Radio Resource Management in LTE-Advanced Systems with Carrier Aggregation

In order to meet the ever-increasing demand for wireless broadband services from fast growing mobile users, the Long Term Evolution -Advanced (LTE-A) standard has been proposed to effectively improve the system capacity and the spectral efficiency for the fourth-generation (4G) wireless mobile communications. Many advanced techniques are incorporated in LTE-A systems to jointly ameliorate system performance, among which Carrier Aggregation (CA) is considered as one of the most promising improvements that has profound significance even in the upcoming 5G era. Component carriers (CCs) from various portions of the spectrum are logically concatenated to form a much larger virtual band, resulting in remarkable boosted system capacity and user data throughput. However, the unique features of CA have posed many emerging challenges as well as span-new opportunities on the Radio Resource Management (RRM) in the LTE-A systems. First, although multi-CC transmission can bring higher throughput, it may incur more intensive interference for each CC and more power consumption for users. Thus the performance gain of CA under different conditions needs fully evaluating. Besides, as CA offers flexible CC selection and cross-CC load balancing and scheduling, enhanced RRM strategies should be designed to further optimize the overall resource utilization. In addition, CA enables the frequency reuse on a CC resolution, adding another dimension to inter-cell interference management in heterogeneous networks (HetNets). New interference management mechanisms should be designed to take the advantage of CA. Last but not least, CA empowers the LTE-A systems to aggregate the licensed spectrum with the unlicensed spectrum, thus offering a capacity surge. Yet how to balance the traffic between licensed and unlicensed spectrum and how to achieve a harmony coexistence with other unlicensed systems are still open issues. To this end, the dissertation emphasizes on the new functionalities introduced by CA to optimize the RRM performance in LTE-A systems. The main objectives are four-fold: 1) to fully evaluate the benefits of CA from different perspectives under different conditions via both theoretical analysis and simulations; 2) to design cross-layer CC selection, packet scheduling and power control strategies to optimize the target performance; 3) to analytically model the interference of HetNets with CA and propose dynamic interference mitigation strategies in a CA scenario; and 4) to investigate the impact of LTE transmissions on other unlicensed systems and develop enhanced RRM mechanisms for harmony coexistence. To achieve these objectives, we first analyze the benefits of CA via investigating the user accommodation capabilities of the system in the downlink admission control process. The LTE-A users with CA capabilities and the legacy LTE users are considered. Analytical models are developed to derive the maximum number of users that can be admitted into the system given the user QoS requirements and traffic features. The results show that with only a slightly higher spectrum utilization, the system can admit as much as twice LTE-A users than LTE users when the user traffic is bursty. Second, we study the RRM in the single-tier LTE-A system and propose a cross-layer dynamic CC selection and power control strategy for uplink CA. Specifically, the uplink power offset effects caused by multi-CC transmission are considered. An estimation method for user bandwidth allocation is developed and a combinatorial optimization problem is formulated to improve the user throughput via maximizing the user power utilization. Third, we explore the interference management problem in multi-tier HetNets considering the CC-resolution frequency reuse. An analytical model is devised to capture the randomness behaviors of the femtocells exploiting the stochastic geometry theory. The interaction between the base stations of different tiers are formulated into a two-level Stackelberg game, and a backward induction method is exploited to obtain the Nash equilibrium. Last, we focus on the mechanism design for licensed and unlicensed spectrum aggregation. An LTE MAC protocol on unlicensed spectrum is developed considering the coexistence with the Wi-Fi systems. The protocol captures the asynchronous nature of Wi-Fi transmissions in time-slotted LTE frame structure and strike a tunable tradeoff between LTE and Wi-Fi performance. Analytical analysis is also presented to reveal the essential relation among different parameters of the two systems. In summary, the dissertation aims at fully evaluating the benefits of CA in different scenarios and making full use of the benefits to develop efficient and effective RRM strategies for better LTE-Advanced system performance

Optimisation de la gestion des interférences inter-cellulaires et de l'attachement des mobiles dans les réseaux cellulaires LTE

