21-06-2014, 11:14 AM
COGNITIVE RADIO
COGNITIVE RADIO.docx (Size: 270.89 KB / Downloads: 12)
INTRODUCTION
The radio frequency spectrum is a limited natural resource to enable wireless communication between transmitters and receivers. Licenses are usually required for operation on certain frequency bands. The use of radio frequency spectrum is globally governed by the International Telecommunication Union (ITU).
Exclusivity leads to inefficient use of spectrum. FCC reported that while some bands are heavily used – such as those bands used by cellular base stations – many other bands are not in use or are used only part of the time. Much greater spectral efficiency can be achieved with unlicensed spectrum usage in the bands that are not heavily used. The underutilization of some frequency bands opens up the opportunity to identify and exploit spectrum holes. A promising mechanism to improve the spectrum utilization by exploiting the spectrum holes is based on the cognitive radio concept.
Cognitive radio (CR) is an intelligent wireless communication system that is aware of its surrounding environment, learns from the environment and adapts its internal states to statistical variations in the incoming RF stimuli by making corresponding changes in certain operating parameters in real time. The primary objectives of the cognitive radio are to provide highly reliable communications whenever and wherever needed and to utilize the radio spectrum efficiently. The key issues in the cognitive radio are awareness, intelligence, learning, adaptivity, reliability, and efficiency
Cognitive radios and networks
Cognitive radio (CR) is an intelligent wireless communication system that is aware of its surrounding environment, learns from the environment and adapts its internal states to statistical variations in the incoming RF stimuli by making corresponding changes in certain operating parameters in real time. The primary objectives of the cognitive radio are to provide highly reliable communications whenever and wherever needed and to utilize the radio spectrum efficiently. The key issues in the cognitive radio are awareness, intelligence, learning, adaptivity, reliability, and efficiency.
The term cognitive radio was first suggested by Mitola in 1999. He defines the cognitive radio as a radio driven by a large store of a priori knowledge, searching out by reasoning ways to deliver the service the users want. The cognitive radio is reconfigurable and built on the software-defined radio (SDR).
ENABLING TECHNIQUES
The following are enabling techniques for cognitive radios :
• Bayesian signal processing (e.g. cognitive radar with the availability of a priori information),
• Dynamic programming,
• Learning machines with feedback (e.g. neural networks), and
• Game-theoretic models.
In addition, also other enabling techniques can be identified:
• Dynamic frequency management,
• Software-defined radio (SDR), and
• Cross-layer protocol design
Dynamic programming
Dynamic programming refers to multi-stage decision processes. The multi-stage decision processes correspond to situations where there is a physical system whose state at any time is specified by a vector and in the course of time, the system is subject to changes. Due to the changes, the variables describing the system undergo transformations. In a decision process, there is a choice of decisions or transformations that can be applied to the system at any time. In a single-stage process we have to make a single decision. If we make a sequence of decisions, the process is a multi-stage decision process. The multi-stage decision problems are exceedingly difficult. Dynamic programming suffers from the exponentially increasing demand on computation resources and size as the input-space dimensionality is increased linearly.
Learning machines with feedback
A major task of the cognitive radio is to make decision on how to adapt the radio based on the information gathered from the environment. Techniques for improving the performance based on learning from the past history will be crucial in the development of cognitive networks. Generic learning-based cognitive radio with the capability to learn from the past in addition to simple reasoning is a relatively recent research area. Various researchers have used, e.g., genetic algorithms or neural networks to fine tune radio parameters with the goal of optimizing the performance
COGNITIVE TASKS
An ideal Cognitive Radio can be defined as wireless communication system with the ability and potential for sensing, perceiving, orienting, planning, decision making and autonomous learning. Through direct observation, a radio accepts information about its operating environment or the outside world. The received information is evaluated with the aim of knowing its relevance or importance. The evaluation determines the ‘plan’ and chooses an alternative ‘decide’ in a way that would improve evaluation. In a situation where change in waveform is necessary, the radio implements the alternative ‘act’ by adjusting ite resources and performing the appropriate signaling. These changes are reflected in the interference profile presented by the CR in the outside world. As part of the radio ‘learn’, perhaps by creating new modeling states and generating new alternatives
Radio-scene analysis
Radio-scene analysis encompasses spectrum awareness that can be classified into passive and active awareness as presented in Figure 3-3. In the passive awareness, the knowledge about electromagnetic environment, i.e. the spectrum use pattern, is received outside own secondary communication system. The knowledge about electromagnetic environment can be received from existing communication system, i.e., primary and secondary users negotiate for spectrum usage. For example, the base station of the existing (primary) communication system like television broadcasts frequency environment to the CR terminals (SUs). The spectrum use pattern can be obtained also from a server or database. In addition, in a policy based approach, the primary system
Channel identification
Knowledge of the channel state is required at the receiver for coherent reception. Thus, the channel state has to be estimated in the receiver. In addition, the computation of the channel capacity of a cognitive radio link and the power control algorithm in the transmitter require knowledge of channel-state information. This implies that digital baseband algorithms for adaptive estimation of the state of a fast fading channel are also needed in CR system. Channel identification algorithms can be classified into three categories: data-aided, non-dataaided and decision-directed methods. Data-aided channel estimation methods assume that the transmitted data is known and use this information in deriving the channel estimates. Non-dataaided channel estimation methods assume unknown transmitted data and remove the data by averaging. Decision-directed methods approximate the data-aided methods by detecting the data and using this data as a reference signal to the estimator
COGNITIVE RADIO NETWORKS
Cognitive Radio Network (CRN) is defined as composed of cognitive, spectrum-agile devices capable of changing their configurations on the fly based on spectral environment.
