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Recently, various wireless communication technologies are being developed and used in many aspects of human life very closely. Many studies have been devoted by academic laboratories and industries. The third generation system, for example, is currently in general use, and next generation communication technology is a matter of primary concern. Despite of the bright future of the wireless communications, however, it may have many problems regarding the scarcity of frequency spectrum. New wireless technology requires a new frequency spectrum in many cases; however it is getting harder to find a new spectrum especially for a wide-band multimedia application. Thus, spectrum allocation and management are very important tasks because of its scarce spectrum. As the frequency spectrum becomes exhausted, cognitive radio is arousing related party's big interest. The cognitive device is able to sense unused spectrum, and use it efficiently without interference. Note that most WPAN and WLAN systems are designed to be operated in unlicensed ISM band. Those devices have to compete for spectrum resource with many others in the band. On the other hand, other wireless systems, such as TV broadcasting, digital TV and wireless microphone, are seemingly wasting the licensed spectrum depending on time and location. The primary application of the cognitive radio has been the accessing unused resource of licensed users without disturbing them.
3.2 Software Defined Radio (SDR):
A Software Defined Radio (SDR) is a mixture of software and hardware technologies that permits reconfigurable system architectures for user terminals and wireless networks. Through programmatic reconfiguration, radio hardware can be reset over time to perform varying functions. This sets the stage for common platform technologies supporting varied infrastructure services, and would enable better scalability, reusability, interoperability, and portability of platforms and waveforms.  SDR is a radio communication system which has the ability to tune to any frequency band and receive any modulation across a wide frequency spectrum by using the same hardware and process the signals through software. From mobile communication perspective it means user can access multiple systems from a single terminal.
Another and somewhat different view of the software radio is in terms of the traditional OSI model of a communication system. In this regard, the software defined radio mainly concerns the two lowest layers of the OSImodel, as depicted in figure 3.1
Figure 3.1: SDR in terms of OSI Model
Based on the OSI model, it becomes very clear, that SDR is a software reconfigurable radio technology. The term SDR was originally coined by DARPA's chief scientist, Dr. Joseph Mitola, who saw a graduation of technologies that began with the hardware defined radio and evolved into the digital radio and the software-defined radio in which all applications can be configured by software. Based on the OSI model, it becomes very clear, that SDR is a software reconfigurable radio technology. The term SDR was originally coined by DARPA's chief scientist, Dr. Joseph Mitola, who saw a graduation of technologies that began with the hardware-
defined radio and evolved into the digital radio and the software-defined radio in which all applications can be configured by software. In wireless network the total available spectrum (Bandwidth) is divided into sub spectrums (channels also know as frequencies). Then each cell is allocated with these sub spectrums. Mobile users present in the networks use the spectrum of the cell in which they are present. Channels are allocated in such a way that they overcome the effect of Co-Channel Interference (CCI), which is caused due the interference of adjacent cell channels in the networks. CCI occurs if the adjacent cells are allocated with same channel, here the concept of frequency reuse (channel reuse) comes into account.
3.2.1 SDR to CR:
CR builds on SDR technology. It represents an SDR with not only the ability to adapt to spectrum availability, protocols, and waveforms but the capability to learn waveforms and protocols, to adapt to local spectrum activity, and to learn the current needs of its user. 
3.3 Aware, Adaptive and Cognitive Radio:
CR technology enables the radio itself to learn, allowing it to perform cognitive functions such as identifying and using empty spectrum to communicate more efficiently. CRs will sense and adapt their behavior according to the environment in which they operate. Once there is an embedded machine in which the software implements the protocols programmed for it, the radio is able to be smart and alert and it can negotiate with its environment. For example, a CR would learn about various services of interest to its user by being aware of its user's activities. The radio knows how to find those services and knows the likelihood that some services will be of interest to the user in the immediate area. For instance, a CR could be aware of a Bluetooth network and what is available and of interest to its user within the Bluetooth service zone. It could also be aware of what's available in a wireless LAN range, cell phone range and so on. Figure 3.2 shows the ability of cognitive radio. 
Figure 3.2: Cognitive Radio Concept
3.4 The vision of cognitive radio:
A Cognitive Radio is an extension of modern Software Defined Radio. This extension creates new capabilities for users. An aware radio has sensors and is aware of the environment (or at least a subset of the environment). An adaptive radio is aware of its environment and is capable of changing its behavior in response.  The next level is CR, and the following characteristics are included in concept of a CR:
Aware of its environment.
