It is a technology of wireless communication where the network or a user’s flexibility is bound to change its transmitting or receiving parameters to achieve more efficient communication performance without interfering with licensed or unlicensed users [1]. According to Mitola, the cognitive radio cycle continually observes the environment, orients itself, creates plans and decides then acts. The cognitive radio monitors the available spectral bands, captures the information or data provided from these bands, and detects the spectrum holes. It focusses on the frequencies usage and the mode identification. The cognitive users are the unlicensed users who need to keep monitoring the spectrum. They usually seek to detect the presence of licensed or the primary users. There are a number of merits associated with the use of cooperation in cognitive radio. This paper looks at the implementation and testing of the cooperative cognitive spectrum sensing within a network. The merits that stand out the in the cooperative spectrum sensing in a cognitive radio network are the reduction of detection time and the increased agility. The cognitive users are involved in the implementation using the proposed practical algorithm which allows cooperation in random networks.
The cognitive radio is an advancement of the software defined radio which seeks to achieve the significant improvements over services offered by the current wireless networks. The cognitive users also utilize the license band which must detect the presence of licensed users in a very short time and must vacate the band for the primary users. This paper seeks to find out how the cognitive users sense the primary users within a given mobile network. The cognitive spectrum sensing techniques are totally based on opportunistic spectrum sharing. The new technology does not really rely on the license-based spectrum allocation trends as provided by the Federal Communication Commission. The FCC provides a license to the service provider for the SP to have authorized access to the spectrum in the defined range. Some of the service providers do not fully utilize the spectrum leading to scarcity of the Radio Frequency spectrum. Researchers have been quite involved in finding a newer model of the spectrum sharing considering the frequency spectrum is a scarce resource that is required by many people. An alternative of the cognitive radio in the spectrum sharing strategy is the ultra-wideband. The cognitive radio is preferred over UWB as it achieves a more efficient spectrum utilization by finding empty frequency bands unlike UWB which achieves spectrum sharing by overlaying existing narrowband systems. The technology has the ability to unlock the hidden or unused spectrum which may be useful for deployment of the next LTE data rate systems. One of the newest advancement in the design of improved cognitive radio is the available resource map which is based on similar operational principles of the conventional cellular networks. The advancement is a real-time map that of all spectrum usages and it is updated and maintained by the ME and BSS systems in a mobile communication network. The cognitive radio is useful in detecting wireless intrusions and the building software for the radio is 802.11b. The scan ISM band for different services is used to detect the unknown activity.
The cellular network is comprised of the cell and the mobile switching center. The public telephone network and internet which could be wired connection. The cell covers a given geographical region and is comprised of the mobile users, the air interface or the abis interface and the base station. The base station is referenced in the IEEE 802.11 AP. The mobile switching center connects the cells to wide area networks and manages the call setups as well as handling the mobility of the users within the general mobile network. Within the first hop, there are two techniques for sharing the mobile to Base station radio spectrum. The time domain multiple access and the frequency domain multiple access are combined to ensure more efficient communications. The code division multiple access is an alternative to the two as it improves on the uplink and downlink data rates. The aim of improving the mobile communication necessitates the mobility of users in the systems. Initially, all the primary users of a given mobile network would require to be at a telephone booth to make a call but the improvements in the access of services improved to half duplex on mobile user equipment and later on to full duplex. The cellular networks are assigned licenses to operate in a given frequency band. The frequency band, is thereafter, utilized exhaustively to ensure that as many users are accommodated in a given time span as possible. The primary users of the network are registered on the system in a resource known as the home location register and in the visitor location register while in transit.
The primary users do not use the system continuously during communication and the network can be in idle state for a given period of time especially during the off-peak hours. The cognitive radio takes advantage of such periods to utilize the unused segments of the signal. The cognitive users are not licensed users hence any action or attempt to access the system while the system is in use by the primary users may be considered a breach of privacy or a threat to the primary users licensed network. The alternative way to utilize the network is to have the cognitive users listen on in the network so that they can know when to use the unused frequency. The network should equally have systems that check if the system has unused frequencies which it can share with the cognitive users to ensure more effectiveness in the use of the frequency band allocated to the users.
