Spectrum sharing between public safety and commercial users in 4G-LTE

Shajaiah, Haya; Abdelhadi, Ahmed; Clancy, T. Charles

doi:10.1109/iccnc.2014.6785417

Cited by 30 publications

(22 citation statements)

References 10 publications

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“…whose exponential parts are 1 2K of those in (4). Under power constraint, the end-to-end data rate achieved according to (5) under frequency division are higher than those achieved according to (4) under time division. Under energy constraint, the end-to-end data rate achieved according to (5) under frequency division are identical to those achieved according to (4) under time division with p k and q k replaced by 2Kp k and 2Kq k , respectively.…”

Section: B End-to-end Data Ratementioning

confidence: 89%

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Maximizing Mobile Coverage via Optimal Deployment of Base Stations and Relays

Guo

Grosspietsch

et al. 2016

IEEE Trans. Veh. Technol.

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Deploying relays and/or mobile base stations is a major means of extending the coverage of a wireless network. This paper presents models, analytical results, and algorithms to answer two related questions: The first is where to deploy relays in order to extend the reach from a base station to the maximum; the second is where to deploy a mobile base station and how many relays are needed to reach any point in a given area. Simple time-division and frequency-division scheduling schemes as well as an end-to-end data rate requirement are assumed. An important use case of the results is in the Public Safety Broadband Network, in which deploying relays and mobile base stations is often crucial to provide coverage to an incident scene.

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Section: B End-to-end Data Ratementioning

confidence: 89%

“…Under frequency division, the end-to-end data rate expressed as (5) should be no less than the data rate requirement. The optimization problem to find the optimal distance tuple (d 1 , .…”

Section: Algorithmmentioning

confidence: 99%

Maximizing Mobile Coverage via Optimal Deployment of Base Stations and Relays

Guo

Grosspietsch

et al. 2016

IEEE Trans. Veh. Technol.

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“…To incorporate the carrier aggregation feature, we have introduced a multi-stage resource allocation using carrier aggregation in [9]. In [22] and [23], we present resource allocation with users discrimination algorithms to allocate the eNodeB resources optimally among mobile users with elastic and inelastic traffic. In [24], the authors have presented a radio resource block allocation optimization problem using a utility proportional fairness approach.…”

Section: A Related Workmentioning

confidence: 99%

Robust Resource Allocation With Joint Carrier Aggregation in Multi-Carrier Cellular Networks

Shajaiah

Abdelhadi

Clancy

2018

IEEE Trans. Cogn. Commun. Netw.

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Abstract-In this paper, we present a novel approach for robust optimal resource allocation with joint carrier aggregation to allocate multiple carriers resources optimally among users with elastic and inelastic traffic in cellular networks. We use utility proportional fairness allocation policy, where the fairness among users is in utility percentage of the application running on the user equipment (UE). Each UE is assigned an application utility function based on the type of its application. Our objective is to allocate multiple carriers resources optimally among users subscribing for mobile services. In addition, each user is guaranteed a minimum quality of service (QoS) that varies based on the user's application type. We present a robust algorithm that solves the drawback in the algorithm presented in [1] by preventing the fluctuations in the resource allocation process, in the case of scarce resources, and allocates optimal rates for both high-traffic and low-traffic situations. Our distributed resource allocation algorithm allocates an optimal rate to each user from all carriers in its range while providing the minimum price for the allocated rate. In addition, we analyze the convergence of the algorithm with different network traffic densities and show that our algorithm provides traffic dependent pricing for network providers. Finally, we present simulation results for the performance of our resource allocation algorithm.

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“…To incorporate the carrier aggregation feature and the case of different classes of users, we have introduced a multi-stage resource allocation using carrier aggregation in [9]. Furthermore, in [12] and [13], we presented resource allocation with users discrimination algorithms to allocate a single carrier resources optimally among mobile users running elastic and inelastic traffic. In [24], the authors have presented a radio resource block allocation optimization problem using a utility proportional fairness approach.…”

Section: A Related Workmentioning

confidence: 99%

An Efficient Multi-carrier Resource Allocation with User Discrimination Framework for 5G Wireless Systems

Shajaiah

Abdelhadi

Clancy

2015

Int J Wireless Inf Networks

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Abstract-In this paper, we present an efficient resource allocation with user discrimination framework for 5G Wireless Systems to allocate multiple carriers resources among users with elastic and inelastic traffic. Each application running on the user equipment (UE) is assigned an application utility function. In the proposed model, different classes of user groups are considered and users are partitioned into different groups based on the carriers coverage area. Each user has a minimum required application rate based on its class and the type of its application. Our objective is to allocate multiple carriers resources optimally among users, that belong to different classes, located within the carriers' coverage area. We use a utility proportional fairness approach in the utility percentage of the application running on the UE. Each user is guaranteed a minimum quality of service (QoS) with a priority criterion that is based on user's class and the type of application running on the UE. In addition, we prove the existence of optimal solutions for the proposed resource allocation optimization problem and present a multicarrier resource allocation with user discrimination algorithm. Finally, we present simulation results for the performance of the proposed algorithm.

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Spectrum sharing between public safety and commercial users in 4G-LTE

Cited by 30 publications

References 10 publications

Maximizing Mobile Coverage via Optimal Deployment of Base Stations and Relays

Maximizing Mobile Coverage via Optimal Deployment of Base Stations and Relays

Robust Resource Allocation With Joint Carrier Aggregation in Multi-Carrier Cellular Networks

An Efficient Multi-carrier Resource Allocation with User Discrimination Framework for 5G Wireless Systems

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