1,607 research outputs found
A Survey on QoE-oriented Wireless Resources Scheduling
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.Comment: Revised version: updated according to the most recent related
literature; added references; corrected typo
Adaptive Closed Loop OFDM-Based Resource Allocation Method using Machine Learning and Genetic Algorithm
In this paper, the concept of Machine Learning (ML) is introduced to the
Orthogonal Frequency Division Multiple Access-based (OFDMA-based) scheduler.
Similar to the impact of the Channel Quality Indicator (CQI) on the scheduler
in the Long Term Evolution (LTE), ML is utilized to provide the scheduler with
pertinent information about the User Equipment (UE) traffic patterns, demands,
Quality of Service (QoS) requirements, instantaneous user throughput and other
network conditions. An adaptive ML-based framework is proposed in order to
optimize the LTE scheduler operation. The proposed technique targets multiple
objective scheduling strategies. The weights of the different objectives are
adjusted to optimize the resources allocation per transmission based on the UEs
demand pattern. In addition, it overcomes the trade-off problem of the
traditional scheduling methods. The technique can be used as a generic
framework with any scheduling strategy. In this paper, Genetic Algorithm-based
(GA-based) multi- objective scheduler is considered to illustrate the
efficiency of the proposed adaptive scheduling solution. Results show that
using the combination of clustering and classification algorithms along with
the GA optimizes the GA scheduler functionality and makes use of the ML process
to form a closed loop scheduling mechanism.Comment: 6 page
An Efficient Multi-Carrier Resource Allocation with User Discrimination Framework for 5G Wireless Systems
In this paper, we present an efficient resource allocation with user
discrimination framework for 5G Wireless Systems to allocate multiple carriers
resources among users with elastic and inelastic traffic. Each application
running on the user equipment (UE) is assigned an application utility function.
In the proposed model, different classes of user groups are considered and
users are partitioned into different groups based on the carriers coverage
area. Each user has a minimum required application rate based on its class and
the type of its application. Our objective is to allocate multiple carriers
resources optimally among users, that belong to different classes, located
within the carriers' coverage area. We use a utility proportional fairness
approach in the utility percentage of the application running on the UE. Each
user is guaranteed a minimum quality of service (QoS) with a priority criterion
that is based on user's class and the type of application running on the UE. In
addition, we prove the existence of optimal solutions for the proposed resource
allocation optimization problem and present a multi-carrier resource allocation
with user discrimination algorithm. Finally, we present simulation results for
the performance of the proposed algorithm.Comment: Under Submissio
Dynamic Joint Uplink and Downlink Optimization for Uplink and Downlink Decoupling-Enabled 5G Heterogeneous Networks
The concept of user-centric and personalized service in the fifth generation
(5G) mobile networks encourages technical solutions such as dynamic asymmetric
uplink/downlink resource allocation and elastic association of cells to users
with decoupled uplink and downlink (DeUD) access. In this paper we develop a
joint uplink and downlink optimization algorithm for DeUD-enabled wireless
networks for adaptive joint uplink and downlink bandwidth allocation and power
control, under different link association policies. Based on a general model of
inter-cell interference, we propose a three-step optimization algorithm to
jointly optimize the uplink and downlink bandwidth allocation and power
control, using the fixed point approach for nonlinear operators with or without
monotonicity, to maximize the minimum level of quality of service satisfaction
per link, subjected to a general class of resource (power and bandwidth)
constraints. We present numerical results illustrating the theoretical findings
for network simulator in a real-world setting, and show the advantage of our
solution compared to the conventional proportional fairness resource allocation
schemes in both the coupled uplink and downlink (CoUD) access and the novel
link association schemes in DeUD.Comment: 17 pages, 8 figure
Multiuser Video Streaming Rate Adaptation: A Physical Layer Resource-Aware Deep Reinforcement Learning Approach
We consider a multi-user video streaming service optimization problem over a
time-varying and mutually interfering multi-cell wireless network. The key
research challenge is to appropriately adapt each user's video streaming rate
according to the radio frequency environment (e.g., channel fading and
interference level) and service demands (e.g., play request), so that the
users' long-term experience for watching videos can be optimized. To address
the above challenge, we propose a novel two-level cross-layer optimization
framework for multiuser adaptive video streaming over wireless networks. The
key idea is to jointly design the physical layer optimization-based beamforming