Capable of altering its physical behavior to adapt to its current environment.
Learns from previous experiences.
Deals with unknown situations.
CR networks offer a much more intelligent way to fully and fairly utilize the spectrum, by adjusting the CR's transmission parameters to accommodate to the fluctuations of the communication environment. A cognitive radio network is shown in figure 3.3.
Figure 3.3: Cognitive Radio Network Exam
Practically there are multiple levels of cognition, the simplest being a pre-programmed device that has no model-based reasoning, but can sense its operating environment and make some decisions about which built-in capabilities to use at a given time. An example of such a device is a multiband, multi-protocol cellular telephone. Higher levels of cognition, which are not available today, would allow a cognitive radio to negotiate the parameters of the communication with other radios or base stations. 
3.5 Dynamic Spectrum Allocation in Cognitive Radio:
In 2004, the FCC issued a notice of proposed rule making (NPRM) that there is a possibility that unlicensed users can borrow spectrum from licensed users taking into account that there is no interference between licensed and unlicensed users, and unlicensed users itself. Cognitive Radio is a kind of device that borrows the unused spectrum on temporary basis. Basic cognitive radio techniques, such as dynamic frequency selection (DFS) and transmit power control (TPC), already exist in many unlicensed devices. However, to reach the full promise of cognitive radios, many significant design challenges lie ahead.  Sensing of vacant spectrum is a main task for dynamic spectrum allocation in cognitive radio networks (CRNs). An important issue linked with MAC-layer sensing in CRNs is how often and in which order to sense the availability of licensed channels. Cognitive Radio (CR) provides an innovatory concept to allow a secondary usage when a licensed band is available. By SDR technology, a CR node can intelligently be aware of its surrounding environment, exchange channel utilization and signal noise, and then choose the best way to communicate. In CR network, a licensed user, also called Primary User (PU), owns spectral resource and has right to access it. However, a Secondary User (SU), also called CR node, could use the spectral resource when it is free and vacates the spectrum in case the PU claims it. The main tasks for CR implementation can be summarized as follows:
The implementation issues are shown in the figure 3.4.
Figure 3.4: Cognitive Cycle
3.6 Cognitive Radio & Physical layer:
Conventional communications systems are defined and standardized using seven ISO/OSI layers, where physical layer functions realize signaling for the specific medium. Physical layer functions are interfaced with a data/link layer through a handshaking protocol. Even though cognitive radios are quite different from traditional wireless radios, it is reasonable to assume that a cognitive radio framework would be based on ISO/OSI layering methodology. A further advantage of layering approach could be to leverage a cognitive radio system design by enhancing existing layers of conventional radios with unique cognitive functionalities.  First and foremost, one should start from cognitive functions on a physical layer in order to understand capabilities and limitations of their implementation so that upper layers can be designed using realistic models. Cognitive radio communication is strictly conditional on the reliable detection of unoccupied spectrum. This requirement establishes a new type of functionality on the physical layer for spectrum sensing over all available degrees of freedom (time, frequency, and space) in order to identify frequency bands currently available for transmission. The major challenge of spectrum sensing is the detection of weak signals in noise with a very small probability of miss detection. Spectrum sensing requires the radio to receive a wideband signal through an RF front-end, sample it by high speed analog-to-digital (A/D) converter, and perform measurements for detection of primary user signals, as illustrated in figure 3.5.
Figure 3.5: Cognitive Radio Receiver
The challenges in spectrum sensing are:
Achieving sufficient RF front-end sensitivity for wideband signals.
Accurately detecting dissimilar, frequency band dependent, primary signals at differing received power levels.
After identifying an available spectrum segment, a cognitive radio should use modulation schemes that provide best spectrum utilization and capacity while avoiding interference to any primary user. Furthermore, the desired transmission scheme should be flexible to allow assignments of any band to any user, and should be scalable with the number of users and bands. In the ideal case, this flexible wideband transmission would be realized by digital domain waveform synthesis, where a set of parameters specifies transmission bands and power control. Figure b illustrates the top-level architecture of a wideband transmitter. The main challenge is to create a signal that, without external analog filters, adaptively changes the occupied bandwidth and without causing interference to any active primary users.  The cognitive radio transmitter is shown in figure 3.6.
