Radio resource allocation framework for quality of experience optimization in wireless networks

Monteiro, Victor Farias; Sousa, Diego Aguiar; Maciel, Tarcísio F.; Lima, F. Rafael M.; Rodrigues, Emanuel B.; Cavalcanti, Francisco R. P.

doi:10.1109/mnet.2015.7340422

Cited by 23 publications

(34 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In (Fei et al, 2015), the proposed scheduler performs the same first step as the previous solution, i.e., users are served following an MT fashion until all of them have the respective minimum throughput requirement satisfied; afterwards, the remaining resources are allocated in a fair manner such that almost the same quantity is assigned to all users. In (Monteiro et al, 2015), the proposed algorithm serves the users following an MT fashion until a predefined number of users is satisfied by having a QoE equal or greater than a certain threshold (it is assumed that throughput can be mapped into QoE); afterwards, the remaining resources are allocated to the users with the lowest QoE. Rugelj et al (2014) presented a method that searches for the users with the minimum QoE and assigns resources to them; this process is repeated until each user is considered satisfied according to the respective minimum QoE (the authors adopted mapping functions based on throughput plus packet loss regarding video streaming and audio applications, whereas for Web browsing applications the considered relevant parameter was service response time); next, the remaining resources are allocated following an MT fashion, with the proviso that users that achieve a very high satisfaction threshold are excluded from being further served.…”

Section: Other Applicationsmentioning

confidence: 99%

A survey on QoE-oriented wireless resources scheduling

Sousa

Queluz

Rodrigues

2020

Journal of Network and Computer Applications

View full text Add to dashboard Cite

Future wireless systems are expected to provide a wide range of services to more and more users. Advanced scheduling strategies thus arise not only to perform efficient radio resource management, but also to provide fairness among the users. On the other hand, the users' perceived quality, i.e., Quality of Experience (QoE), is becoming one of the main drivers within the schedulers design. In this context, this paper starts by providing a comprehension of what is QoE and an overview of the evolution of wireless scheduling techniques. Afterwards, a survey on the most recent QoE-based scheduling strategies for wireless systems is presented, highlighting the application/service of the different approaches reported in the literature, as well as the parameters that were taken into account for QoE optimization. Therefore, this paper aims at helping readers interested in learning the basic concepts of QoE-oriented wireless resources scheduling, as well as getting in touch with its current research frontier.

show abstract

Section: Other Applicationsmentioning

confidence: 99%

A survey on QoE-oriented wireless resources scheduling

Sousa

Queluz

Rodrigues

2020

Journal of Network and Computer Applications

View full text Add to dashboard Cite

show abstract

“…Considering the previous example, the system operator can determine that the number of satisfied UEs in VoIP calls is greater than the number of satisfied UE that upload personal files to the server in the cloud. The per‐service minimum satisfaction concept was proposed as a system‐level metric in the work of Furuskär and used in many other works in downlink OFDMA. In the work of Lima et al two distinct RRA problems in uplink SC‐FDMA in a multiservice scenario were proposed: the constrained rate maximization (CRM) and unconstrained rate maximization problems that consist in maximizing the total data rate with and without per‐service minimum satisfaction requirements, respectively.…”

Section: State‐of‐the‐art and Main Contributionsmentioning

confidence: 99%

Maximizing energy efficiency in SC‐FDMA uplink with QoS guarantees and user satisfaction

Braga

Lima

Maciel

et al. 2019

Trans Emerging Tel Tech

Self Cite

View full text Add to dashboard Cite

Despite the advent of fifth-generation mobile networks in the next years, the Long-Term Evolution system still have an important role in the mobile communication scenario. Moreover, the development of solutions that fulfill the demand of new multimedia applications as well as provide an efficient use of the energy resources is one of the drivers of future networks. Therefore, in this article, we deal with the total energy efficiency maximization problem subject to minimum satisfaction constraints per service and quality of service in the uplink of Long-Term Evolution using single-carrier frequency-division multiple access. Although the formulated problem is a nonlinear combinatorial optimization problem, we obtain the optimal solution through algebraic manipulations and by using an iterative method. Motivated by the high computational complexity to obtain the optimal solution, we propose a heuristic solution with lower computational complexity. Realistic system-level simulations indicate that the proposed solution presents good performance regarding both outage rate and energy efficiency.Trans Emerging Tel Tech. 2019;30:e3674.wileyonlinelibrary.com/journal/ett

show abstract

“…We compare our proposed algorithm (referred to as PM) with a QoE maximizing algorithm, referred to as OSM [7]. OSM is a RA algorithm that maximizes the user QoE through an iterative procedure, which in each iteration allocates enough resources to satisfy a single user, starting from the user with the highest spectral efficiency towards the user with the lowest.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…power allocation, fairness etc. ), without taking into account the impact of their proposals on economic aspects [6], [7]. Two user-oriented joint subcarrier and Power Allocation (PA) algorithms for OFDMA systems are proposed in [6].…”

Section: Introductionmentioning

confidence: 99%

“…The first algorithm guarantees that all users share the same QoE, whereas the second algorithm provides the trade-off between the appropriate QoE level and the system spectral efficiency. Similarly, a QoE-aware joint RA and PA algorithm with a satisfaction factor that determines the percentage of served users is proposed in [7]. However, there is a limited number of works on RA or scheduling that consider both network and economic aspects.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A QoE-Aware Joint Resource Allocation and Dynamic Pricing Algorithm for Heterogeneous Networks

Trakas¹,

Adelantado²,

Verikoukis³

2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

View full text Add to dashboard Cite

Abstract-The rise of third-party content providers and the introduction of numerous applications has been driving the growth of mobile data traffic in the past few years. The applications' various Quality of Service (QoS) requirements as well as the use of multiple devices per user have increased the traffic heterogeneity, pressing the telecommunications industry to the deployment of dense Heterogeneous Networks (HetNets). At the same time, the content providers' rise has also led to the decrease of the Mobile Network Operators' (MNOs) revenues. Under these circumstances, the MNOs need to guarantee the users' Quality of Experience (QoE) requirements, while ensuring the sustainability of HetNet investments. To this end, we consider a HetNet deployment where MNOs offer a multitude of services with diverse pricing. We propose a heuristic, joint QoE-aware resource allocation and dynamic pricing algorithm with overall user satisfaction constraints to maximize the MNO profit, while providing high QoE. Simulation results show that the proposed algorithm can handle traffic heterogeneity by achieving substantial profit and QoE gains, compared to a state of the art algorithm. Moreover, we demonstrate the benefits of our dynamic pricing scheme and its applicability on other resource allocation algorithms.

show abstract

Radio resource allocation framework for quality of experience optimization in wireless networks

Cited by 23 publications

References 7 publications

A survey on QoE-oriented wireless resources scheduling

A survey on QoE-oriented wireless resources scheduling

Maximizing energy efficiency in SC‐FDMA uplink with QoS guarantees and user satisfaction

A QoE-Aware Joint Resource Allocation and Dynamic Pricing Algorithm for Heterogeneous Networks

Contact Info

Product

Resources

About