Driven by an exponential growth in mobile broadband-enabled devices and a continue dincrease in individual data consumption, mobile data traffic has grown 4000-fold over the past 10 years and almost 400-million-fold over the past 15 years. Homogeneouscellular networks have been facing limitations to handle soaring mobile data traffic and to meet the growing end-user demand for more bandwidth and betterquality of experience. These limitations are mainly related to the available spectrumand the capacity of the network. Telecommunication industry has to address these challenges and meet exploding demand. At the same time, it has to guarantee a healthy economic model to reduce the carbon footprint which is caused by mobile communications.Heterogeneous Networks (HetNets), composed of macro base stations and low powerbase stations of different types, are seen as the key solution to improve spectral efficiency per unit area and to eliminate coverage holes. In such networks, intelligent user association and interference management schemes are needed to achieve gains in performance. Due to the large imbalance in transmission power between macroand small cells, user association based on strongest signal received is not adapted inHetNets as only few users would attach to low power nodes. A technique based onCell Individual Offset (CIO) is therefore required to perform load balancing and to favor some Small Cell (SC) attraction against Macro Cell (MC). This offset is addedto users’ Reference Signal Received Power (RSRP) measurements and hence inducing handover towards different eNodeBs. As Long Term Evolution (LTE) cellular networks use the same frequency sub-bands, mobile users may experience strong inter-cellxv interference, especially at cell edge. Therefore, there is a need to coordinate resource allocation among the cells and minimize inter-cell interference. To mitigate stronginter-cell interference, the resource, in time, frequency and power domain, should be allocated efficiently. A pattern for each dimension is computed to permit especially for cell edge users to benefit of higher throughput and quality of experience. The optimization of all these parameters can also offer gain in energy use. In this thesis,we propose a concrete versatile dynamic solution performing an optimization of user association and resource allocation in LTE cellular networks maximizing a certainnet work utility function that can be adequately chosen. Our solution, based on gametheory, permits to compute Cell Individual Offset and a pattern of power transmission over frequency and time domain for each cell. We present numerical simulations toillustrate the important performance gain brought by this optimization. We obtain significant benefits in the average throughput and also cell edge user through put of40% and 55% gains respectively. Furthermore, we also obtain a meaningful improvement in energy efficiency. This work addresses industrial research challenges and assuch, a prototype acting on emulated HetNets traffic has been implemented.Conduit par une croissance exponentielle dans les appareils mobiles et une augmentation continue de la consommation individuelle des données, le trafic de données mobiles a augmenté de 4000 fois au cours des 10 dernières années et près de 400millions fois au cours des 15 dernières années. Les réseaux cellulaires homogènes rencontrent de plus en plus de difficultés à gérer l’énorme trafic de données mobiles et à assurer un débit plus élevé et une meilleure qualité d’expérience pour les utilisateurs.Ces difficultés sont essentiellement liées au spectre disponible et à la capacité du réseau.L’industrie de télécommunication doit relever ces défis et en même temps doit garantir un modèle économique pour les opérateurs qui leur permettra de continuer à investir pour répondre à la demande croissante et réduire l’empreinte carbone due aux communications mobiles. Les réseaux cellulaires hétérogènes (HetNets), composés de stations de base macro et de différentes stations de base de faible puissance,sont considérés comme la solution clé pour améliorer l’efficacité spectrale par unité de surface et pour éliminer les trous de couverture. Dans de tels réseaux, il est primordial d’attacher intelligemment les utilisateurs aux stations de base et de bien gérer les interférences afin de gagner en performance. Comme la différence de puissance d’émission est importante entre les grandes et petites cellules, l’association habituelle des mobiles aux stations de bases en se basant sur le signal le plus fort, n’est plus adaptée dans les HetNets. Une technique basée sur des offsets individuelles par cellule Offset(CIO) est donc nécessaire afin d’équilibrer la charge entre les cellules et d’augmenter l’attraction des petites cellules (SC) par rapport aux cellules macro (MC). Cette offset est ajoutée à la valeur moyenne de la puissance reçue du signal de référence(RSRP) mesurée par le mobile et peut donc induire à un changement d’attachement vers différents eNodeB. Comme les stations de bases dans les réseaux cellulaires LTE utilisent les mêmes sous-bandes de fréquences, les mobiles peuvent connaître une forte interférence intercellulaire, en particulier en bordure de cellules. Par conséquent, il est primordial de coordonner l’allocation des ressources entre les cellules et de minimiser l’interférence entre les cellules. Pour atténuer la forte interférence intercellulaire, les ressources, en termes de temps, fréquence et puissance d’émission, devraient être alloués efficacement. Un modèle pour chaque dimension est calculé pour permettre en particulier aux utilisateurs en bordure de cellule de bénéficier d’un débit plus élevé et d’une meilleure qualité de l’expérience. L’optimisation de tous ces paramètres peut également offrir un gain en consommation d’énergie. Dans cette thèse, nous proposons une solution dynamique polyvalente effectuant une optimisation de l’attachement des mobiles aux stations de base et de l’allocation des ressources dans les réseaux cellulaires LTE maximisant une fonction d’utilité du réseau qui peut être choisie de manière adéquate.Notre solution, basée sur la théorie des jeux, permet de calculer les meilleures valeurs pour l’offset individuelle par cellule (CIO) et pour les niveaux de puissance à appliquer au niveau temporel et fréquentiel pour chaque cellule. Nous présentons des résultats des simulations effectuées pour illustrer le gain de performance important apporté par cette optimisation. Nous obtenons une significative hausse dans le débit moyen et le débit des utilisateurs en bordure de cellule avec 40 % et 55 % de gains respectivement. En outre, on obtient un gain important en énergie. Ce travail aborde des défis pour l’industrie des télécoms et en tant que tel, un prototype de l’optimiseur a été implémenté en se basant sur un trafic HetNets émulé

