Mériem Afif

Vertical Handover Decision Policy Based on the End User's Perceived Quality of Service

The integration of heterogeneous wireless networks allows the users to benefit simultaneously from these radio access technology (RAT) and they can also use an important number of applications. New applications constraints are more complex and may be change dynamically and rapidly under time. The handover mechanism is responsible to guarantee the required quality of service when users roam across different RAT in order to provide an ubiquitous environment. With the diversification of the context metrics allowed by these networks, the level of computationally and complexity has a direct impact on the perceived quality of service especially of the network selection process on application performance. The present work addresses the network selection process, which is probably the most important key of the vertical handover decision mechanism. It proposes a modification of the Simple Additive Weighting called Enhanced-Simple Additive Weighting (E-SAW) for ranking networks that avoid computational cheaper by eliminating network access which does not satisfy a minimum required context. After ranking, in order to avoid bad network selection which does not respond to user requirement, we define handover decision scheme that improves network performances perceived by end users and avoid the processing delay caused by unnecessary handover.

A Gain-Computation Enhancements Resource Allocation for Heterogeneous Service Flows in IEEE 802.16 m Mobile Networks

This paper deals with radio resource allocation in fourth generation (4G) wireless mobile networks based on Orthogonal Frequency Division Multiple Access (OFDMA) as an access method. In IEEE 802.16 m standard, a contiguous method for subchannel construction is adopted in order to reduce OFDMA system complexity. In this context, we propose a new subchannel gain computation method depending on frequency responses dispersion. This method has a crucial role in the resource management and optimization. In a single service access, we propose a dynamic resource allocation algorithm at the physical layer aiming to maximize the cell data rate while ensuring fairness among users. In heterogeneous data traffics, we study scheduling in order to provide delay guaranties to real-time services, maximize throughput of non-real-time services while ensuring fairness to users. We compare performances to recent existing algorithms in OFDMA systems showing that proposed schemes provide lower complexity, higher total system capacity, and fairness among users.

An Adaptive PRB, Power and MCS Allocation Using AMC for MIMO-OFDMA Systems

Long Term Evolution (LTE) is a promising technology for a high communication quality thanks to its radio link flexibility using Adaptive Modulation and Coding (AMC), Multiple Input Multiple Output (MIMO) and Orthogonal Frequency Division Multiple Access (OFDMA) technologies. In this paper, we address the adaptive resource allocation problem in down-link transmission of MIMO-OFDMA systems. We aim at maximizing the total system capacity to meet the 4G-technologies promises. In order to reduce the computational complexity, the original optimization problem is divided into sub-optimal ones. Firstly, a recursive Physical Resource Block (PRB) allocation algorithm is developed where the Effective Signal-to-Interference-plus-Noise Ratio (ESINR) metric is investigated to properly characterize the channel condition. Secondly, a power allocation procedure is performed. As all resource blocks allocated to the same user must use the same Modulation and Coding Scheme (MCS) according to the LTE standard requirements, power optimization sub-problem is divided into three steps: power allocation among PRBs, power allocation among antennas and power allocation among users. Finally, a more effective MCS is obtained, which ensures a better system capacity. Simulation results prove the efficiency of the proposed method to maximize the total system capacity with a low complexity even in the case of loaded systems compared to Minimum-MCS (Min-MCS) and Maximum-SINR (Max-SINR) algorithms.

A vertical handover decision based context awareness guaranteeing the user perceived quality of service

Most typically, trend of future cellular network is to give access to a large number of applications such as: VoIP, videoconferencing, interactive gaming, multimedia services, etc. The integration of heterogeneous wireless networks is designed to improve the network performances, to guarantee high Quality of Service (QoS) and to provide ubiquitous broadband access. Major Quality of Service assessment techniques are based on radio resource measurements and many applications require strict QoS to pursue its connection when the users roam across different radio access technologies. Handover making decision is based on various QoS metrics indicating the network access provided quality but does not necessarily imply that improve the user quality perception. The purpose of this paper is to investigate all type of information that can formed the context-aware which integrate the Quality of Experience (QoE) or the Perceived Quality of Service (PQoS) by the user in making handover decision. We propose a handover making decision algorithm based on the user requirements which reduces the decision delay in network selection by reducing the number of the target PoAs for different class of service features and improve the user perceived quality.

Video Quality Assessment Based on Statistical Selection Approach for QoE Factors Dependency

Quality of Experience (QoE) becomes a topic of utmost eminence for service providers and the major factor in the success of multimedia services. Thus, it is challenging to investigate thoroughly the human side of QoE in order to find out the impact of factors that affect user satisfaction. In this paper, we provide a structured way to build an accurate and objective QoE model. In order to serve this purpose, Principal Component Analysis (PCA) and Analytic Hierarchy Process (AHP) approaches are combined and used to select the factors which have a significant impact on user satisfaction and essential for predicting QoE. Random Forest technique is used as a machine learning method to classify original datasets based on real environment, collected in the form of subjective scores. The results show an efficient estimation of QoE with respect to the five most influencing factors (frame rate, video size, audio rate, resolution and mean bit rate).