Spectrum sensing is the main enabling functionality in the Cognitive radio technology as it is very sensitive to limit the unwanted interference. Wireless communication system design needs higher data rates, larger channel capacity, and improved quality of service to meet the needs of the consumers who are increasing by the day. The spectrum utilization efficiency helps to meet the needs of the wireless users. The security issues surrounding wireless connections or communication systems have drawn a large following as it is considered to have an open-air nature. The frequency band is a limited resource that needs to be shared among many users. According to statistics performed in the U.S., the utilization rate of the licensed spectrum stands at 15-85 percent regardless of the time or location. The spectrum sensing cognitive radio detects and utilizes unused spectrum for the non-invasive opportunistic channel access. Some of the applications are the emergency network solutions, vehicular communications, and causes an increase in transmission rates and distances. The cognitive radio technology is motivated by the spectrum access and scarcity. The cognitive radio technology is a part of wireless communication where a network or a user flexibility changes its transmitting or receiving parameters to achieve more efficient communication performance without interfering with licensing or unlicensed users [1].
Under cognitive radio technology lies the spectrum sensing, spectrum management, spectrum sharing, and spectrum mobility. Unfortunately, the cognitive radio technology encounters some caveats as the intrinsic properties of cognitive radio paradigm produce new threats and challenges to wireless communications. A spectrum hole is a band of frequencies assigned to a primary user but at a particular time and specific geographical location, the band is not being utilized by that user. According to Mitola II, the cognitive radio is a radio that employs model based reasoning to achieve a specified level of competence in radio-related domains. Under digital systems, the cognitive radio is an intelligent wireless communication system that is aware of its surrounding environment. It uses the methodology of understanding through a training and learning process from its surrounding so as to adapt its internal conditions to the statistical variations in the incoming radio frequency stimuli. The adaptation is aided by making corresponding changes in certain operating parameters such as the transmit-power, carrier frequency, and modulation strategy in real-time with two primary aim. The cognitive radio seeks to provide universal, highly-reliable communication as needed while efficiently utilizing the scarce radio spectrum. The cognitive radio can co-exist with the legacy wireless systems, uses their spectrum resources, and does not interfere with them.
Spectrum sensing in cognitive radios seeks to obtain awareness about the spectrum usage and existence of primary users in a geographical area. A classical signal detection problem where the channel gain, primary signal and noise are taken into account when tackling the problem. The software defined radio, SDR, primarily defined in software form which supports a broad range of frequencies and its initial configurations can be modified for user requirements. The SDR tend to be easily reconfigurable, easy to upgrade, have lower maintenance costs, and responds to the changes in the operating environment. The cognitive radio is a combination of intelligence and software defined radio. It is denoted as,
There are several spectrum sensing methods which use transmitter detection, cooperative detection, and interference temperature detection. The transmitter detection is achieved using the matched filter detection, energy detection, or cyclisation detection techniques. The use of multiantenna system model sends a single PU signal to detect and it does not require the TX signal or noise variance knowledge. The spectrum sensing problem is formulated according to simple binary hypothesis test. Under the transmitter detection, some detection techniques are used to achieve the spectrum sensing required in cognitive radio technology. The matched filter detection technique has better detection performance compared to other detection techniques and less time to achieve processing gain. The matched filter detection, however, requires pilots and preambles or synchronized messages. The procedure used in the matched filter detection is as illustrated below,
The final transmitter detection mode is the cyclo-stationary detection techniques. It seeks to exploit the built-in periodicity of modulated signals coupled with sine wave carriers, hopping sequences, and cyclic prefixes. It is observed to perform better than the energy detection but it tends to be more computationally complex and has longer observation time. The proper application of the software defined radio technology would facilitate the single platform design and would also provide a path towards the realization of concepts such as reconfigurability, run-time reconfiguration and eventually self-governed learning also referred to as cognitive radio. The aim is to have a universal wireless device that can seamlessly handle a range of frequencies, modulation techniques, and encoding schemes. The primary users or the licensed uses have exclusive rights to a certain spectrum band hence they are the license holders while the secondary users are the cognitive-radio enabled users with lower priorities than primary users as they do not have a license. In summary, the spectrum sensing seeks to detect the spectrum holes, to determine the spectrum resolution for each spectrum hole, provide an estimation of the spatial directions of an incoming interfering signal and the signal classification. The detection of the spectrum holes is the main task as it supports the entire spectrum sensing process especially when the detection is performed within the confines of a narrow frequency band.
