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In the late 1990s, nearly all telecommunications radios were built using digital signal processor (DSP) processors to implement modulation and signal processing functions, and a General Purpose Processor (GPP) to implement operator interface, network signaling, and system overhead functions. This architecture is attractive to a manufacturer because the same basic electronics can be used over and over for each new radio design, thereby reducing engineering development, enabling volume purchasing, and optimizing production of a common platform, while retaining the flexibility for sophisticated waveforms and protocols. A few manufacturers called their radios “Software Defined Radios” (SDRs), recognizing the power and market attractiveness of the customer community being able to add additional functionality that is highly tuned to market specific applications.

Current data networking technology limits a network’s ability to adapt, often resulting in sub-optimal performance. Limited in state, scope, and response mechanisms, the network elements (consisting of nodes, protocol layers, policies, and behaviors) are unable to make intelligent adaptations. Communication of network state information is stifled by the layered protocol architecture, making individual elements unaware of the network status experienced by other elements. Any response that an element may make to network stimuli can only be made in the context of its limited scope. The adaptations that are performed are typically reactive, taking place after a problem has occurred. In this chapter, we advance the idea of cognitive networks, which have the promise to remove these limitations by allowing networks to observe, act, and learn in order to optimize their performance.

Cognitive radio has evolved to include a wide range of technologies for making wireless systems more flexible via more flexible transceiver platforms and enhanced computational intelligence. Dynamic spectrum access networks [1, 2] evolved rapidly from regulatory rulings of the past few years [7].

Wireless communication devices are composed of three main entities; signaling, physical hardware, and its functionalities. These three main streams, which complement each other, have evolved since the invention of the radio transmission by Guglielmo Marconi. The primitive communications devices had very simple signaling, analog hardware, and limited functionality. In time, each of these entities evolved significantly. Different signaling methods have been invented and used around the world. Furthermore, numerous different wireless communications systems and standards have been developed around the world without any global plan. Recently, the diversity in wireless systems and standards bring some issues on the surface such as interoperability and global seamless connectivity. In parallel, hardware technology evolved significantly, too. Some of the key milestones in this progress are transition from analog hardware to digital hardware and then introduction of sophisticated processors. This is followed by the development of Software Defined Radio (SDR) structures and virtual hardwares that are under development currently.

In this chapter, the concept of a telecommunications value-chain is developed, leading to an exploration of the many ways in which the value-chain can be altered by reconfigurable software-defined radios, cognitive radios, and cognitive networks.

Cognitive radio is an emerging wireless communications paradigm in which either the network or the wireless node itself intelligently adapts particular transmission or reception parameters by sensing the environment. The goals of adaptation include maximizing spectral efficiency, minimizing interference to other cognitive radios, coexistence of licensed and unlicensed band communications, battery energy efficiency, etc. The environmental parameters that are continually sensed for adaptation include occupied radio frequency bands, user traffic, network state, etc. One promising technology that enables the implementation of a cognitive radio network is software-defined radio. The underlying theoretical principles for cognition are broadly based on signal-processing and machine-learning.

Cognitive radio devices have been considered as a key technology for next-generation of wireless communication. These devices can opportunistically utilize the wireless spectrum to achieve better individual device/user performance and improve the overall spectrum-utilization efficiency. However, allowing opportunistic use of the wireless spectrum creates new problems such as peaceful coexistence with other wireless technologies as well as understanding the influence of interference that each of these networks can create. In this chapter we model the efficiency of the cognitive radios from the Medium Access Control (MAC) analytically and study the improvement that cognitive radios can get compared to conventional radios. Then we consider the effect of peaceful coexistence with different types of cognitive radios and also consider a simple spectrum sensing protocol which is a function of the primary utilization and required opportunity.

Wireless communications is established through a common medium which is highly dynamic. The elements of wireless communications systems such as nodes in a network, users, and some properties of the wireless devices themselves (e.g. battery) are dynamic as well. In order for wireless communications systems to better perform, adaptation to these dynamic conditions and elements is essential. How well a wireless system adapts to these dynamic conditions depends on the amount of the knowledge of varying parameters. It is clear that the more the knowledge, the better the adaptation.

The need for higher data rates is increasing as a result of the transition from voice-only communication to wireless multimedia and web type of applications. Given the limitations of natural frequency spectrum, it becomes obvious that current static frequency allocation schemes cannot accommodate these requirements of increasing number of higher data rate devices. As a result, innovative techniques that can offer new ways of exploiting the available spectrum are needed. arises to be a tempting solution to spectral crowding problem by introducing the opportunistic usage of frequency bands that are not heavily occupied by licensed users [1]. While there is no agreement on the formal definition of cognitive radio as of now, the concept has evolved recently to include various meanings in several contexts [2].

Location information has been traditionally used for the positioning systems to estimate and track the location of a target device or object. The tremendous growth in the number of mobile users initiates the development of locationbased services, which are mainly based on the positioning systems. Moreover, the demands on the higher Quality of Service (QoS) such as global mobility and seamless connectivity from the users as well as wireless network operators motivate to exploit the utilization of location information in the wireless networks. Recently, it has been recognized that location-based services are not the only applications, where the location information can be used, but also it can be utilized to solve some other issues in the wireless networks. The applications based on the utilization of location information are folded under four categories: location-based services, network optimization, transceiver algorithm development and optimization, and environment characterization. For instance, location-assisted handover mechanism, routing, drop call management, and adaptive coverage systems are some examples of network optimization.