Handover Decision in Heterogeneous Networks

The integration of heterogeneous wireless networks allows the mobile users to benefit simultaneously from the radio coverage of different access technologies (RAT: Radio Access Technology) and the diversity of applications. Nowadays, applications are becoming more and more critical in terms of Quality of Service (QoS) and dynamicity. Handover mechanism is responsible of guaranteeing the required quality of service in ubiquitous environment when mobile users roam across different RATs. The diversification of context metrics offered by these different networks has a direct impact on the user perceived quality of service especially during the network selection process. The present work addresses the network selection process, which is the most important stage of the vertical handover decision mechanism. Our proposed algorithm for Vertical Handover decision making is based on the context awareness and the quality of experience (QoE). It integrates the threshold values of sensitive criteria, which are considered as the required metrics in the making decision process used to select the optimal target network. However, we define a decision function that checks the performance of the given networks and identifies the best solution. Performance offered by the chosen network has to be reviewed since the handover is launched to control the quality of service perceived by the end user. Simulation results improve the perceived network performance by avoiding unnecessary handover. In Ubiquitous environment, most Handover decision mechanisms generate a ping pong effect caused by the inefficiency of the network handover metrics which do not consider the required end user performance. As compared to several existing algorithms, our algorithm fits the expected changes in the network performance and the users preferences without wastage of network resources. It reduces the processing delay and the complexity level caused by unnecessary computation over available radio access technologies which do not ensure required connection performance.

Interference Mitigation Through Inter-Cell Interference Coordination Using Virtual PRB Allocation in 4G Networks

This paper proposes a distributed and coordinated resource allocation algorithm in multi-cell MIMO-OFDMA systems. Firstly, each cell classifies mobile users into cell-edge and cell-center users based on a dynamic threshold aiming to avoid throughput loss. Secondly, each cell independently and dynamically allocates physical resource blocks (PRBs) to its cell-center users using a recursive resource allocation algorithm where the effective SINR metric is investigated. After that, each cell aggregates the unused sub-carriers by its cell-center users to construct virtual PRBs. These latter are intended to be allocated to cell-edge users. Here, each cell informs its neighboring cells, via messages exchange, about its unused sub-carriers and their corresponding SINRs. If two or more cells share the same unused sub-carrier, a new collision avoidance algorithm is applied to avoid the inter-cell interference. Then, each cell constructs its virtual PRBs based on radio resource exchange between eNodeBs. After virtual PRB allocation, a power allocation procedure is performed and an adaptive modulation and coding algorithm is executed. Simulation results demonstrate that the proposed scheme permits to enhance the system performance in cell-edge and cell-center region compared to well-known algorithms.

A Recursive Resource Allocation Algorithm for MIMO-OFDMA Systems in Multi-user Context

This paper presents a new resource allocation algorithm for downlink multi-user MIMO-OFDMA systems. The main objective is to maximize the total system capacity and to guarantee the fairness among users with a low complexity. The proposed algorithm is based on a recursive sub-carriers allocation procedure aiming to satisfy Quality of Service (QoS) requirements for each mobile user. Simulation results demonstrate that the proposed algorithm performs well in terms of system capacity and fairness when the number of users in the cell is important closing to the practical case.

Resource allocation based on cross-layer QoS-guaranteed scheduling for multi-service multi-user MIMO---OFDMA systems

Cross-layer strategies for resource allocation in wireless networks are essential to guaranty an efficient utilization of the scarce resource. In this paper, we present an efficient radio resource allocation scheme based on PHY/MAC cross layer design and QoS-guaranteed scheduling for multi-user (MU), multi-service (MS), multi-input multi-output (MIMO) concept, orthogonal frequency division multiple access (OFDMA) systems. It is about a downlink multimedia transmission chain in which the available resources as power and bandwidth, are dynamically allocated according to the system parameters. Among these parameters, we can mention the physical link elements such as channel state information, spectral efficiency and error code corrector rate, and MAC link variables, which correspond to the users QoS requirements and the queue status. Primarily, we use a jointly method which parametrizes these system parameters, according to the total power, and the bit error rate constraints. Secondly, we propose a QoS-guaranteed scheduling that shares the sub-carriers to the users. These users request several type of traffic under throughput threshold constraints. The main objective in this work is to adjust the average throughput per service of each user, according to their needs and likewise to satisfy a great number of connexions. Subsequently, we consider a model of moderated compartmentalization between various classes of services by partitioning the total bandwidth into several parts. Each class of service will occupy a part of the bandwidth and will be transmitted over a maximum number of sub-carriers. The simulation results show that the proposed strategy provides a more interesting performance improvement (in terms of average data rate and user satisfaction) than other existing resource allocation schemes, such as nonadaptive resource allocation strategy. The performances are also analyzed and compared for the two multi-service multi-user MIMO–OFDMA systems; with sub-carriers partitioning and without sub-carriers partitioning.

Novel AHP-based QoE factors' selection approach

Due to the accelerating uptake of multimedia services over Internet, Quality of Experience (QoE) assessment has become a topic of prime importance. While current QoE prediction models focus on technology-centered Quality of Service (QoS) parameters as the main influencing keys on users perception quality, they do not sufficiently address the following question: “Based upon QoS, is it competent to understand the overall QoE?” Two limitations on QoE models have been noticed: (1) lack of factors introducing in assessment models, and (2) studies that examine the impact of each factors on QoE are limited. The need to take a more holistic view of influencing QoE factors has steered researchers towards an in-depth study on the multidimensional aspect of QoE. In order to tackle this challenge and giving the growing importance of understanding QoE and the factors influencing it, our contribution in this paper is to give a solution to get around limits mentioned above. This paper proposes a proper design of selection factor based on the Analytic Hierarchy Process (AHP) derived from a Multi-Criteria Decision Making (MCDM) theory. The result shows that due to our model, we can retrieve a ranking of the influential factors. The relevant goal of this paper is to give a structured process that examines influencing factors on QoE. Future QoE assessment models can benefit from applying our proposed model in order to build a QoE model as realistic as possible.