QoE-Aware Self-Tuning of Service Priority Factor for Resource Allocation Optimization in LTE Networks

Uthansakul, Peerapong; Anchuen, Patikorn; Uthansakul, Monthippa; Khan, Arfat Ahmad

doi:10.1109/tvt.2019.2952568

Cited by 19 publications

(19 citation statements)

References 32 publications

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“…The self-tuning approach of [36] improves the QoE in multi-tier LTE networks based on parameters from network data traces. Another self-tuning approach [37] computes the priority score in a multi-service environment and allocates the resource blocks of the highest priority score to enhance QoE. Though QoE parameters have gained significant importance in live/running cellular networks [38], considering QoE in network planning is a crucial task as user expectations are changing the way MNOs manage and plan their networks.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Service-Based Network Dimensioning for 5G Networks Assisted by Real Data

2020

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The fifth-generation (5G) of cellular communications is expected to be deployed in the next years to support a wide range of services with different demands of peak data rates, latency and quality of experience (QoE). In this work, we propose a novel approach for radio network dimensioning (RND), named as Heuristic RND (HRND), which uses real open data in the network dimensioning process. This procedure, named as NetDataDrilling, provides the dimensioning target area by means of network data selection and visualization from the existing infrastructure. Moreover, the proposed NetDimensioning heuristic considers the necessary parameters of numerology and bandwidth parts (BWP) supported by New Radio (NR) to provide a balanced network design mediating among the requirements of coverage, capacity, QoE and cost. The proposed HRND is based on the novel quality of experience (QoE) parameter ζ by probabilistically characterizing the 5G radio resource control (rrc) states to ensure the availability of peak data rates for the MNO's required percentage of the time. The simulation results show the fulfilment of QoE and load balancing parameters with significant cost savings compared to the conventional RND methodology.INDEX TERMS 5G, new radio, cellular network dimensioning, network data, capacity model.

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Section: Related Work and Contributionsmentioning

confidence: 99%

Service-Based Network Dimensioning for 5G Networks Assisted by Real Data

2020

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“…Service type Objective Procedure [13] Single-cell Device-to-device (D2D) Video QoE enhancement PC, RA [18] Single-cell Mobile users Video Video adaptation PC, RA [19] Multi-cell Mobile users Video QoE & EE PC, RA [20] Single-cell D2D unlicensed Video + FD QoE aware RA [21] Femto multi-cell Mobile users Video + HTTP + FTP QoE driven RA [22] Single-cell Mobile users Video + VoIP + FD QoE driven PC, RA [23] Single-cell Mobile users Video + VoIP + HTTP QoE aware RA [24] Multi-cell NOMA Vehicular use cases cover a multitude of scenarios spanning from self driving cars, to multimedia utilization on an incar infotainment system, to real time diagnostics, and remote driving. The verticals for 5G vehicular devices are still to be disclosed, while network slicing is ramping up.…”

Section: Reference Network Scenario Ue Typementioning

confidence: 99%

“…In baseline 1 RSU serves all the vehicle and its performance is lower than network slicing, which is because RSU doesn't have high quality V2I links with all the vehicles which leads to lower QoE. The video quality selection algorithm is based on the solution of the optimization problem (23), where the optimization variables are the choice of video quality and the resource block assignment. Fig.…”

Section: Performance Analysismentioning

confidence: 99%

“…QoE enhancements have been studied from various perspectives in the existing literature [13], [18]- [24] and it is summarized in Table I. The works [13], [18], [20], [22], [23] study the QoE enhancement in single-cell downlink networks for services such as video, VoIP, FD, and HTTP, via power control and resource allocation. Furthermore, the QoE enhancement in multi-cell scenarios is studied in [19], [21], [24] for multiple services (web browsing, HTTP, and FTP).…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Video Streaming in Vehicular Networks via Resource Slicing

Khan

Samarakoon

Bennis

2020

IEEE Trans. Veh. Technol.

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Vehicle-to-everything (V2X) communication is a key enabler that connects vehicles to neighboring vehicles, infrastructure and pedestrians. In the past few years, multimedia services have seen an enormous growth and it is expected to increase as more devices will utilize infotainment services in the future i.e. vehicular devices. Therefore, it is important to focus on user centric measures i.e. quality-of-experience (QoE) such as video quality (resolution) and fluctuations therein. In this paper, a novel joint video quality selection and resource allocation technique is proposed for increasing the QoE of vehicular devices. The proposed approach exploits the queuing dynamics and channel states of vehicular devices, to maximize the QoE while ensuring seamless video playback at the end users with high probability.The network wide QoE maximization problem is decoupled into two subparts. First, a network slicing based clustering algorithm is applied to partition the vehicles into multiple logical networks. Secondly, vehicle scheduling and quality selection is formulated as a stochastic optimization problem which is solved using the Lyapunov drift plus penalty method. Numerical results show that the proposed algorithm ensures high video quality experience compared to the baseline. Simulation results also show that the proposed technique achieves low latency and high-reliability communication.

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“…In [20], the authors have presented an improvement in QoE quality in terms of Voice over Internet Protocol (VoIP) services with software-defined networking (SDN) by forwarding data path control and improved coders/decoders (CODECS) performance of VoIP service. The QoE user satisfaction is also predicted using an Artificial Neural Network (ANN) and resource blocks (RBs) with Particle Genetic (PGA) [21,22] to allocate QoS and data precision. As a result, the average QoE satisfaction for each service can be increased.…”

Section: Introductionmentioning

confidence: 99%

Optimal Hybrid Precoding Based QoE for Partially Structured Massive MIMO System

Singh¹,

Goyal²,

Ali³

et al. 2022

Computers, Materials &Amp; Continua

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Precoding is a beamforming technique that supports multi-stream transmission in which the RF chain plays a significant role as a digital precoding at the receiver for wireless communication. The traditional precoding contains only digital signal processing and each antenna connects to each RF chain, which provides high transmission efficiency but high cost and hardware complexity. Hybrid precoding is one of the most popular massive multiple input multiple output (MIMO) techniques that can save costs and avoid using complex hardware. At present, network services are currently in focus with a wide range of traffic volumes. In terms of the Quality of Service (QoS), it is critical that service providers pay a lot of attention to this parameter and its relationship to Quality of Experience (QoE) which is the measurement of the overall level of user satisfaction. Therefore, this paper proposes hybrid precoding of a partially structured system to improve transmission efficiency and allocate resources to provide network services to users for increasing the user satisfaction under power constraints that optimize the quality of baseband precoding and radio frequency (RF) precoding by minimizing alternating algorithms. We focus on the web browsing, video, and Voice over IP (VOIP) services. Also, a Mean Opinion Score (MOS) is employed to measure the level of user satisfaction. The results show that the partially structured system provides a good user satisfaction with the network's services. The partially structured system provides high energy efficiency up to 85%. Considering web service, the partially structured system for 10 users provides MOS at 3.21 which is higher than 1.75 of fully structured system.

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QoE-Aware Self-Tuning of Service Priority Factor for Resource Allocation Optimization in LTE Networks

Cited by 19 publications

References 32 publications

Service-Based Network Dimensioning for 5G Networks Assisted by Real Data

Service-Based Network Dimensioning for 5G Networks Assisted by Real Data

Enhancing Video Streaming in Vehicular Networks via Resource Slicing

Optimal Hybrid Precoding Based QoE for Partially Structured Massive MIMO System

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