Cross-Layer Based Resource Management Frameworks for Mobile Cognitive Radio Networks

Abstract

Abstract.Resource management has been one of the most important aspects of Mobile Cognitive Radio Networks (MCRNs) since their inception. It engulfs fundamental network operations such as spectrum management, bandwidth allocation, channel access, data routing, transmission power assignment and energy management. All such mechanisms are necessary for achieving the delay/throughput requirements desired by modern network services and users. Compared to traditional wireless mobile networks, the MCRN environment poses additional challenges and complicated requirements to the above functions and resource management in general, which eventually lead to an urgent need for reconsideration of the employed wireless protocol stack. The objective in MCRNs would be to apply more efficient protocol hierarchies and achieve better management of the common available resources for both secondary and primary users, at the minimum possible cost and by leaving the minimum possible resource footprint, as required for all modern communications systems. The cross-layer design approach has been proven extremely useful towards this direction, especially for network types such as sensor, mesh and cognitive radios. It allowed exploiting the most appropriate network mechanisms, while bypassing monolithic and outdated requirements when possible. In this chapter, we focus exactly on this aspect of protocol architectures and network management, and present state-of-the-art cross-layer based resource management approaches for MCRNs. Initially, we present three important resource management frameworks, and then we provide directions for developing generic and adaptive resource management protocol architectures for MCRNs, based on cross-layer and component-based design principles emerging from the previously presented approaches. This is the first attempt to highlight the suitability of combining cross-layer with componentbased network design, and this work will also sketch qualitatively the potential benefits of such approach. Finally, some practical considerations for the proposed architecture, along with directions for future research conclude the chapter.

Smart Routing Management Framework Exploiting Dynamic Data Resources of Cross-Layer Design and Machine Learning Approaches for Mobile Cognitive Radio Networks: A Survey

“…The cross-layer design depends on the fact that traditional static models can be revised in many ways to achieve QoS, network performance, and interference mitigation [17]. Also, spectrum management in CRNs needs a cross-layer design mechanism where different layers can be considered in attaining the optimal design and implementation results [22]. Indeed, the routing performance can take advantage of the stack-layer information and make them useful tools to develop routing operations [7].…”

“…The cross-layer is defined as the design that violation of the communication architecture of a reference protocol concerning the architecture of the particular layered [22], [23]. In general, routing protocol in MCRNs is considered the main issue because of the fluctuation availability of spectrum resource and PU activity [6].…”

“…In more specific, all these problems that are referred to in the cross-layer can be negatively affected in routing performance. For that, it is necessary to address these challenges during the design of the cross-layer model [22]. The significant optimizations of cross-layer could cause struggles in a layer.…”

“…Routing is a transfer packet across the network from source to destination [2]. Cognitive radio routing is various from traditional routing in wireless Ad-hoc [22]. In MCRNs, the routing algorithm poses several from critical challenges due to spectrum diversity and PU activities.…”

Exploiting social features for improving cognitive radio infrastructures and social services via combined MRF and back pressure cross-layer resource allocation

“…Our work aligns with this centralized framework and aims at further improving SU operation. Various crosslayer analyses of centralized CRNs have emerged in the literature [12][13][14][15], and compared to them, our work further introduces an MRF-based cross-layered resource allocation approach in a suitable manner for incorporating social information. MRF formulation serves for modeling spatial dependencies and, along with Gibbs sampling, has attracted the attention of diverse research fields due to the inherent capability for addressing a class of problems with large space of possible configurations.…”