Resource Assignment for Rate Maximization With QoS Guarantees in Multiservice Wireless Systems

Lima, F. Rafael M.; Maciel, Tarcísio F.; Freitas, Walter C.; Cavalcanti, Francisco R. P.

doi:10.1109/tvt.2012.2183905

Cited by 32 publications

(72 citation statements)

References 17 publications

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“…In the following analysis, we compare our proposed algorithm (RMEC) with the optimal solution, obtained by solving (12), and with the RAISES heuristic provided by [19]. The comparisons are performed in terms of outage and total system rate.…”

Section: A Scenario Modelingmentioning

confidence: 99%

“…In order to compare the results of our algorithm with those provided in [19], we convert the QoS into QoE metrics, using the function φ u (·).…”

Section: A Scenario Modelingmentioning

confidence: 99%

“…However, the network operator, in general, requires that at least a certain fraction of the users be satisfied per service due to resources scarcity and/or economic reasons [17], [18]. In this context, in [19] the problem of maximizing the total system rate considering a multi-service scenario has been analyzed, where each service must have at least a certain number of users with their QoS requirement satisfied. The users' QoS is measured in terms of their individual throughput and the requirement is defined by the service the user subscribed to.…”

Section: Introductionmentioning

confidence: 99%

“…Note that our problem extends the problem studied in [19] since we consider a more holistic manner of measuring the user satisfaction, i.e., QoE instead of QoS. We summarize our main contributions as:…”

Section: Introductionmentioning

confidence: 99%

“…We highlight that to the best of our knowledge, this problem was not considered in the previous literature; • We manage to reformulate the resource allocation problem as an ILP and solve it using standard algorithms; • We propose a low-complexity suboptimal solution that has near optimal performance and presents high scalability in terms of the problem inputs. Moreover, differently of [19], our algorithm also treats unfeasible instances of the problem producing near feasible solutions. The rest of this paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Resource Management for Rate Maximization with QoE Provisioning in Wireless Networks

Sousa¹,

Monteiro²,

Maciel³

et al. 2016

JCIS

Self Cite

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Abstract-In this work, we study the problem of allocating resources in a multi-service cellular network aiming at maximizing the total system rate while providing suitable Quality of Experience (QoE) to the network users. In our formulation, we try to satisfy at least a certain number of users per service plan, which is an important constraint from the mobile network operators' perspective. We manage to reformulate this nonlinear optimization problem as an Integer Linear Problem (ILP), that can be solved by standard methods. However, due to the exponentially high complexity to solve large instances of this problem, we propose and evaluate a suboptimal algorithm with a much lower complexity, called Rate Maximization under Experience Constraints (RMEC), whose main idea is to divide the problem into three smaller subproblems with reduced complexity. By means of computational simulations, we show that our proposed algorithm presents a near optimal performance and outperforms the state-of-art solution of the literature.

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Section: A Scenario Modelingmentioning

confidence: 99%

“…In order to compare the results of our algorithm with those provided in [19], we convert the QoS into QoE metrics, using the function φ u (·).…”

Section: A Scenario Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Resource Management for Rate Maximization with QoE Provisioning in Wireless Networks

Sousa¹,

Monteiro²,

Maciel³

et al. 2016

JCIS

Self Cite

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Rate maximization in full‐duplex systems: Gains and complexity of employing adaptive power allocation in downlink and/or uplink

Braga

Lima

Maciel

et al. 2021

Trans Emerging Tel Tech

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The next generations of mobile communications should deal with a set of new use cases, leading to a higher demand for spectral efficiency. Full-duplex (FD) arises as a promising technology since it has the potential to double the system capacity without additional spectrum. However, FD networks are hindered by self-interference (SI) and co-channel interference caused by the reuse of frequency resources. Therefore, radio resource allocation (RRA) in FD networks by means of user pairing, frequency assignment, and power allocation are of utmost importance in order to improve spectral/energy efficiency and ensure quality of service (QoS). Although previous works investigated RRA to improve spectral

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Capacity, Fairness, and QoS Trade-Offs in Wireless Networks with Applications to LTE

Rodrigues

Lima

Casadevall

et al. 2013

Resource Allocation and MIMO for 4G and Beyond

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Resource Assignment for Rate Maximization With QoS Guarantees in Multiservice Wireless Systems

Cited by 32 publications

References 17 publications

Resource Management for Rate Maximization with QoE Provisioning in Wireless Networks

Resource Management for Rate Maximization with QoE Provisioning in Wireless Networks

Rate maximization in full‐duplex systems: Gains and complexity of employing adaptive power allocation in downlink and/or uplink

Capacity, Fairness, and QoS Trade-Offs in Wireless Networks with Applications to LTE

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