QoS-aware Adaptive Resource Management in OFDMA Networks

PhDOne important feature of the future communication network is that users in the network are required to experience a guaranteed high quality of service (QoS) due to the popularity of multimedia applications. This thesis studies QoS-aware radio resource management schemes in different OFDMA network scenarios. Motivated by the fact that in current 4G networks, the QoS provisioning is severely constrained by the availability of radio resources, especially the scarce spectrum as well as the unbalanced traffic distribution from cell to cell, a joint antenna and subcarrier management scheme is proposed to maximise user satisfaction with load balancing. Antenna pattern update mechanism is further investigated with moving users. Combining network densi fication with cloud computing technologies, cloud radio access network (C-RAN) has been proposed as the emerging 5G network architecture consisting of baseband unit (BBU) pool, remote radio heads (RRHs) and fronthaul links. With cloud based information sharing through the BBU pool, a joint resource block and power allocation scheme is proposed to maximise the number of satisfi ed users whose required QoS is achieved. In this scenario, users are served by high power nodes only. With spatial reuse of system bandwidth by network densi fication, users' QoS provisioning can be ensured but it introduces energy and operating effciency issue. Therefore two network energy optimisation schemes with QoS guarantee are further studied for C-RANs: an energy-effective network deployment scheme is designed for C-RAN based small cells; a joint RRH selection and user association scheme is investigated in heterogeneous C-RAN. Thorough theoretical analysis is conducted in the development of all proposed algorithms, and the effectiveness of all proposed algorithms is validated via comprehensive simulations.China Scholarship Counci

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Efficient radio resource management for future generation heterogeneous wireless networks

The heterogeneous deployment of small cells (e.g., femtocells) in the coverage area of the traditional macrocells is a cost-efficient solution to provide network capacity, indoor coverage and green communications towards sustainable environments in the future fifth generation (5G) wireless networks. However, the unplanned and ultra-dense deployment of femtocells with their uncoordinated operations will result in technical challenges such as severe interference, a significant increase in total energy consumption, unfairness in radio resource sharing and inadequate quality of service provisioning. Therefore, there is a need to develop efficient radio resource management algorithms that will address the above-mentioned technical challenges. The aim of this thesis is to develop and evaluate new efficient radio resource management algorithms that will be implemented in cognitive radio enabled femtocells to guarantee the economical sustainability of broadband wireless communications and users' quality of service in terms of throughput and fairness. Cognitive Radio (CR) technology with the Dynamic Spectrum Access (DSA) and stochastic process are the key technologies utilized in this research to increase the spectrum efficiency and energy efficiency at limited interference. This thesis essentially investigates three research issues relating to the efficient radio resource management: Firstly, a self-organizing radio resource management algorithm for radio resource allocation and interference management is proposed. The algorithm considers the effect of imperfect spectrum sensing in detecting the available transmission opportunities to maximize the throughput of femtocell users while keeping interference below pre-determined thresholds and ensuring fairness in radio resource sharing among users. Secondly, the effect of maximizing the energy efficiency and the spectrum efficiency individually on radio resource management is investigated. Then, an energy-efficient radio resource management algorithm and a spectrum-efficient radio resource management algorithm are proposed for green communication, to improve the probabilities of spectrum access and further increase the network capacity for sustainable environments. Also, a joint maximization of the energy efficiency and spectrum efficiency of the overall networks is considered since joint optimization of energy efficiency and spectrum efficiency is one of the goals of 5G wireless networks. Unfortunately, maximizing the energy efficiency results in low performance of the spectrum efficiency and vice versa. Therefore, there is an investigation on how to balance the trade-off that arises when maximizing both the energy efficiency and the spectrum efficiency simultaneously. Hence, a joint energy efficiency and spectrum efficiency trade-off algorithm is proposed for radio resource allocation in ultra-dense heterogeneous networks based on orthogonal frequency division multiple access. Lastly, a joint radio resource allocation with adaptive modulation and coding scheme is proposed to minimize the total transmit power across femtocells by considering the location and the service requirements of each user in the network. The performance of the proposed algorithms is evaluated by simulation and numerical analysis to demonstrate the impact of ultra-dense deployment of femtocells on the macrocell networks. The results show that the proposed algorithms offer improved performance in terms of throughput, fairness, power control, spectrum efficiency and energy efficiency. Also, the proposed algorithms display excellent performance in dynamic wireless environments

