Cloud-Enabled Geo-Location Based Qos-Centric Spectrum And Channel Allocation Framework For Cognitive Radio Networks

Abstract

The rapid expansion of the wireless communication systems is increasing the demand for an efficient spectrum utilization. Conventional fixed spectrum allocation policies are often causing the spectrum scarcity even when the significant portions of the spectrum are remaining underutilized. Cognitive Radio Networks (CRNs) is addressing this challenge by allowing secondary users in opportunistically accessing the unused spectrum without interfering with the primary users. However, the dynamic and heterogeneous nature of the wireless environments is creating the significant challenges in maintaining the Quality of Service (QoS). The cloud computing and geo-location awareness is providing newer opportunities for an intelligent spectrum management because this is supporting the centralized monitoring and the data processing capabilities. Despite significant advancement in the cognitive radio research, where various limitations are remaining in conventional spectrum allocation techniques. Many of the methods are focussing primarily on maximizing the throughput without considering the influence of the channel availability and interference. In this paper, we propose a cloud-enabled geo-location with the QoS-centric spectrum and channel allocation framework for a cognitive radio networks. The proposed architecture is combining four cooperative modules that is including the spectrum sensing, resource awareness analysis, QoS-centric channel allocation, and policy-controlled spectrum access.  The spectrum sensing module is monitoring continuously the licensed and unlicensed frequency bands, and it is reporting the channel availability. The resource awareness layer is aggregating the traffic demand, the node state information, the channel quality indicators, and the interference levels that are processed within the cloud platform. A QoS-centric channel allocation mechanism is evaluating the metrics, and it is assigning the priority scores to the available channels. The spectrum access controller is verifying the regulatory policies and dynamically is reallocating the channels when the primary user activity is detected. The proposed method is achieving a spectrum utilization efficiency of 94.3%, which is performing better than the conventional allocation approaches. The system is achieving a QoS satisfaction ratio of 92.6%, which is indicating the reliable service delivery for a heterogeneous user requirement.