scheme (performed at the base stations) and the application layer Deep
Reinforcement Learning (DRL)-based scheme (performed at the user terminals), so
that a highly complex multi-user, cross-layer, time-varying video streaming
problem can be decomposed into relatively simple problems and solved
effectively. Our strategy represents a significant departure for the existing
schemes where either short-term user experience optimization is considered, or
only single-user point-to-point long-term optimization is considered. Extensive
simulations based on real-data sets show that the proposed cross-layer design
is effective and promising.Comment: 29 pages, 7 figures, 5 table
Traffic offloading in future, heterogeneous mobile networks
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. In order to tackle this challenge, Mobile Network Operators (MNOs) aim to increase their networks' capacity by expanding their infrastructure, deploying more Base Stations (BSs). Particularly, the creation of Heterogeneous Networks (HetNets) and the application of traffic offloading through the dense deployment of low-power BSs, the small cells (SCs), is one promising solution to address the aforementioned explosive data traffic increase.
Due to their financial implementation requirements, which could not be met by the MNOs, the emergence of third parties that deploy small cell networks creates new business opportunities. Thus, the investigation of frameworks that facilitate the implementation of outsourced traffic offloading, the collaboration and the transactions among MNOs and third-party small cell owners, as well as the provision of participation incentives for all stakeholders is essential for the deployment of the necessary new infrastructure and capacity expansion.
The aforementioned emergence of third-party content providers and their applications not only drives the increase in mobile data traffic, but also create new Quality of Service (QoS) as well as Quality of Experience (QoE) requirements that the MNOs need to guarantee for the satisfaction of their subscribers. Moreover, even though the MNOs accommodate this traffic, they do not get any monetary compensation or subsidization for the required capacity expansion. On the contrary, their revenues reduce continuously. To that end, it is necessary to research and design network and economic functionalities adapted to the new requirements, such as QoE-aware Radio Resource Management and Dynamic Pricing (DP) strategies, which both guarantee the subscriber satisfaction and maximization the MNO profit (to compensate the diminished MNOs' revenues and the increasing deployment investment).
Following a thorough investigation of the state-of-the-art, a set of research directions were identified. This dissertation consists of contributions on network sharing and outsourced traffic offloading for the capacity enhancement of MNO networks, and the design of network and economic functions for the sustainable deployment and use of the densely constructed HetNets. The contributions of this thesis are divided into two main parts, as described in the following.
The first part of the thesis introduces an innovative approach on outsourced traffic offloading, where we present a framework for the Multi-Operator Radio Access Network (MORAN) sharing. The proposed framework is based on an auction scheme used by a monopolistic Small Cell Operator (SCO), through which he leases his SC infrastructure to MNOs. As the lack of information on the future offered load and the auction strategies creates uncertainty for the MNOs, we designed a learning mechanism that assists the MNOs in their bid-placing decisions. Our simulations show that our proposal almost maximizes the social welfare, satisfying the involved stakeholders and providing them with participation incentives.
The second part of the thesis researches the use of network and economic functions for MNO profit maximization, while guaranteeing the users' satisfaction. Particularly, we designed a model that accommodates a plethora of services with various QoS and QoE requirements, as well as diverse pricing, that is, various service prices and different charging schemes. In this model, we proposed QoE-aware user association, resource allocation and joint resource allocation and dynamic pricing algorithms, which exploit the QoE-awareness and the network's economic aspects, such as the profit. Our simulations have shown that our proposals gain substantial more profit compared to traditional and state-of-the-art solutions, while providing a similar or even better network performance.El aumento de los proveedores de contenido de terceros y la introducción de numerosas aplicaciones ha impulsado el crecimiento del tráfico de datos en redes móviles en los últimos años. Para hacer frente a este desafío, los operadores de redes móviles (Mobile Network Operators, MNOs) apuntan a aumentar la capacidad de sus redes mediante la expansión de su infraestructura y el despliegue de más estaciones base (BS). Particularmente, la creación de Redes Heterogéneas (Heterogenous Networks, HetNets) y la aplicación de descarga de tráfico a través del despliegue denso de BSs de baja potencia, las células pequeñas (small cells, SCs), es una solución prometedora para abordar el aumento del tráfico de datos explosivos antes mencionado.
