A Distributed Consensus-Based Cooperative Spectrum-Sensing Scheme in Cognitive Radios

Abstract

Abstract-In cognitive radio (CR) networks, secondary users can cooperatively sense the spectrum to detect the presence of primary users. In this paper, we propose a fully distributed and scalable cooperative spectrum-sensing scheme based on recent advances in consensus algorithms. In the proposed scheme, the secondary users can maintain coordination based on only local information exchange without a centralized common receiver. Unlike most of the existing decision rules, such as the OR-rule or the 1-out-of-N rule, we use the consensus of secondary users to make the final decision. Simulation results show that the proposed consensus scheme can have significant lower missing detection probabilities and false alarm probabilities in CR networks. It is also demonstrated that the proposed scheme not only has proven sensitivity in detecting the primary user's presence but also has robustness in choosing a desirable decision threshold.

Interference Aware Routing Game for Cognitive Radio Ad-hoc Networks

“…Due to the ability of cognitive radio (CR) to solve the problem of spectrum scarcity, spectrum congestion and underutilization, Cognitive Radio Networks (CRNs) have been recognized as an outstanding technology [1]. Recently, researchers consider lower layers' challenges such as spectrum sensing, sharing, and spectrum mobility in infrastructure-based networks that use a base-station for considering the spectrum information [2]- [4]. Cognitive Radio Ad-Hoc Networks (CRAHNs) as a new class of CRNs without any central entity [5] have been considered recently from different aspects including spectrum sensing, spectrum mobility and the routing issue in the network layer of CRAHNs [6]- [9].…”

“…Cognitive Radio Ad-Hoc Networks (CRAHNs) as a new class of CRNs without any central entity [5] have been considered recently from different aspects including spectrum sensing, spectrum mobility and the routing issue in the network layer of CRAHNs [6]- [9]. As demonstrated in [10], routing challenges in CRAHNs are classified into three main categories: channel-based [5]- [9], host-based [4], [11], and network-based [7], [12], [13] routing. Channel-based challenges are related to the operating environment, such as channel availability and diversity.…”

Consensus based distributed estimation with local-accuracy exchange in dense wireless systems

“…In distributed estimation, consensus algorithm guarantees that the iterative exchange of local statistics lets the network reach a consensus on sufficient statistics, that in turn improves the local estimates. Examples of distributed statistical inference in wireless communications are spectum analysis in cognitive radio [1], (time and/or frequency) synchronization [2], and localization [3].…”

“…The cost of the exchange of reliability of these local estimates at consensus set-up is fairly unexpensive (ranging from p up to p 2 /2 samples/node) if compared to the amount of information exchanged during consensus steps (p samples/node/iteration). Compared to simple consensus, the overhead arising from this preliminary set-up where nodes exchanges their mutual characteristics is worthwhile only if nodes have different degree of accuracy (or large differences in signal-to-noise ratios) to account for, as in the application examples addressed above [1], [2], [3].…”

Cooperative estimation for under-determined linear systems

“…Examples of distributed statistical inference in wireless communications are spectrum analysis in cognitive radio [1], (time and/or frequency) synchronization [2], and localization [3]. Let us consider N networked agents modelled as an undirected graph labeled in V = {1, .…”