QoS Variance Aware Spectrum Sensing and Allocation Strategy for Cognitive Radio Wireless Mesh Networks

QoS (Quality of Service) aware spectrum sensing and channel allocation in cognitive radio wireless mesh networks is a continuous practice due to the divergent scope of communication in wireless mesh networks. Henceforth the current research is moving in a direction to find effective solutions towards QoS aware spectrum sensing and channel allocation. But all of these solutions are specific to one or two QoS factors. According to the real-time practices the QoS assessment by one or two factors is impractical

Reliable Broadcast over Cognitive Radio Networks: A Bipartite Graph-Based Algorithm

Cognitive radio (CR) is a promising technology that aims to enhance the spectrum utilisation by enabling unlicenced users to opportunistically use the vacant spectrum bands assigned to licenced users. Broadcasting is considered as a fundamental operation in wireless networks, as well as in cognitive radio networks (CRNs). The operation of most network protocols in the ad hoc network depends on broadcasting control information from neighbouring nodes. In traditional single-channel or multichannel ad hoc networks, due to uniform channel availability, broadcasting is easily implemented as nodes are tuned to a single common channel. On the contrary, broadcasting in CR ad hoc networks is both a challenging and complex task. The complexity emerges from the fact that different CR users might acquire different channels at different times. Consequently, this partitions the network into different clusters. In this chapter, the problem of broadcasting in ad hoc CR networks is presented, current solutions for the problem are discussed and an intelligent solution for broadcasting based on graph theory to connect different local topologies is developed

A survey of measurement-based spectrum occupancy modeling for cognitive radios

Spectrum occupancy models are very useful in cognitive radio designs. They can be used to increase spectrum sensing accuracy for more reliable operation, to remove spectrum sensing for higher resource usage efficiency, or to select channels for better opportunistic access, among other applications. In this survey, various spectrum occupancy models from measurement campaigns taken around the world are investigated. These models extract different statistical properties of the spectrum occupancy from the measured data. In addition to these models, spectrum occupancy prediction is also discussed, where autoregressive and/or moving-average models are used to predict the channel status at future time instants. After comparing these different methods and models, several challenges are also summarized based on this survey

A Belief-Based Decision-Making Framework for Spectrum Selection in Cognitive Radio Networks

This paper presents a comprehensive cognitive management framework for spectrum selection in cognitive radio (CR) networks. The framework uses a belief vector concept as a means to predict the interference affecting the different spectrum blocks (SBs) and relies on a smart analysis of the scenario dynamicity to properly determine an adequate observation strategy to balance the tradeoff between achievable performance and measurement requirements. In this respect, the paper shows that the interference dynamics in a given SB can be properly characterized through the second highest eigenvalue of the interference state transition matrix. Therefore, this indicator is retained in the proposed framework as a relevant parameter to drive the selection of both the observation strategy and spectrum selection decision-making criterion. This paper evaluates the proposed framework to illustrate the capability to properly choose among a set of possible observation strategies under different scenario conditions. Furthermore, a comparison against other state-of-the-art solutions is presented