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

Energy efficiency with quality of service constraints in heterogenous networks

The Fifth Generation (5G) cellular network is a new technology that is driven by the demand of high data usage, large number of wireless devices and better Quality of Service (QoS). An important challenge for 5G is the energy consumption which is causing the wireless communication networks to be one of the main contributors of the global worming. Thus, energy efficiency becomes an important aspect in designing the wireless communication networks. In this thesis, we study different approaches for Energy Efficient (EE) operation of Small Base Stations (SBSs) in Heterogenous wireless Networks (HetNets). First, we focus on enhancing energy efficiency in heterogenous networks, where Macro BSs (MBSs) and SBSs co-exist, by presenting a sleeping strategy. In the sleeping strategy SBSs serving few or no users are turned off and their users and resources are offloaded to neighboring SBSs. However, adapting the sleeping strategy will affect the lifetime of the electronics of the SBSs, due to the frequent change of power level between turning ON and OFF the SBS. Therefore, in order to maximize energy savings, we formulate an optimization problem that provides an optimal user association and SBSs sleeping strategy for the entire network, while minimizing the total numbers of the switching of SBSs ON and OFF. Furthermore, an other approach consider is the deactivated SBSs are equipped with two power sources, a harvested energy (HE) source and a grid power source, where first the SBS will use its available HE to serve the associated users. Then, the SBS will request any shortage of its energy from other active or deactivated SBSs which have surplus of HE. Finally, if there is still shortage in energy, the SBS will use the power drawn from the grid. However, since the formulated problem is a Mixed Integer NonLinear Problem (MINLP), Generalized Bender Decomposition (GBD) is proposed to decompose the problem into two subproblems. Moreover, a new heuristic approach is proposed to provide a computational efficient algorithm to solve and optimize the user association and energy harvesting of the system model. A new UEs’ prediction method is introduced to provide a future information for the model and to apply an accurate designing parameters. This method is based on a combined approach of Non-linear Autoregressive with External input(NARX) and probabilistic Latent semantic Analysis (pLSA) to provide accurate prediction for multiple steps. Therefore, we consider integrating the powerful Machine Learning (ML) techniques to provide solution of the system model with less computation demands. Thus, we introduced an efficient less complex approach that is based on synthetically generating data from the optimization problem and employing it to train and configure an Artificial Neural Network. An extensive simulation results are presented to show the effectiveness of our approaches in comparison to the optimal results

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Radio Resource Management for New Application Scenarios in 5G: Optimization and Deep Learning