Figure 3.6: Cognitive Radio Transmitter
3.7 Spectrum Sensing:
Sensing the status/availability of a channel is commonly recognized as one of the most fundamental elements of a CR (and, hence, this framework) due to its crucial role of discovering spectrum opportunities and detecting the existence/return of PUs. Spectrum sensing can be realized as a two layer mechanism.  The spectrum sensing in cognitive radio is shown in figure 3.7.
Figure 3.7: Cognitive Radio and Spectrum
3.7.1 Transmitter Detection:
The cognitive radio should distinguish between used and unused spectrum bands. This means cognitive radio should have the ability to determine whether the signals transmitted from primary user/users are present in a specific portion of spectrum or not. Transmitter detection approach is based on the detection of the weak signal from a primary transmitter through the local observations of users. 
Three techniques are generally used for the transmitter detection:
Matched filter detection
Cyclostationary feature detection
184.108.40.206 Matched Filter Detection:
When the information of the primary user signal is known to the CR user, the optimal detector in stationary Gaussian noise is the matched filter since it maximizes the received signal-to-noise ratio (SNR). While the main advantage of the matched filter is that it requires less time to achieve high processing gain due to coherency, it requires a priori knowledge of the primary user signal such as the modulation type and order, the pulse shape, and the packet format. Hence, if this information is not accurate, then the matched filter performs poorly. However, since most wireless network systems have pilot, preambles, synchronization word or spreading codes, these can be used for the coherent detection. 
220.127.116.11 Energy Detection:
If the receiver cannot gather sufficient information about the primary user signal, for example, if the power of the random Gaussian noise is only known to the receiver, the optimal detector is an energy detector. In order to measure the energy of the received signal, the output signal of bandpass filter with bandwidth is squared and integrated over the observation interval T. 
18.104.22.168 Cyclostationary Feature:
An alternative detection method is the cyclostationary feature detection. Generally, Modulated signals are coupled with sine wave carriers, pulse trains or repeating spreading which result in built-in periodicity. These modulated signals are characterized as cyclostationary since their mean and autocorrelation exhibit periodicity. These features are detected by analyzing a spectral correlation function. The main advantage of the spectral correlation function is that it differentiates the noise energy from modulated signal energy, which is a result of the fact that the noise is a wide-sense stationary signal with no correlation, while modulated signals are cyclostationary with spectral correlation due to the embedded redundancy of signal periodicity. Therefore, a cyclostationary feature detector can perform better than the energy detector in discriminating against noise due to its robustness to the uncertainty in noise power. However, it is computationally complex and requires significantly long observation time.
3.7.2 Cooperative Detection:
The assumption of the primary transmitter detection is that the locations of the primary receivers are unknown due to the absence of signaling between primary users and the CR users. Therefore, the cognitive radio should rely on only weak primary transmitter signals based on the local observation of the CR user. However, in most cases, a CR network is physically separated from the primary network so there is no interaction between them. Thus, with the transmitter detection, the CR user cannot avoid the interference due to the lack of the primary receiver's information. Moreover, the transmitter detection model cannot prevent the hidden terminal problem. A CR transmitter can have a good line-of-sight to a receiver, but may not be able to detect the transmitter due to the shadowing. Consequently, the sensing information from other users is required for more accurate detection. Cooperative detection refers to spectrum sensing technique in which information from multiple users is incorporated for primary user detection. Cooperative detection can be implemented either in a centralized or in a distributed manner. In the centralized method, the base-station plays a role to gather all sensing information from the users and detect the spectrum holes. On the other hand, distributed solutions require exchange of observations among users. Cooperative detection among unlicensed users is theoretically more accurate since the uncertainty in a single user's detection can be minimized.
3.7.3 Interference-based Detection:
Interference is typically regulated in a transmitter-centric way, which means interference can be controlled at the transmitter through the radiated power, the out-of-band emissions and location of individual transmitters. However, interference actually takes place at the receivers. Therefore recently, a new model for measuring interference, referred to as interference temperature has been introduced by the FCC. The model shows the signal of a radio station designed to operate in a range at which the received power approaches the level of the noise floor. As additional interfering signals appear, the noise floor increases at various points within the service area, as indicated by the peaks above the original noise floor. Unlike the traditional transmitter centric approach, the interference temperature model manages interference at the receiver through the interference temperature limit, which is represented by the amount of new interference that the receiver could tolerate. In other words, the interference temperature model accounts for the cumulative RF energy from multiple transmissions and sets a maximum cap on their aggregate level. As long as CR users do not exceed this limit by their transmissions, they can use this spectrum band.