It is implemented by the cognitive radios. It provides attributes such as the self-awareness, context-awareness and adaptability. It defines a cognitive radio as that which has control processes that permit the radio to leverage situational knowledge and intelligent processing to autonomously adapt towards some goal. The intelligence is the capacity to acquire and apply knowledge especially towards a purposeful goal. The architecture of the cognitive radio is given such that the radio environment has a reconfigurable radio platform with a transmit and receive, user behavior is factored in as well as the device and state of the system. It provides a sensing feedback path that ensures the results are compared with the intended output to eliminate any form of additional errors caused by disturbances in the radio environment. The radio system has policies and rules that govern its operations.
Cognitive radio finds application in the wireless cellular networks, public safety networks, smart grid networks, and the wireless medical networks. The cognitive radios listen to their radio environment and the various signal processing techniques. The sensing quality is vulnerable to wireless impairments. The sensing quality is enhanced with cooperative communication techniques.
Spectrum sensing aims at identifying the spectrum holes. The cooperative spectrum sensing seeks to mitigate the multipath fading, shadowing and hidden terminal problem. The primary user has two states, they can be idle or busy. There is noise and noise plus signal. The binary hypothesis describing the spectrum condition is given as,
The hidden node problem and the need for cooperative spectrum sensing is provided for during the implementation of cognitive radio. The illustration below is a case of the hidden node problem in a cognitive radio network.
There are three steps taken to mitigate such problems namely: local sensing, reporting, and decision or data fusion. The basic configuration of the centralized cooperative spectrum sensing is given as,
Some of the proposed sensing structures are:
The cooperative spectrum sensing in Cognitive Radio network is simulated in the presence of primary user emulation attack. Through the proposed optimal combination scheme, the detection probability of the spectrum hole is optimized under the constraint of a required false alarm probability. Simulation results show the detection performance improvement of the proposed optimal combining scheme over the conventional MRC method. The cooperative detection prevents the hidden terminal problem by incorporating the information from multiple system users for the primary user detection. The cooperative detection can be implemented in a centralized and distributed manner. The transmission can overlap to the air interfaces already present in the environment so that it can change the nature of observations and make new problems. To solve the problems as suggested, there are two distinct networks deployed separately. The sensor network for cooperative spectrum sensing and the operational network for data transmission. The method is implemented in a centralized manner. The system allows for sharing the analysis model in an off-line method when in the environment no SUs are observing the radio scene. It is assumed that the observation of the SU is due to its position and to the state of radio source rather than the observation of the SU independent measurements for each SU is presented either in a centralized or distributed manner [2].
There are two detection methods namely log-likelihood combining and weighted gain combining. The log likelihood combining is observed using the likelihood ratio test as,
The weighted gain combining is given such that,
Some of the key merits of using the cooperative spectrum sensing are:
There are a number of spectrum sensing techniques namely
Wireless transmission is evolving to transport data, voice and video at faster speeds. The main bandwidth increment drivers are voice and video which have a larger capacity. There are technological solutions that can provide very large capacities which are based on abandoning the property rights model of owning part of the frequency spectrum. The research shows that the fixed frequency spectrum allocation has become fundamentally flawed and as such it has created the need to exploit wireless communication strategies that exploit the time, space, and frequency degrees of freedom. In sharing a transmission medium, three degrees of freedom are availed namely the frequency, time and space. The time and space are more effective to use as compared to the frequency. The frequency division model allows users to communicate while being allocated different frequencies which does not fully exploit the channel as only a limited number of users are allowed at a given time [5]. It is also a very expensive model or approach in design of a transmission medium considering that each frequency is obtained from the service providers at a fee. The use of the space and angle model seeks to reduce the transmission power.
