Optimizing Network Information for Radio Access Technology Selection

International audienceThe rapid proliferation of radio access technologies (e.g., HSPA, LTE, WiFi and WiMAX) may be turned into advantage. When their radio resources are jointly managed, heterogeneous networks inevitably enhance resource utilization and user experience. In this context, we tackle the Radio Access Technology (RAT) selection and propose a hybrid decision framework that integrates operator objectives and user preferences. Mobile users are assisted in their decisions by the network that broadcasts cost and QoS parameters. By signaling appropriate decisional information, the network tries to globally control users decision in a way to meet operator objectives. Besides, mobiles combine their needs and preferences with the signaled network information, and select their access technology so as to maximize their own utility. Deriving network information is formulated as a Semi-Markov Decision Process (SMDP). We show how to dynamically optimize long-term network reward, aligning with user preferences. Index Terms—Radio access technology selection, Semi-Markov Decision Process, hybrid decision-making approach

Ordonnancement opportuniste dans les réseaux mobiles de nouvelle génération

The scarce resources in wireless systems compounded by their highly variable and error prone propagation characteristics stress the need for efficient resource management. Scheduling is a key tool to allocate efficiently the radio frequency spectrum. While fading effects have long been combated in wireless networks, primarily devoted to voice calls, they are now seen as an opportunity to increase the capacity of novel wireless networks that incorporate data traffic. For data applications, there is a service flexibility afforded by the delay tolerance of elastic traffic and by their ability to adapt their rate to the variable channel quality. Channel-aware scheduling exploit these characteristics by making use of channel state information to ensure that transmission occurs when radio conditions are most favourable. When users have heterogeneous characteristics and quality of service requirements, channel-aware scheduling becomes a challenging task. In this thesis, channel-aware transmission schemes for supporting downlink non-real time services are proposed and analyzed for novel cellular systems. The proposed schemes are designed for providing various QoS requirements for users while increasing the system global throughput.L'Internet a connu un essor remarquable ces dernières années. Cet essor n'a pas été restreint aux réseaux fixes mais a gagné récemment les réseaux mobiles. Les réseaux sans fil, initialement conçus pour véhiculer exclusivement des services voix, s'adaptent progressivement à ces changements pour transporter des services data. Avec un besoin grandissant pour accéder à ces nouveaux services, proposer des méthodes performantes pour gérer la ressource radio et fournir des garanties de performances aux services data mobiles est désormais d'une importance capitale. L'ordonnancement est l'un des mécanisme clé visant à augmenter l'efficacité du spectre mobile tout en faisant face aux caractéristiques uniques du canal mobile et à une demande croissante pour un accès haut débit à l'Internet mobile. Notre travail s'est concentré sur les politiques d'ordonnancement dites opportunistes qui, utilisant l'information relative à l'état du canal, donnent une sorte de priorité aux utilisateurs ayant un bon état de canal dans le but d'optimiser l'allocation des ressources. Diverses politiques d'ordonnancement opportunistes sont proposées dans le cadre de cette thèse pour les services data des réseaux mobiles de nouvelle génération. Elles sont conçues pour mettre à profit les variations du canal dans le but d'augmenter la capacité globale du système tout en satisfaisant différents critères en terme de qualité de service

L'ordonnancement opportuniste dans les réseaux mobiles de nouvelle génération

L'Internet a connu un essor remarquable ces dernières années. Cet essor n'a pas été restreint aux réseaux fixes mais a gagné récemment les réseaux mobiles. Les réseaux sans fil, initialement conçus pour véhiculer exclusivement des services voix, s'adaptent progressivement à ces changements pour transporter des services data. Avec un besoin grandissant pour accéder à ces nouveaux services, proposer des méthodes performantes pour gérer la ressource radio et fournir des garanties de performances aux services data mobiles est désormais d'une importance capitale. L'ordonnancement est l'un des mécanisme clé visant à augmenter l'efficacité du spectre mobile tout en faisant face aux caractéristiques uniques du canal mobile et à une demande croissante pour un accès haut débit à l'Internet mobile. Notre travail s'est concentré sur les politiques d'ordonnancement dites opportunistes qui, utilisant l'information relative à l'état du canal, donnent une sorte de priorité aux utilisateurs ayant un bon état de canal dans le but d'optimiser l'allocation des ressources. Diverses politiques d'ordonnancement opportunistes sont proposées dans le cadre de cette thèse pour les services data des réseaux mobiles de nouvelle génération. Elles sont conçues pour mettre à profit les variations du canal dans le but d'augmenter la capacité globale du système tout en satisfaisant différents critères en terme de qualité de service.The scarce resources in wireless systems compounded by their highly variable and error prone propagation characteristics stress the need for efficient resource management. Scheduling is a key tool to allocate efficiently the radio frequency spectrum. While fading effects have long been combated in wireless networks, primarily devoted to voice calls, they are now seen as an opportunity to increase the capacity of novel wireless networks that incorporate data traffic. For data applications, there is a service flexibility afforded by the delay tolerance of elastic traffic and by their ability to adapt their rate to the variable channel quality. Channel-aware scheduling exploit these characteristics by making use of channel state information to ensure that transmission occurs when radio conditions are most favourable. When users have heterogeneous characteristics and quality of service requirements, channel-aware scheduling becomes a challenging task. In this thesis, channel-aware transmission schemes for supporting downlink non-real time services are proposed and analyzed for novel cellular systems. The proposed schemes are designed for providing various QoS requirements for users while increasing the system global throughput.PARIS-Télécom ParisTech (751132302) / SudocSudocFranceF