Debido a sus requisitos de implementación financiera, que los MNO no pudieron cumplir, la aparición de terceros que implementan redes de células pequeñas crea nuevas oportunidades comerciales. Por lo tanto, la investigación de marcos que faciliten la implementación de la descarga tercerizada de tráfico, la colaboración y las transacciones entre MNOs y terceros propietarios de células pequeñas, así como la provisión de incentivos de participación para todas las partes interesadas esencial para el despliegue de la nueva infraestructura necesaria y la expansión de la capacidad.
La aparición antes mencionada de proveedores de contenido de terceros y sus aplicaciones no solo impulsa el aumento del tráfico de datos móviles, sino también crea nuevos requisitos de calidad de servicio (Quality of Service, QoS) y calidad de la experiencia (Quality of Experience, QoE) que los operadores de redes móviles deben garantizar para la satisfacción de sus suscriptores. Además, a pesar de que los operadores de redes móviles adaptan este tráfico, no obtienen ninguna compensación monetaria o subsidio por la expansión de capacidad requerida. Por el contrario, sus ingresos se reducen continuamente.
Para ello, es necesario investigar y diseñar funcionalidades económicas y de red adaptadas a los nuevos requisitos, tales como las estrategias QoE-conscientes de gestión de recursos de radio y de precios dinámicos (Dynamic Pricing, DP), que garantizan la satisfacción del abonado y la maximización de la ganancia de operador móvil (para compensar los ingresos de los MNOs disminuidos y la creciente inversión de implementación).
Después de una investigación exhaustiva del estado del arte, se identificaron un conjunto de direcciones de investigación. Esta disertación consiste en contribuciones sobre el uso compartido de redes y la descarga tercerizada de tráfico para la mejora de la capacidad de redes MNO, y el diseño de funciones económicas y de red para el despliegue y uso sostenible de las HetNets densamente construidas. Las contribuciones de esta tesis se dividen en dos partes principales, como se describe a continuación.
La primera parte de la tesis presenta un enfoque innovador sobre la descarga subcontratada de tráfico, en el que presentamos un marco para el uso compartido de la red de acceso de radio de múltiples operadores (Multi-Operator RAN, MORAN). El marco propuesto se basa en un esquema de subasta utilizado por un operador monopólico de celda pequeña (Small Cell Operator, SCO), a través del cual arrienda su infraestructura SC a MNOs. Como la falta de información sobre la futura carga de red y las estrategias de subasta creaban incertidumbre para los MNO, diseñamos un mecanismo de aprendizaje que asiste a los MNO en sus decisiones de colocación de pujas. Nuestras simulaciones muestran que nuestra propuesta casi maximiza el bienestar social, satisfaciendo a las partes interesadas involucradas y proporcionándoles incentivos de participación.