It is achieved by decreasing the radius of the omni-directional cells and it exploits the angular nature of the spatial channel for multiple antennas. The state telecommunication regulator usually allows for sharing of the frequency spectrum where the interference is quite negligible and the ultrawide bandwidths are allowed. The time domain filter seeks to block the impulse in time and they can be easily made adaptive unlike the frequency domain. Spatially separated sensing radios can make independent measurements. The single radio sensitivity can be improved by the sue of multiple antennas using beam forming [6]. The angular isolation of the beam formed signals requires not only co-location but the same angle. It essentially eliminates interference and the UWB-like spectrum sharing and cognitive techniques to achieve essentially unlimited capacity. The number of users is increased with the use of multiple antennas which provides an unlimited increase if frequency or antenna area is increased. The interference between beamformed signals has to not only be in the same space but also have the same angle.
Wireless communication system design requires higher data rate and larger channel capacity as well as better quality of service and spectrum utilization efficiency to meet the needs of wireless users. The security issues have drawn much research attention in wireless communications due to its open-air nature [6].
The most critical function of the cognitive radio is spectrum sensing. The radio frequency spectrum is a scarce resource and it is important to detect and utilize the unused spectrum also known as the white space for the non-invasive opportunistic channel access. The applications of the spectrum sensing cognitive radio is in the emergency network solutions, vehicular communications, increase transmission rates and distances [7]. The cognitive radio techniques seek to increase system capacity. With proper power allocation by the primary users the secondary users can reach the maximum achievable rates. The first step taken is in creating the binary hypothesis test,
The null hypothesis is one taken when there exists only noise and no transmitted signal while H1 is used for the period where there is both noise and signal during transmission. The generalized likelihood ratio test is given as,
The generalized likelihood radio test for the spectrum sensing, the ML estimates are given such that the
The statistic is a ratio of the largest eigenvalue to the sum of eigenvalues of the sample covariance matrix. The GLR factor is the threshold that is determined from a given probability of the false alarm. The simulation comparable are given as ED-U using the multichannel case with u as the noise uncertainty. The GLRT scheme is based on known noise variance that replaces the noise variance by the smallest eigenvalue of the sample covariance matrix. The AGM computes eigenvalues of a sample covariance matrix and compares to threshold from probability of false alarm.
Some of the assumptions made during the simulation are that the probability of the false alarm is 0.01, the covariance matrix for receiving signals is ranked at 1 and the independent Rayleigh fading channels are used. The simulation mitigates the issues stemming from estimation of noise variance in the hope to exploit signal structures. It is the rank 1 covariance matrix. When there are fewer samples, GLRT is significantly better. It has a marginal performance gain with N=100 samples. As expected, probability of detection increases with N. The statistic is a ratio of the largest eigenvalue to the sum of eigenvalues of the sample covariance matrix. The GLR gives the threshold determined from a given probability of the false alarm. The asymptotic results provide close prediction of detection performance of the GLRT [8].
The tests conclude that the GLRT provides better performance than all other methods used in the test for every case of N samples. It is significantly better for less samples [9]. The model can reduce number of samples required or improve performance with a fixed number of samples. For the binary hypothesis, the performance metrics are either false alarm, missed detection and detection. The higher detection and lower false alarm efficiency is the most preferred for spectrum sensing high performance. The energy detection is the energy of the received signal.
The additive white gaussian noise is factored in the test statistics alongside the deterministic or random signal and channel. The values obtained are compared with the threshold. To determine the performance of the systems, the following metrics are considered,
The average power at the detection or receiver point,
The area under the receiver operating characteristics curve is given such that the probability that choosing correct decision is more likely than choosing incorrect decision. The AUC versus the SNR is given as,
Spectrum sensing timing windows
The intrinsic properties of cognitive radio paradigm produce new threats and challenges to wireless communications. The spectrum occupancy failures are such as the policy failures, location failures, sensor failures, transceiver failures, operating system disconnect, compromised cooperative, CR, and common control channel attacks [4]. The radio spectrum has a number of unutilized frequencies in given bands. The telecommunication service providers need to use practical algorithms that specify which frequencies or parts of a channel band are unoccupied. The cognitive users as well must come up with algorithms such as the energy detection algorithm to detect which areas of the radio spectrum are unused for them to use them in the unlicensed form [5]. There are five basic approaches to using the available spectrum for the primary and secondary users. The cognitive radio seeks to preserve some part of the spectrum for the primary users and the secondary users are allowed to access the unused bands and channels. The aim of implementing the cognitive radio in a telecommunication mobile network is to eliminate legacy users entirely. The UWB is an energy limited regime that works because most bands are not used. The secondary takes cares to avoid disturbing the primary users. There are denials where the system detects that the primary user may be disturbed and as such the secondary user uses opportunism to keep listening on the network to tell if the primary is online or not.