The notion of cognitive radios can be extended to cognitive radio networks. The cognitive radio network is an intelligent multiuser wireless communication system with the following abilities:
1. To perceive the radio environment (i.e., outside world) by empowering each user’s receiver to sense the surrounding environment continuously.
2. To learn from the environment and adapt to it in response to deviations in the environment.
3. To facilitate communication among multiple users through co-operation in a self-organized manner.
4. To control the communication resources among the multiple users through competition.
5. To create the experience of intention and self-awareness
CHANNEL MEASUREMENTS AND MODELS
The basic mechanisms by which the radio waves propagate are reflection, diffraction, and scattering. The temporal and spatial variations of the received signal in the wireless communication channel are divided into path loss, slow fading or shadowing, and fast fading or multipath fading. Path loss is the deterministic overall decrease in the signal strength with distance which is caused by the spreading of the electromagnetic wave radiating from the transmit antenna and the obstructive effects of objects surrounding the antenna. Shadowing is superimposed on the path loss. Shadowing causes slow random variations in the signal amplitude due to diffraction, scattering, and multiple reflections. Multipath fading causes fast random variations of the signal amplitude and phase due to mutual interference of the wave components of the multiray field
ACTIVE SPECTRUM SENSING TECHNIQUES[/b]
To be capable to sense very weak signals, cognitive radios must have significantly better sensitivity than conventional radios. Requirements for radio frequency (RF) frontend and analog-to-digital converter (ADC) are very demanding. After reliable reception and sampling of a wideband signal, digital signal processing techniques are utilized to further increase radio sensitivity. Most of the recent spectrum sensing works focuses on primary transmitter detection based on local observations of secondary users.
The spectrum has been classified into three types by estimating the incoming RF stimuli, thus, black spaces, grey spaces and white spaces. Black spaces are occupied by high power local interferer some of the time and unlicensed users should avoid those spaces at those times.
Grey spaces are partially occupied by low power interferers but they are still candidates for secondary use. White spaces are free RF interferers except for ambient noise made up of natural and artificial forms of noise e.g. thermal noise, transient reflections and impulsive noise. White
COOPERATIVE DETECTION
Considering spectrum sensing performed by a single radio, sensing requirements are set by the worst case channel conditions introduced by multipath, shadowing and local interference. By allowing multiple radios to share their sensing measurements it is possible to improve the overall probability of detection through exploiting the inherent variability of the channel. Several cognitive radios in various locations will not experience the worst channel conditions; therefore, the one with good channel conditions can provide reliable sensing information for the whole network. In order to improve the performance of the spectrum sensing, several authors have proposed cooperation among SUs. In the cooperative sensing, all the SUs send their knowledge about the channel state to an access point or a “master” node
Boosting protocol
Different approaches have been suggested for collecting and sharing the information in cooperative spectrum sensing. A boosting protocol for spectrum pooling system is suggested. The boosting protocol consists of two different phases. In the first phase, the subbands that are accessed since the last detection cycle are indicated. In the second phase, the subbands that have become idle since last detection system are signaled. The basic idea is that the information will be sent by transmitting complex symbols at maximum power level on the OFDM symbols that want to be pointed out and on the remaining OFDM symbols zeros will be transmitted. This information will be gathered by an access point (AP) and the information about actual pool allocation will be distributed among all associated mobile terminals and those who want to get associated
CONCLUSION
Many milestones, both regulatory and technical, have been reached in opening spectrum for more flexible and efficient use, and this trend will continue. Cognitive radio technology plays a significant role in making the best use of scarce spectrum to support fast growing demand for wireless applications, ranging from smart grid, public safety, broadband cellular, to medical applications. Standard development organizations (SDOs) have begun to develop standards to take advantage of the opportunities. However, challenges still remain since CR-enabled networks have to coexist with primary as well as secondary users and need to mitigate interference in such a way that they can better support such applications from end to end.