The fifth-generation (5G) New Radio (NR) systems are expected to support a wide range of emerging applications with diverse Quality-of-Service (QoS) requirements. New application scenarios in 5G NR include enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low-latency communications (URLLC). New wireless architectures, such as full-dimension (FD) massive multiple-input multiple-output (MIMO) and mobile edge computing (MEC) system, and new coding scheme, such as short block-length channel coding, are envisioned as enablers of QoS requirements for 5G NR applications. Resource management in these new wireless architectures is crucial in guaranteeing the QoS requirements of 5G NR systems. The traditional optimization problems, such as subcarriers and user association, are usually non-convex or Non-deterministic Polynomial-time (NP)-hard. It is time-consuming and computing-expensive to find the optimal solution, especially in a large-scale network. To solve these problems, one approach is to design a low-complexity algorithm with near optimal performance. In some cases, the low complexity algorithms are hard to obtain, deep learning can be used as an accurate approximator that maps environment parameters, such as the channel state information and traffic state, to the optimal solutions. In this thesis, we design low-complexity optimization algorithms, and deep learning frameworks in different architectures of 5G NR to resolve optimization problems subject to QoS requirements. First, we propose a low-complexity algorithm for a joint cooperative beamforming and user association problem for eMBB in 5G NR to maximize the network capacity. Next, we propose a deep learning (DL) framework to optimize user association, resource allocation, and offloading probabilities for delay-tolerant services and URLLC in 5G NR. Finally, we address the issue of time-varying traffic and network conditions on resource management in 5G NR

Improving Frequency Reuse and Cochannel Interference Coordination in 4G HetNets

This report describes my M.A.Sc. thesis research work. The emerging 4th generation (4G) mobile systems and networks (so called 4G HetNets) are designed as multilayered cellular topology with a number of asymmetrically located, asymmetrically powered, self-organizing, and user-operated indoor small cell (e.g., pico/femto cells and WLANs) with a variety of cell architectures that are overlaid by a large cell (macro cell) with some or all interfering wireless links. These designs of 4G HetNets bring new challenges such as increased dynamics of user mobility and data traffic trespassing over the multi-layered cell boundaries. Traditional approaches of radio resource allocation and inter-cell (cochannel) interference management that are mostly centralized and static in the network core and are carried out pre-hand by the operator in 3G and lower cellular technologies, are liable to increased signaling overhead, latencies, complexities, and scalability issues and, thus, are not viable in case of 4G HetNets. In this thesis a comprehensive research study is carried out on improving the radio resource sharing and inter-cell interference management in 4G HetNets. The solution strategy exploits dynamic and adaptive channel allocation approaches such as dynamic and opportunistic spectrum access (DSA, OSA) techniques, through exploiting the spatiotemporal diversities among transmissions in orthogonal frequency division multiple access (OFDMA) based medium access in 4G HetNets. In this regards, a novel framework named as Hybrid Radio Resource Sharing (HRRS) is introduced. HRRS comprises of these two functional modules: Cognitive Radio Resource Sharing (CRRS) and Proactive Link Adaptation (PLA) scheme. A dynamic switching algorithm enables CRRS and PLA modules to adaptively invoke according to whether orthogonal channelization is to be carried out exploiting the interweave channel allocation (ICA) approach or non-orthogonal channelization is to be carried out exploiting the underlay channel allocation (UCA) approach respectively when relevant conditions regarding the traffic demand and radio resource availability are met. Benefits of CRRS scheme are identified through simulative analysis in comparison to the legacy cochannel and dedicated channel deployments of femto cells respectively. The case study and numerical analysis for PLA scheme is carried out to understand the dynamics of threshold interference ranges as function of transmit powers of MBS and FBS, relative ranges of radio entities, and QoS requirement of services with the value realization of PLA scheme.1 yea

D4.3 Final Report on Network-Level Solutions

Research activities in METIS reported in this document focus on proposing solutions to the network-level challenges of future wireless communication networks. Thereby, a large variety of scenarios is considered and a set of technical concepts is proposed to serve the needs envisioned for the 2020 and beyond. This document provides the final findings on several network-level aspects and groups of solutions that are considered essential for designing future 5G solutions. Specifically, it elaborates on: -Interference management and resource allocation schemes -Mobility management and robustness enhancements -Context aware approaches -D2D and V2X mechanisms -Technology components focused on clustering -Dynamic reconfiguration enablers These novel network-level technology concepts are evaluated against requirements defined by METIS for future 5G systems. Moreover, functional enablers which can support the solutions mentioned aboveare proposed. We find that the network level solutions and technology components developed during the course of METIS complement the lower layer technology components and thereby effectively contribute to meeting 5G requirements and targets.Aydin, O.; Valentin, S.; Ren, Z.; Botsov, M.; Lakshmana, TR.; Sui, Y.; Sun, W.... (2015). D4.3 Final Report on Network-Level Solutions. http://hdl.handle.net/10251/7675