Robust Cooperative Spectrum Sensing for Disaster Relief Networks in Correlated Environments
Nuno Pratas, Nicola Marchetti, Neeli Rashmi Prasad, Antonio Rodrigues, and Ramjee Prasad

TL;DR
This paper introduces a cluster-based cooperative spectrum sensing scheme for disaster relief networks that adapts to environmental conditions and node correlations, enhancing spectrum detection accuracy in cognitive radio ad-hoc networks.
Contribution
It proposes both centralized and decentralized adaptive cooperative sensing schemes tailored for disaster relief networks, improving spectrum sensing performance by leveraging environmental and correlation information.
Findings
The proposed scheme outperforms blind Round Robin sensing in accuracy.
Centralized and decentralized approaches can be combined for robustness.
Adaptive sensing schemes improve spectrum estimation in correlated environments.
Abstract
Disaster relief networks are designed to be adaptable and resilient so to encompass the demands of the emergency service. Cognitive Radio enhanced ad-hoc architecture has been put forward as a candidate to enable such networks. Spectrum sensing, the cornerstone of the Cognitive Radio paradigm, has been the focus of intensive research, from which the main conclusion was that its performance can be greatly enhanced through the use of cooperative sensing schemes. To apply the Cognitive Radio paradigm to Ad-hoc disaster relief networks, the design of effective cooperative spectrum sensing schemes is essential. In this paper we propose a cluster based orchestration cooperative sensing scheme, which adapts to the cluster nodes surrounding radio environment state as well as to the degree of correlation observed between those nodes. The proposed scheme is given both in a centralized as well as…
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Taxonomy
TopicsCognitive Radio Networks and Spectrum Sensing · Distributed Sensor Networks and Detection Algorithms · Advanced MIMO Systems Optimization