Efficient and Virtualized Scheduling for OFDM-Based High Mobility Wireless Communications Objects

Services providers (SPs) in the radio platform technology standard long term evolution (LTE) systems are enduring many challenges in order to accommodate the rapid expansion of mobile data usage. The modern technologies demonstrate new challenges to SPs, for example, reducing the cost of the capital and operating expenditures while supporting high data throughput per customer, extending battery life-per-charge of the cell phone devices, and supporting high mobility communications with fast and seamless handover (HO) networking architecture. In this thesis, a variety of optimized techniques aimed at providing innovative solutions for such challenges are explored. The thesis is divided into three parts. The first part outlines the benefits and challenges of deploying virtualized resource sharing concept. Wherein, SPs achieving a different schedulers policy are sharing evolved network B, allowing SPs to customize their efforts and provide service requirements; as a promising solution for reducing operational and capital expenditures, leading to potential energy savings, and supporting higher peak rates. The second part, formulates the optimized power allocation problem in a virtualized scheme in LTE uplink systems, aiming to extend the mobile devices’ battery utilization time per charge. While, the third part extrapolates a proposed hybrid-HO (HY-HO) technique, that can enhance the system performance in terms of latency and HO reliability at cell boundary for high mobility objects (up to 350 km/hr; wherein, HO will occur more frequent). The main contributions of this thesis are in designing optimal binary integer programmingbased and suboptimal heuristic (with complexity reduction) scheduling algorithms subject to exclusive and contiguous allocation, maximum transmission power, and rate constraints. Moreover, designing the HY-HO based on the combination of soft and hard HO was able to enhance the system performance in term of latency, interruption time and reliability during HO. The results prove that the proposed solutions effectively contribute in addressing the challenges caused by the demand for high data rates and power transmission in mobile networks especially in virtualized resources sharing scenarios that can support high data rates with improving quality of services (QoSs)

Efficient Methods for Resource Allocation in Multi-Antenna Orthogonal Frequency-Division Multiple Access (OFDMA) Systems

