minimum eigenvalue detection, energy with minimum eigenvalue detection.

Maximum- minimum eigenvalue based detection compares the ratio of maximum eigenvalue and the minimum eigenvalue with a given threshold value.

Energy with minimum eigenvalue detection compares the average energy and the minimum eigenvalue with a given threshold value.

The first approach does not require any information but second approach needs certain information like SNR value.

Hence, the maximum- minimum eigenvalue approach can overcome the uncertainty problem in the noise level.

Thus the method detects primary user signals with unknown source, channel and noise power. But there is a disadvantage regarding its complexity.

4.3.5Covariance based signal detection

This is another method for the detection of primary user signal by Cognitive radio users.

The main concept of this technique is to exploit both the signal and noise covariance since the statistical covariance of noise and signal are normally different. These properties of the covariance differentiate the noise from the signal where the covariance matrix of the received signal is calculated based on the receiving filter.

The received signal in a vector channel form can be represented as

Y= Gs + w

Where G is the channel matrix through which the signal is passed.

The covariance’s corresponding to the noise and signal can be represented as

Ry = E[yyT]

Rs = E[ssT]

Rn = E[wwT]

Where E[.] is the expected value of [.].

If s=0 that means if there is no signal then the Rs=0. Therefore the off diagonal elements of Ry are all zeros.

If s≠0 and samples of the signal are correlated then Rs is not a diagonal matrix. Therefore this method of detection identifies the presence of primary signals with the help of covariance matrix of the received signal.

That means if all the off diagonal elements of the matrix Ry are zeros then the primary user is not utilizing the particular frequency band at that given time and location, and otherwise the frequency band is not idle.

4.3.5Comparison

Each detection method has its advantages and disadvantages in signal detection.

These are shown in below table where various aspects of these methods are compared.

Execution time: The ideal signal detector need to function properly in real time so that the execution time is the shorter possible.

Noise rejection: One of the Characteristic of the method to be immune to the white noise

Prior Knowledge: Information required in detecting the signal. In CR this information should be minimum.

Computational complexity: Capacity calculation required to detect the signal.

Interference rejection: Skill of the method to be immune to the disturbance which is different from white noise

Execution time

Noise Rejection

Prior Knowledge

Computational Complexity

Interference rejection

Matched Filter

GOOD

MEDIUM

HIGH

LOW

HIGH

Energy Detection

HIGH

LOW

NONE

LOW

LOW

Feature Detection

LOW

HIGH

MEDIUM

HIGH

HIGH

Eigenvalue Detection

LOW

HIGH

NONE

MEDIUM

LOW

Covariance based Detection

MEDIUM

LOW

MEDIUM

MEDIUM

HIGH

Table shows the comparison of various detection techniques

From this table, it can be concluded that eigenvalue based approach is more accurate in detecting the signal.

Figure shows Sensing accuracy and complexities of various detection methods.

It can be analysed from the figure, that matched filter based detection is complex to implement in CRs, but has high accuracy. Similarly, the energy based detection is least complex to implement in CR system and least accurate compared to other approaches. And other approaches are in the middle of these two.

But the eigenvalue based approach has more accuracy as well as complexity to implement is least. Hence this method is more accurate and used as a core for the proposed SVD based signal detection.

4.4 Singular Value Decomposition (SVD) based Signal detection:

Based on the low detection time and low Interference rejection, Singular value decomposition based signal detection is considered as more efficient in detecting primary user without knowing the properties of the primary signal.

The most accurate technique that can achieve simultaneously both low probability of false alarm and high probability of detection with very less information about the primary signals and noise spectrum is eigenvalue- based detection technique.

The block diagram of the SVD based signal detection is shown below.

User interface - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Threshold| |

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Comparison

Eigenvalues

Calculation

Covariance matrix| |

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Radio System interface Radio system interface

Figure shows the block diagram of SVD based signal detection.

In this method, the decision threshold is obtained from Random matrix theory to find the hypothesis testing for signal detection.

So in order to calculate the eigenvalue and compare it with the derived threshold, this method is using eigenvalue decomposition.

Singular value decomposition is quite similar to eigenvalue decomposition but the difference is that eigenvalue decomposition is applied to certain square matrices whereas Singular value decomposition based approach can be applied to any m x n matrix.

SVD has several advantages when compared with other decomposition methods. The main advantage is that it is more robust to the numerical error, exposes the geometric structure of a matrix an important aspect of many matrix calculations. [2]

After constructing the covariance matrix, SVD is performed on the received matrix. Maximum and minimum eigenvalues are obtained.

The presence or absence of the primary users can be detected by comparing the singular values with the given threshold.

Using SVD, the received matrix can be represented as U ∑ VT

Where U and V are orthogonal matrices and ∑ is the diagonal matrix.

4. System Model

Suppose that there is one primary and secondary user. Assume that the wideband frequency is divided into K sub channels. A sub- channel is either occupied by the primary user (H1) or unoccupied (H0). In the ith sub channel, yi(n) the received signal at the nth sampling such that i=1, 2,……K, n=1,2,….N where N is the sample number and K is number of sub channels. yi(n) can be defined as

H0: yi(n)= È i(n) Signal absent

H1: yi(n)= xi(n) + È i(n) Signal Present

Where È i(n) is Additive white Gaussian Noise (AWGN)

xi(n) is primary user signal in ith sub channel and it can be superposition of the received signals from multiple primary users, hence synchronization is not required.

There are two probabilities involved for signal detector: probability of detection, Pd, which defines, the hypothesis H1, the probability of the detecting algorithm having detected the presence of the primary signal; and probability of false alarm, Pfa, which defines, at hypothesis H0, the probability of the detecting algorithm claiming the presence of the primary signal. Test statistic for an energy detector is given by

Under the hypothesis H0, it shows a Gaussian random distribution when number of signal sample (Ns) is large with mean ση2 and variance 2/Ns ση2. Hence, for a given probability of false alarm Pfa, the threshold ϒ of an energy detector can be derived as

is the normal Q-function.

5. Proposed Model

Received signal matrix from CR user.

Factorize in the form of U ∑ VT

Obtain λmax , λmin and ϒ(threshold)

λmax/ λmin >ϒ

Primary signal is absent

No

Yes

Primary signal is present

6. Simulation Results:

7. Conclusions: