A flexible mid-term frequency domain scheduler for resource allocation in HetNets based on the SINR requested by users

Pachón, Álvaro; García-Palomares, Ubaldo M.; Cadavid, Andrés Navarro

doi:10.1016/j.comnet.2015.01.004

Cited by 1 publication

(2 citation statements)

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“…We also demonstrate the aforementioned weakness of FO-01, which tries to improve conditions for a single user; OF-01 ( 12 ) presents the highest level of users with degraded service, a condition that OF-02 improves upon. Model 2 ( 14 ), with its function OF-02, improves significantly upon previous results presented by the authors in [61] , reaching higher levels of full satisfaction, lower percentages of users with degraded or denied service, and higher fulfillment levels with respect to the achieved throughput. Fig.…”

Section: Experiments 5: Objective Function For the Optimization Modelsupporting

confidence: 70%

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Mid-Term frequency domain scheduler for resource allocation in wireless mobile communications systems

Pachón

García-Palomares

2017

Computer Communications

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This article approaches the dynamic resource allocation problem for the downlink of a wireless mobile communication system (WMCS). The article defines the architecture and functions of the global resource scheduler, as well as the quality index for scheduling, the signal to interference-plus-noise ratio (SINR). The proposed approach divides the scheduling task into two components: a distributed one, with local, short-term scope; and a centralized one, with global, medium-term scope. The optimization model considers a set of slack variables for guaranteeing feasibility. This allows the service provider to fully satisfy the users' service demands. The model type is mixed, non-linear, which demands large computational power for an exact solution. So an approximate strategy is used, in order to decouple the search space

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Section: Experiments 5: Objective Function For the Optimization Modelsupporting

confidence: 70%

“…The slack variable may be interpreted as an index giving information about the amount of improvement (when > 0) or deterioration (when < 0) the user experiments in the best/worst system state. Model 1 ( 12 ), was proposed by Pachón et al [61,62] .…”

Section: Modelmentioning

confidence: 99%