La segunda parte de la tesis investiga el uso de las funciones económicas y de red para la maximización de los beneficios de los MNOs, al tiempo que garantiza la satisfacción de los usuarios. Particularmente, diseñamos un modelo que acomoda una gran cantidad de servicios con diversos requisitos de QoS y QoE, tanto como diversos precios, es decir, varios precios de servicio y diferentes esquemas de cobro. En este modelo, propusimos algoritmos QoE-conscientes para asociación de usuarios, asignación de recursos y conjunta asignación de recursos y de fijación dinámica de precios, que explotan la conciencia de QoE y los aspectos económicos de la red, como la ganancia. Nuestras simulaciones han demostrado que nuestras propuestas obtienen un beneficio sustancial en comparación con las soluciones tradicionales y del estado del arte, a la vez que proporcionan un rendimiento de red similar o incluso mejor.Postprint (published version
Greedy-Knapsack Algorithm for Optimal Downlink Resource Allocation in LTE Networks
The Long Term Evolution (LTE) as a mobile broadband technology supports a
wide domain of communication services with different requirements. Therefore,
scheduling of all flows from various applications in overload states in which
the requested amount of bandwidth exceeds the limited available spectrum
resources is a challenging issue. Accordingly, in this paper, a greedy
algorithm is presented to evaluate user candidates which are waiting for
scheduling and select an optimal set of the users to maximize system
performance, without exceeding available bandwidth capacity. The
greedy-knapsack algorithm is defined as an optimal solution to the resource
allocation problem, formulated based on the fractional knapsack problem. A
compromise between throughput and QoS provisioning is obtained by proposing a
class-based ranking function, which is a combination of throughput and QoS
related parameters defined for each application. The simulation results show
that the proposed method provides high performance in terms of throughput, loss
and delay for different classes of QoS over the existing ones, especially under
overload traffic.Comment: Wireless Networks, 201
DR9.3 Final report of the JRRM and ASM activities
Deliverable del projecte europeu NEWCOM++This deliverable provides the final report with the summary of the activities carried out in NEWCOM++ WPR9, with a particular focus on those obtained during the last year. They address on the one hand RRM and JRRM strategies in heterogeneous scenarios and, on the other hand, spectrum management and opportunistic spectrum access to achieve an efficient spectrum usage. Main outcomes of the workpackage as well as integration indicators are also summarised.Postprint (published version
Scheduling for VoLTE: Resource Allocation Optimization and Low-Complexity Algorithms
We consider scheduling and resource allocation in long-term evolution (LTE)
networks across voice over LTE (VoLTE) and best-effort data users. The
difference between these two is that VoLTE users get scheduling priority to
receive their required quality of service. As we show, strict priority causes
data services to suffer. We propose new scheduling and resource allocation
algorithms to maximize the sum- or proportional fair (PF) throughout amongst
data users while meeting VoLTE demands. Essentially, we use VoLTE as an example
application with both a guaranteed bit-rate and strict application-specific
requirements. We first formulate and solve the frame-level optimization problem
for throughput maximization; however, this leads to an integer problem coupled
across the LTE transmission time intervals (TTIs). We then propose a TTI-level
problem to decouple scheduling across TTIs. Finally, we propose a heuristic,
with extremely low complexity. The formulations illustrate the detail required
to realize resource allocation in an implemented standard. Numerical results
show that the performance of the TTI-level scheme is very close to that of the
frame-level upper bound. Similarly, the heuristic scheme works well compared to
TTI-level optimization and a baseline scheduling algorithm. Finally, we show
that our PF optimization retains the high fairness index characterizing
PF-scheduling
A Particle Filtering Approach for Enabling Distributed and Scalable Sharing of DSA Network Resources
Handling the massive number of devices needed in numerous applications such
as smart cities is a major challenge given the scarcity of spectrum resources.
Dynamic spectrum access (DSA) is seen as a potential candidate to support the
connectivity and spectrum access of these devices. We propose an efficient
technique that relies on particle filtering to enable distributed resource
allocation and sharing for large-scale dynamic spectrum access networks. More
specifically, we take advantage of the high tracking capability of particle
filtering to efficiently assign the available spectrum and power resources
among cognitive users. Our proposed technique maximizes the per-user throughput
while ensuring fairness among users, and it does so while accounting for the
different users' quality of service requirements and the channel gains'
variability. Through intensive simulations, we show that our proposed approach
performs well by achieving high overall throughput while improving user's
fairness under different objective functions. Furthermore, it achieves higher
performance when compared to state-of-the-art techniques
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