Multi-Armed Bandit for distributed Inter-Cell Interference Coordination

International audienceIn order to achieve high data rates in future wireless packet switched cellular networks, aggressive frequency reuse is inevitable due to the scarcity of the radio resources. While intra-cell interference is mostly mitigated and can be ignored, inter-cell interference can severely degrade performances of end-users. Hence, Inter-Cell Interference Coordination is commonly identified as a key radio resource management mechanism to enhance system performance of 4G networks. This paper addresses the problem of ICIC in the downlink of Long Term Evolution (LTE) systems where the Resource Blocks (RB) selection process is inspired from the reinforcement learning theory targeted to address the adversarial Multi-Armed Bandit problem. We resort to the popular EXP3 algorithm whose goal is to steer autonomously the decision of each Base Station (BS) towards the least interfered RBs while ensuring reactivity to the possible changes that can occur in the common resource usage and radio channel quality. However, the EXP3 algorithm is computationally heavy as its strategy set grows exponentially with the number of needed RBs and the total amount of available RBs. Therefore, we propose an efficient adaptation of the EXP3 algorithm, deemed Q-EXP3, where the needed RBs are selected one by one requiring only polynomial time computation

A Heuristic Algorithm for Joint Power-Delay Minimization in Green Wireless Access Networks

International audienceIn this paper, we seek to jointly minimize the network power consumption and the user transmission delays in green wireless access networks. We recently studied the case of a WLAN, where we evaluated the tradeoffs between these two minimization objectives using a Mixed Integer Linear Programming (MILP) formulation. However, the MILP formulation could not deliver solutions in a reasonable amount of time due to computational complexity issues. As a result, we propose here a heuristic algorithm for the power-delay minimization problem. The proposed heuristic aims to compute the transmit power level of the Access Points (APs) deployed in the network and associate users with these APs in a way that jointly minimizes the total network power and the total network delay. The simulation results show that the proposed algorithm has a low computational complexity, which makes it advantageous compared with the optimal scheme, particularly in dense networks. Moreover, the heuristic algorithm performs close to optimally and provides power savings of up to 45% compared with legacy networks

Optimization Models for the Joint Power-Delay Minimization Problem in Green Wireless Access Networks

International audienceIn wireless access networks, one of the most recent challenges is reducing the power consumption of the network, while preserving the quality of service perceived by users. Hence, mobile operators are rethinking their network design by considering two objectives, namely, saving power and guaranteeing a satisfactory quality of service. Since these objectives are conflicting, a tradeoff becomes inevitable. We formulate a multi-objective optimization with aims of minimizing the network power consumption and transmission delay. Power saving is achieved by adjusting the operation mode of the network base stations from high transmit power levels to low transmit levels or even sleep mode. Minimizing the transmission delay is achieved by selecting the best user association with the network base stations. In this article, we cover two different technologies: IEEE 802.11 and LTE. Our formulation captures the specificity of each technology in terms of the power model and radio resource allocation. After exploring typical multi-objective approaches, we resort to a weighted sum mixed integer linear program. This enables us to efficiently tune the impact of the power and delay objectives.We provide extensive simulations for various preference settings that enable to assess the tradeoff between power and delay in IEEE 802.11WLANs and LTE networks. We show that for a power minimization setting, aWLAN consumes up to 16%less power than legacy solutions. A thorough analysis of the optimization results reveals the impact of the network topology, particularly the inter-cell distance, on both objectives. For an LTE network, we assess the impact of urban, rural and realistic deployments on the achievable tradeoffs. The power savings mainly depend on user distribution and the power consumption of the sleep mode. Compared with legacy solutions, we obtained power savings of up to 22.3% in a realistic LTE networks. When adequately tuned, our optimization approach reduces the transmission delay by up to 6% in a WLAN and 8% in an LTE network

