A Spectrum Decision Framework for Cognitive Radio Networks

Abstract

Abstract-Cognitive radio networks have been proposed as a solution to both spectrum inefficiency and spectrum scarcity problems. However, they face to a unique challenge based on the fluctuating nature of heterogeneous spectrum bands as well as the diverse service requirements of various applications. In this paper, a spectrum decision framework is proposed to determine a set of spectrum bands by considering the application requirements as well as the dynamic nature of spectrum bands. To this end, first, each spectrum is characterized by jointly considering primary user activity and spectrum sensing operations. Based on this, a minimum variancebased spectrum decision is proposed for real-time applications, which minimizes the capacity variance of the decided spectrum bands subject to the capacity constraints. For best-effort applications, a maximum capacity-based spectrum decision is proposed where spectrum bands are decided to maximize the total network capacity. Moreover, a dynamic resource management scheme is developed to coordinate the spectrum decision adaptively dependent on the time-varying cognitive radio network capacity. Simulation results show that the proposed methods provide efficient bandwidth utilization while satisfying service requirements.

Channel Selection scheme for Cooperative Routing protocols in Cognitive Radio Networks

“…However, Salameh et al, in [21], sought to maximize the achievable capacity. A brute force search for the best channel, according to different metrics, has been proposed in other publications [22], [23]. Lee et al, in [22], aimed at choosing the ones that minimize the end-to-end delay for all connections.…”

“…A brute force search for the best channel, according to different metrics, has been proposed in other publications [22], [23]. Lee et al, in [22], aimed at choosing the ones that minimize the end-to-end delay for all connections. Kim et al, in [23], chose the channel that is not congested with highly-active PUs.…”

Surrogate modeling based cognitive decision engine for optimization of WLAN performance

“…The ranking is done by characterizing the channel activities and making estimations of the capacity accordingly. In particular, the authors in [15] propose a spectrum decision framework for cognitive radio networks which addresses QoS management of the secondary users in response to certain events such as appearance of a primary user or degradation of the QoS. Thus their proposed framework not only accounts for consideration of primary users, but also maintains the QoS delivered to the secondary users by making spectrum decision according to the channel activities.…”

A cognitive QoS management framework for WLANs

“…For instance, in [15], the authors derive a physical model for the throughput of WiMax networks based on monitoring physical layer parameter carrier to interference and noise ratio (CINR). In particular, the authors in [16] propose a spectrum decision framework for cognitive radio networks which addresses QoS management of the secondary users in response to certain events such as appearance of a primary user or degradation of the QoS. Thus, their proposed framework not only accounts for consideration of primary users but also maintains the QoS delivered to the secondary users by making spectrum decision according to the channel activities.…”

“…Thus, their proposed framework not only accounts for consideration of primary users but also maintains the QoS delivered to the secondary users by making spectrum decision according to the channel activities. The current paper is therefore a logical continuance and extension of [16] where we share a common architecture for tackling interference and optimizing the QoS. We extend the concept by bringing cognitive radio techniques for QoS management to the ISM band WLANs as well as incorporating radio environment maps to the framework.…”