Spatiotemporal Characterization of Users’ Experience in Massive Cognitive Radio Networks

Abstract

The need to capture the actual network traffic condition and fundamental queueing dynamics in a massive cognitive radio network (CRN) is important for proper analysis of the intrinsic effects of spatial distribution while capturing the essential temporal distribution properties of the network. In massive CRN, many users, including primary and secondary users, transmit on scarce spectrum resources. While primary users (PUs) are delay-sensitive users that require prioritized access over secondary users (SUs), carrying out analysis that captures this property becomes imperative if users' service experience is to be satisfactory. This paper presents priority conscious spatiotemporal analysis capable of characterizing users' experience in massive CRN. Users in the primary priority queue were considered to have pre-emptive priory over users in the virtual and secondary priority queues. A Geo/G/1 discrete-time Markov chain queueing system was adopted to characterize both primary and secondary priority queues, while the virtual priority queue was analyzed as part of the secondary priority queue. Using the tools of stochastic geometry and queueing theory, the user's coverage probability was determined while the delay experienced by each class of users in the network was obtained using existing results. Through the obtained delay for each class of users in the network, the corresponding quality of service was also obtained. The results obtained show that the proposed framework is capable of accurately characterizing users' service experience in massive CRN.

A Multi-User Tasks Offloading Scheme for Integrated Edge-Fog-Cloud Computing Environments

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Spatiotemporal D2D Small Cell Allocation and On-Demand Deployment for Microgrids

“…There are several spatiotemporal systemic deployment schemes proposed previously [17], [18], [36]- [40], which have pointed out the limitations of separately handling either spatial deployment or real-time resource allocation. spatiotemporal SINR analysis was conducted for HSCNs, trying to overcome the limitation of modeling bias caused by full buffer assumptions in stochastic geometry based spatial allocation [40].…”

“…For instance, stochastic geometry based algorithms are considered recently as the backbone of spatial HSCN deployment [17]. However, previous works either build based on the assumption of full buffer [15] or statistic-based estimation [18], which fails to handle the real-time QoS optimization. It even deteriorates, when these previous works tackle real-time resource allocation for mirogrid based MEC-SC deployment.…”

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“…Similarly, the average throughput in the secondary network can be obtained by substituting (31) into (28). This is expressed as…”