There is a myriad of factors to consider before implementing the cognitive radio such as the primary power, amount of protection given to the primary users, the multiple secondary users that are in a group, the heterogenous propagation losses, the multipath and shadowing, coherence times, primary duty cycles, secondary power, cooperation, competition, modulation models, implementation complexity, and system robustness [10]. There are a number of uncertainties imposed on the limits of the device sensitivity that can be outdone when the secondary users choose to listen on a network. Shadowing is a main challenge but can be overcome with multi-user cooperative diversity within a system. Potential interference from another secondary’s is a very significant uncertainty, but can be mitigated through mandated local cooperation among systems [11]. Non-interference is a system-level, rather than device-level, property and must be regulated as such. Uncertainty imposes limits on device sensitivity that cannot be overcome by just listening longer. A secondary user might be faded while his transmissions could still reach an unfaced primary receiver. A secondary user who cannot distinguish between positions must be quiet in both. The regions that the one is giving up on is safe but might be faded due to uncertainty [12]. The limit on sensitivity and all the detectors are based on some averaged test statistic if the value is high and the primary is considered present and the when the value is low as it is considered absent. Increasing the amount of averaging, increases our confidence in the decision and the unmodeled or non-ergodic uncertainties introduce unresolvable ambiguities.
More potential secondary users to keep quiet in the near vicinity of primary receivers [13]. The challenge of aggregate interference where one secondary user might be acceptable though there are millions who would be limited on power density and the slower effective attenuation with distance. Secondary users should not confuse the uncertain aggregate interference with the primary signal. The nearby secondary users need to be very quiet during the detection phase. There is an increasing density of secondary transmission that requires more cooperation. The caveats are studied based on the classical link budget [14]. For reliable communication at a given range within a given bandwidth the signal to noise ration of a signal is given as,
The coding gain is bounded by channel capacity and the additional margins are needed to deal with uncertainty. The cognitive radio seeks to ensure that the secondary users do not interfere with primary users. This is achieved by setting a limit on the tolerable out-of-system interference and translates to a limit on maximum power and not minimum power. The cognitive radio encounters different propagation path losses which tend to occur between the primary and secondary users [15]. There are multiple opportunistic users that tend to emit heterogeneous transmit powers. The fundamental constraint of the system is,
The determines how much interference above the noise floor the primary system can tolerate. The equation is given by,
While the secondary system must guarantee,
Additionally, the system incorporates the solo secondary sensing link budget such that at the secondary, the primary signal has dropped by a given value, . While at the primary receiver, the secondary transmission must be attenuated by . The sensitivity required versus the desired secondary power is given as,
The single transmitted power constraint [20] is given as,
Conclusion
In a nutshell, the paper managed to analyze the cognitive radio spectrum sensing techniques, performance, and caveats. The main driving force behind the use of cognitive radio is the spectrum sharing methods. It enables the licensed users share the frequency band with the unlicensed users or the cognitive users. The cognitive radio seeks to preserve some part of the spectrum for the primary users and the secondary users are allowed to access the unused bands and channels. The aim of implementing the cognitive radio in a telecommunication mobile network is to eliminate legacy users entirely. The UWB is an energy limited regime that works because most bands are not used. The secondary takes cares to avoid disturbing the primary users. In summary, the spectrum sensing seeks to detect the spectrum holes, to determine the spectrum resolution for each spectrum hole, provide an estimation of the spatial directions of an incoming interfering signal and the signal classification. The detection of the spectrum holes is the main task as it supports the entire spectrum sensing process especially when the detection is performed within the confines of a narrow frequency band.
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