6533b7dcfe1ef96bd1273590

RESEARCH PRODUCT

Dynamic Flow-Adaptive Spectrum Leasing with Channel Aggregation in Cognitive Radio Networks.

Zhenzhen HuXiang XiaoXiang XiaoLei JiaoFanzi Zeng

subject

Computer sciencechannel aggregating02 engineering and technologyBlocking (statistics)lcsh:Chemical technologyBiochemistryArticleAnalytical Chemistry0203 mechanical engineeringflow-adaptive spectrum leasing0202 electrical engineering electronic engineering information engineeringlcsh:TP1-1185Electrical and Electronic EngineeringInstrumentationVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550business.industryQuality of serviceSpectrum (functional analysis)020302 automobile design & engineering020206 networking & telecommunicationsAtomic and Molecular Physics and OpticsCognitive radioTransmission (telecommunications)Flow (mathematics)cognitive radio networksbusinessCommunication channelComputer network

description

Cognitive radio networks (CRNs), which allow secondary users (SUs) to dynamically access a network without affecting the primary users (PUs), have been widely regarded as an effective approach to mitigate the shortage of spectrum resources and the inefficiency of spectrum utilization. However, the SUs suffer from frequent spectrum handoffs and transmission limitations. In this paper, considering the quality of service (QoS) requirements of PUs and SUs, we propose a novel dynamic flow-adaptive spectrum leasing with channel aggregation. Specifically, we design an adaptive leasing algorithm, which adaptively adjusts the portion of leased channels based on the number of ongoing and buffered PU flows. Furthermore, in the leased spectrum band, the SU flows with access priority employ dynamic spectrum access of channel aggregation, which enables one flow to occupy multiple channels for transmission in a dynamically changing environment. For performance evaluation, the continuous time Markov chain (CTMC) is developed to model our proposed strategy and conduct theoretical analyses. Numerical results demonstrate that the proposed strategy effectively improves the spectrum utilization and network capacity, while significantly reducing the forced termination probability and blocking probability of SU flows.

10.3390/s20133800https://pubmed.ncbi.nlm.nih.gov/32645964