Résumé Dans cette thèse, nous proposons une solution au problème d’allocation de ressources d’un système MISO (Multiple Input Multiple Output) – OFDMA (Orthogonal Frequency Division Multiplexing Access) supportant des usagers requérant un débit de transmission minimal. Ce problème peut être modélisé comme une optimisation non-linéaire mixte avec variables entières. Nous nous sommes intéressés dans cette thèse à diverses méthodes permettant la résolution d’un tel problème. La première approche étudiée utilise une méthode hors-ligne et permet d’obtenir une solution quasi-optimale qui peut être utilisée comme références pour évaluer la performance d’algorithmes heuristiques pouvant être réalises en temps réel. Pour ce faire, nous cherchons une allocation réalisable en se basant sur la solution optimale du problème dual. Nous obtenons la fonction duale et trouvons la solution à l’aide d’un algorithme itératif à sous-gradient. Cette solution permet d’obtenir une borne supérieure à la solution optimale. D’autre part, nous développons une heuristique basée sur la solution du problème dual pour obtenir une solution réalisable du problème primaire qui constitue une borne inferieure à la solution optimale. Ces bornes nous permettent d’établir que l’écart de dualité est petit pour les configurations étudiées et elles peuvent servir de référence pour l’évaluation de performances des algorithmes heuristiques. La formulation duale nous fournit aussi une meilleure compréhension du sujet en établissant un lien entre la réalisabilité de l’allocation de ressources et les débits minimaux requis par les usagers. Afin d’obtenir des méthodes de résolution plus pratiques pouvant être réalisées en temps réel, nous proposons deux heuristiques ayant une faible complexité et permettant d’atteindre des performances assez prés des performances optimales. Les performances ainsi obtenues sont légèrement moins bonnes que celles d’autres algorithmes qu’on retrouve dans la littérature, mais supportent une plus grande plage de valeurs de débit minimal tout en réduisant la complexité de l’algorithme d’allocation de ressources de plusieurs ordres de grandeur. L’écart entre la solution trouvée par ces algorithmes heuristiques et la borne supérieure duale est relativement faible. Par exemple, l’écart est de 10.7% en moyenne pour toutes les configurations étudiées. L’augmentation dans la plage de débits minimaux supportes compares avec les méthodes disponibles dans la littérature est de 14.6% en moyenne. Cette amélioration est obtenue en considérant les variables duals de contrainte de débits minimaux dans l’allocation de puissance aux usagers. L’algorithme heuristique proposé sélectionne un ensemble d’usagers pour chaque sous-porteuse, mais contrairement aux autres méthodes proposées précédemment, l’algorithme considère l’ensemble des usagers avec des contraintes de débits minimaux dans la réassignation des sous-porteuses pour s’assurer que le niveau de service requis est rencontre. Suite à la sélection des ensembles d’usagers, un problème d’allocation de puissance convexe est résolu. Des algorithmes permettant de résoudre efficacement et en un temps moindre les problèmes d’assignation des sous-porteuses aux usagers et d’allocation de puissance sont proposées dans cette thèse. Finalement, nous étudions aussi de quelle façon ces algorithmes peuvent être utilises pour résoudre le problème d’allocation de ressources dans une cellule utilisant la technologie LTE (Long Term Evolution)-Advanced. Les méthodes étudiées dans cette thèse font partie d’un nouvel ensemble d’algorithmes nécessaires pour supporter des applications temps réel à haut débit et a l’efficacité spectrale requise dans les prochains réseaux d’accès sans-fil de quatrième génération.----------Abstract In this dissertation, we solve the Resource Allocation (RA) problem of a Multiple Input Single Output (MISO) – Orthogonal Frequency Division Multiplexing Access (OFDMA) sys¬tem supporting minimum rates. This problem can be modelled as a non-linear Mixed Integer Program (NLMIP). We are interested in various kinds of methods to solve this problem. First, our focus is on an off-line method that gives near-optimal solutions that serve as benchmark for more practical methods. For this purpose, we propose a method based on the optimal solution of the dual problem. We obtain a dual function and solve the dual problem through subgradient iterations. Then, we find upper and lower bounds for the optimal solution and verify that the duality gap is small for the system configurations studied. Therefore, the dual optimal serves as a reference for any feasible solution produced by the heuristic methods. The dual formulation also gives a better insight into the problem, as it shows us the relation between the problem’s feasibility and the minimum rate requirements. To obtain more practical methods, we propose two heuristics that have very low com-putational complexity and give performances not far from the optimal. We compare their performance against other methods proposed in the literature and find that they give a somewhat lower performance, but support a wider range of minimum rates while reducing the computational complexity of the algorithm by several orders of magnitude. The gap be¬tween the objective achieved by the heuristics and the upper bound given by the dual optimal is not large. For example, in our experiments this gap is 10.7% averaging over all performed numerical evaluations for all system configurations. The increase in the range of the sup¬ported minimum rates when compared with the method reported in the literature is 14.6% on average. This increase is achieved by considering the rate constraint dual variables in the user power allocation stage. The proposed heuristics select a set of users for each subcarrier, but contrary to other reported methods used to solve the throughput maximization problem, they consider the set of real-time (RT) users to ensure that their minimum rate requirements are met. Then, they solve a power allocation problem for fix subcarrier assignment, which is a convex problem that is simpler to solve. We use efficient algorithms for the subcarrier assignment and power allocation stages to solve the problem much quicker. Finally, we adapt the algorithms to solve the RA problem in a single cell using LTE (Long Term Evolution)–Advanced technology. The methods examined in this dissertation are part of the new set of algorithms needed to support the high rate applications and spectral efficiency required in the wireless access of upcoming 4G networks

Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory

Recently, there has been a growing trend toward ap-plying game theory (GT) to various engineering fields in order to solve optimization problems with different competing entities/con-tributors/players. Researches in the fourth generation (4G) wireless network field also exploited this advanced theory to overcome long term evolution (LTE) challenges such as resource allocation, which is one of the most important research topics. In fact, an efficient de-sign of resource allocation schemes is the key to higher performance. However, the standard does not specify the optimization approach to execute the radio resource management and therefore it was left open for studies. This paper presents a survey of the existing game theory based solution for 4G-LTE radio resource allocation problem and its optimization