Cross-Layer Design for Cognitive Radio Networks

Cross-Layer Design for Cognitive Radio Networks

M. Ayyash (Chicago State University, USA) and Y. Al-Sbou (Mu’tah University, Jordan)
DOI: 10.4018/978-1-4666-2005-6.ch012
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Nowadays, due to the tremendous growth of wireless communications technologies and multimedia applications, the radio spectrum is starting to be crowded and scarce to meet the continuous growth of frequency requirements. Additionally, interference management is one of the key issues in wireless networks. Therefore, network solutions and evolutions have crucial challenges to overcome the inefficiency in configuring and managing network resources. To optimize wireless network operations and spectrum scarcity, a new networking paradigm, known as cognitive radio networks (CRNs), has been introduced. Due to the limited capabilities of the conventional layered protocol, CRNs adjust layer parameters adaptively according to the spectrum environment and Quality of Service (QoS) requirements. Hence, cross-layer design (CLD) solutions were necessary to allow for improving and optimizing CRNs performance. This chapter provides an extensive and exclusive overview of cognitive networks, CLD methodologies and properties, and cross-layer optimization (CLO) schemes among different layers. Moreover, it presents possible research solutions for cognitive networking. Finally, indispensable highlights of future work research directions are provided.
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12.2 Background

The recent development in radio communications and mobile computing provides several technologies for wireless connectivity where, different wireless network standards have appeared such as wireless local area networks (IEEE 802.11b or HiperLAN), wireless wide area networks (WiMax and UMTS) (Baldo & Zorzi, 2008a). The deployment of wireless networks can be in two modes: infrastructure-based mode in which access point links the nodes to the wired network and infrastructureless-based or ad hoc mode. Mobile ad hoc networks (MANETs) consist of wireless mobile nodes without reliance on fixed base stations or wired infrastructures (Ayyash, Ucci, & Alzoubi, 2010).

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