Power-Delay Tradeoffs in Green Wireless Access Networks

Abstract—Targeting energy efficiency while meeting user Quality of Service (QoS) is one of the most challenging problems in future wireless networks. Since base stations (BSs) consume a high percentage of the total energy used in a wireless access network, saving power at the BS level is a major concern in green networks. In this paper, we propose an optimization model based on finding a tradeoff between reducing the number of active radio cells and increasing the transmit power of BSs to better serve all users in the system. The main contribution of the paper is the formulation of a multiobjective optimization problem that jointly minimizes the network power consumption and the sum of data unit transmission delays of all users in the network. Our proposed model is solved using an exhaustive search algorithm to obtain the optimal solution. Solving the optimization problem at hand is very challenging due to the exhaustive search high computational complexity. Therefore, we run simulations in a small network to give insights into the optimal solution. Specifically, we study different cases by tuning the weights of the power and delay costs. This is a distinctive and important feature of our model allowing it to reflect various decision preferences. Regarding these preferences and under various users spatial distribution, results show that our solution allows to select the optimal network configuration in terms of power consumption while guaranteeing minimal delay for all users in the network. I

A three-level slicing algorithm in a multi-slice multi-numerology context

International audienceFifth generation (5G) mobile networks feature a new concept called Bandwidth Part (BWP) that permits applying various multiple Orthogonal Frequency-Division Multiplexing (OFDM) subcarrier spacing, also coined numerologies, in the same band. With flexible numerology, the sought-after purpose is achieving lower latency for the Ultra Reliable Low Latency Communications (URLLC) service. However, the lucrative multiple numerology gains might not stand because of the ensuing Inter-Numerology Interference (INI) problems. In this paper, we assess the feasibility, as well as the profitability, of multi-numerology in a multi-slice setting. In such a context, it is paramount to study the radio resource allocation problem at the Radio Access Network (RAN) level. To that aim, we propose a three-level slicing algorithm that efficiently selects the BWP serving the URLLC users from a set of BWPs using different numerologies, designs a guard band among these BWPs to reduce INI without wasting radio resources, and attributes a bandwidth to each slice depending on multiple parameters such as the users’ Key Performance Indicators (KPIs), channel conditions, INI, and the incurred monetary cost. This work considers the three 5G services, namely the enhanced Mobile BroadBand (eMBB) service, the URLLC service and the massive Machine-Type Communications (mMTC) service. The proposed algorithm combines various tools from Game Theory, Deep-Q learning Networks as well as savvy heuristics. The performance evaluation demonstrates the high efficiency of our solution in terms of latency and throughput compared to well-known approaches in the State-of-the-Art

A Downlink Power Control Heuristic Algorithm for LTE Networks

International audienceThe recent development of mobile terminals, the proliferation of mobile applications and the increasing need for mobile data have led to a dense deployment of mobile networks. In this context, the Long Term Evolution (LTE) standard is adopted by a large number of mobile network operators. LTE uses Orthogonal Frequency Division Multiple Access (OFDMA) technique on the downlink of the radio interface along with frequency reuse-1 model. However, Inter-Cell Interference (ICI) and system power consumption will cause limitations in terms of mean user throughput and system performance. Indeed, several recent works focus on the minimization of ICI and power consumption in multi-user OFDMA networks. In this paper, we propose a distributed heuristic power control algorithm that aims at minimizing the total downlink power of an LTE system. We also study the impact of the power control algorithm on ICI and system performance. Simulation results show that the proposed algorithm largely reduces the downlink power consumption without degrading system performance. In addition, it increases the mean throughput for cell-edge users that are mainly affected by ICI problems

Joint Power-Delay Minimization in Green Wireless Access Networks

Abstract—The growing energy demands, the increasing depletion of traditional energy resources, together coupled with the recent explosion of mobile internet traffic call for green solutions to address the challenges in energy efficient wireless access networks. In this paper, we consider possible power saving through reducing the number of active BSs and adjusting the transmit power of those that remain active while maintaining a satisfying service for all users in the network. Thus, we introduce an optimization problem that jointly minimizes the power consumption of the network and the sum of the transmission delays of the users in the network. Our formulation allows investigating the tradeoff between power and delay by tuning the weighting factors associated to each one. Moreover, to reduce the computational complexity of the optimal solution of our nonlinear optimization problem, we convert it into a Mixed Integer Linear Programming (MILP). We provide extensive simulations for various decision preferences such as power minimization, delay minimization and joint minimization of power and delay. Presented results show that we obtain power savings up to 20% compared to legacy network models. I