Robust provisioning of multicast sessions in cognitive radio networks

Almasoud, Abdullah M.; Kamal, Ahmed E.

doi:10.1109/iwcmc.2014.6906393

Cited by 8 publications

(2 citation statements)

References 17 publications

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“…In other words, if the PU of the currently used channel by the SU becomes active again, the SU must vacate this channel immediately and switch to another available channel [5], [6]. CRNs have unique operation requirements, including spectrum sensing, channel assignment protocols, routing mechanisms, and routing protection [7], [8], [9], [10], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

A Formula for Successful Transmission Probability in Opportunistic Networks Under Memory-Time Correlated Channel Availability

Alnabelsi

2023

IEEE Access

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Dynamic Spectrum Access (DSA) technology in wireless Cognitive Radio Networks (CRNs) provides opportunistic access for unlicensed users, also known as Secondary Users (SUs), which can offer huge bandwidth to enable future wireless communication. Mainly, this technology aims to improve the endto-end throughput by allowing SUs to exploit the licensed channels only when their licensed users, also known as Primary Users (PUs), are not using them. Most existing communication protocols designed for CRNs are based on the assumption that the channel availability time is considered based on a memoryless distribution for PUs arrivals. Unfortunately, this assumption is impractical because the PU channels' activity and availability are memory-time correlated. Worse yet, designing communication protocols for CRNs under this assumption can result in overestimating the Probability of Success (PoS) for SU packet transmissions, resulting in severe degradation in network performance in realistic scenarios. This paper derives a closed-form formula under memory-time correlation for channel availability that quantifies the PoS for SUs' packet transmission in CRNs. This will empower the network designers to get practical expectations about network efficiency rather than the overestimated PoS. Therefore, this work is also useful for emerging wireless networks with multi-hop routing, such as 5G, 6G, vehicular networks, etc., which incorporate DSA techniques. Our numerical and simulation results demonstrate that the PoS is overestimated in most of the literature due to adopting memoryless-based distribution in modeling channels' availability; such overestimation can impact communication protocol decisions, resulting in severe network performance degradation.

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Section: Introductionmentioning

confidence: 99%

A Formula for Successful Transmission Probability in Opportunistic Networks Under Memory-Time Correlated Channel Availability

Alnabelsi

2023

IEEE Access

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“…SU'lar, spektrum algılama işlemi tarafından sağlanan bilgilere dayanarak spektral deliklerin olup olmadığına karar verebilir. Enerji algılama (ED) algılanan enerjiye dayalı olarak PU sinyalini tespit eden tutarlı olmayan bir yöntemdir [30]. ED yöntemi ile mevcut radyo kaynağının enerjisi ölçülür ve önceden tanımlanmış bir eşik seviyesiyle karşılaştırılır.…”

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Lpwan Standartlari Tabanli Bi̇li̇şsel Radyo Ağlari İçi̇n Spektrum Algilama Yaklaşimi

Yalçın

2021

Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi

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Bilişsel radyo ağları (Cognitive Radio Networks, CRNs) kullanılarak güvenilir bir spektrum elde edebilmek günümüz kablosuz iletişim teknolojileri ve Nesnelerin İnterneti (Internet of Things, IoTs) uygulamaları için vazgeçilmez olmuştur. Bu çalışmada, CRN'ler için spektrum algılama yaklaşımında yeni eğilimler incelenmiştir. Düşük güçlü geniş alan ağları (Low Power Wide Area Networks, LPWANs) standartlarından biri olan geniş kapsama (Long Range, LoRa) içerikli CRN'ler ile spektrum algılaması yapılmıştır. Burada amaçlanan, kullanıcıların frekans spektrumlarından maksimum oranda faydalanabilmesini sağlayabilmektir. Daha hassas spektrum algılaması sayesinde haberleşmedeki farklı bant genişliği ve sinyal yayılım faktörlerine bağlı olarak bant genişliği kullanımı verimli hale getirilmiştir. Buna paralel olarak, bant genişliğinde kullanılan miktar artırılarak verimli güç/frekans analizi yapılmıştır.

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An artificial intelligence‐based spectrum sensing methodology for LoRa and cognitive radio networks

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2023

Int J Communication

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The artificial intelligence-based spectrum sensing approach is extremely important in terms of effective bandwidth utilization for low power wide area networks (LPWANs) based on cognitive radio networks (CRNs). Most studies perform spectrum detection with CRNs using optimization or deep neural network methods. However, optimization-based spectrum detection approaches based on current LPWANs are scarce. For this purpose, in this study, a hybrid optimization methodology integrated with CRNs is proposed for LoRa, which is one of the most compatible LPWAN technologies in the Internet of Things (IoTs) recently. In the particle swarm optimization (PSO) part of this hybrid methodology, agent users are created so that secondary users (SUs) could use the licensed band of primary users (PUs) in cognitive radio. On the genetic algorithm side, LoRa error rates are minimized in order to further improve the performance of the proposed method. In this way, effective spectrum sensing is performed in the LoRa network. Various LoRa-CRN experiments have been carried out in the simulation environment, and the probability of detection and false alarm performances have been compared with both theoretical and proposed approaches in terms of quality estimation parameters. It is clear from the results that the proposed methods give successful results for the LoRa-CRNs.

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Robust provisioning of multicast sessions in cognitive radio networks

Cited by 8 publications

References 17 publications

A Formula for Successful Transmission Probability in Opportunistic Networks Under Memory-Time Correlated Channel Availability

A Formula for Successful Transmission Probability in Opportunistic Networks Under Memory-Time Correlated Channel Availability

Lpwan Standartlari Tabanli Bi̇li̇şsel Radyo Ağlari İçi̇n Spektrum Algilama Yaklaşimi

An artificial intelligence‐based spectrum sensing methodology for LoRa and cognitive radio networks

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