A low complexity scheduling for maximizing satisfied users in wireless networks

Uc-Rios, C.E.; Lara-Rodriguez, D.

doi:10.1109/icspcs.2010.5709660

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Several research studies have been carried out to describe and explore algorithms or statistical models to optimize network infrastructures considering user satisfaction. In this section, the work developed by Uc-Rios and Lara-Rodriguez [14], whose objective was to create an algorithm that would guarantee the satisfaction of connectivity of the users of a wireless network, seeking an optimal transmission rate for each user, based on the variable use of the channel and its quality of service. A fascinating investigation was developed by Rugelj et al [15].…”

Section: A Sdwn and Predictive Model A Literature Reviewmentioning

confidence: 99%

Design a Model-Based on Nonlinear Multiple Regression to Predict the Level of User Satisfaction when Optimizing a Traditional WLAN Using SDWN

Hernandez¹,

Ríos²,

Pranolo³

2021

International Journal on Advanced Science, Engineering and Information Technology

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Higher education institutions' wireless networks have different roles and network requirements, ranging from educational platforms and informative consultations. Currently, the inefficient use of network resources, poor wireless planning, and other factors, affect having a robust and stable network platform. Different authors have investigated the various strategies for the optimization of wireless infrastructures. Still, most of the cases studied aim to improve traditional performance variables without considering maximizing the level of user satisfaction, which represents a flaw that this research paper hopes to solve through SDWN and a predictive model. The authors will determine an appropriate methodology to estimate the user's level of satisfaction through an algorithm or predictive model based on nonlinear multiple regression supported on network performance variables, making a characterization of the project's environment analyzing the wireless conditions. The investigation phases will follow the life cycle guidelines defined by the Cisco PPDIOO methodology (Prepare, Plan, Design, Implement, Operate, Optimize). As a result, it is expected that the project will be the beginning of academic research that will help create strategies to optimize the WiFi network of any educational institution to maximize user satisfaction. In short, the optimization process provides the network with differentiating factors through a modular design with variable modification of parameters according to the users' requirements and needs.

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Section: A Sdwn and Predictive Model A Literature Reviewmentioning

confidence: 99%

Design a Model-Based on Nonlinear Multiple Regression to Predict the Level of User Satisfaction when Optimizing a Traditional WLAN Using SDWN

Hernandez¹,

Ríos²,

Pranolo³

2021

International Journal on Advanced Science, Engineering and Information Technology

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“…Considered centralized CRN architecture [22]. over, the work in [13] concentrated on the downlink of a CDMA cellular network and proposed a scheduling algorithm to maximize the number of satisfied users, where a user is satisfied if its packet delay is below a certain threshold.…”

Section: Related Workmentioning

confidence: 99%

Throughput Satisfaction-Based Scheduling for Cognitive Radio Networks

Gözüpek

Eraslan

Alagöz

2012

IEEE Trans. Veh. Technol.

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Opportunistic scheduling algorithms in cognitive radio networks (CRNs) allocate resources by exploiting the variations in channel conditions and spectral activities of primary users. However, most of these scheduling algorithms ignore the per-user throughput requirements. In this paper, we formulate a scheduling problem called maximizing the number of satisfied users (MNSU), which maximizes the number of secondary users that are satisfied in terms of throughput in a centralized CRN. We show that MNSU is NP-hard in the strong sense and cannot be approximated within any constant factor better than 2 unless P = N P . We also prove that MNSU is at least as hard as the max-min fair scheduling problem, which has previously been proven to be a computationally very difficult problem in the literature. We then propose two heuristic algorithms: 1) best first resource assignment and 2) resource assignment with partial backtracking. We demonstrate that our proposed algorithms yield high performance while still achieving low computational complexity.Index Terms-Cognitive radio networks (CRN), dynamic spectrum access (DSA), resource allocation, scheduling.

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Token Bucket Fair Scheduling Algorithm with Adaptive Rate Allocations for Heterogeneous Wireless Networks

AlQahtani

2015

Wireless Pers Commun

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A low complexity scheduling for maximizing satisfied users in wireless networks

Cited by 3 publications

References 17 publications

Design a Model-Based on Nonlinear Multiple Regression to Predict the Level of User Satisfaction when Optimizing a Traditional WLAN Using SDWN

Design a Model-Based on Nonlinear Multiple Regression to Predict the Level of User Satisfaction when Optimizing a Traditional WLAN Using SDWN

Throughput Satisfaction-Based Scheduling for Cognitive Radio Networks

Token Bucket Fair Scheduling Algorithm with Adaptive Rate Allocations for Heterogeneous Wireless Networks

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