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

Sélection de technologie d’accès radio dans les réseaux sans-fil hétérogènes

To cope with the rapid growth of mobile broadband traffic, various radio access technologies (e.g., HSPA, LTE, WiFi, and WiMAX) are being integrated and jointly managed. Radio Access Technology (RAT) selection, devoted to decide to what RAT mobiles should connect, is a key functionality to improve network performance and user experience. When intelligence is pushed to the network edge, mobiles make autonomous decisions regarding selection of their most appropriate RAT. They aim to selfishly maximize their utility. However, because mobiles have no information on network load conditions, their decisions may lead to performance inefficiency. Moreover, delegating decisions to the network optimizes overall performance, but at the cost of increased network complexity, signaling, and processing load. In this thesis, instead of favoring either of these decision-making approaches, we propose a hybrid decision framework: the network provides information for the mobiles to make robust RAT selections. More precisely, mobile users select their RAT depending on their individual needs and preferences, as well as on the monetary cost and QoS parameters signaled by the network. By appropriately tuning network information, user decisions are globally expected to meet operator objectives, avoiding undesirable network states. We first introduce our hybrid decision framework. Decision makings, on the network and user sides, are investigated. To maximize user experience, we present a satisfaction-based Multi-Criteria Decision-Making (MCDM) method. In addition to their radio conditions, mobile users consider the cost and QoS parameters, signaled by the network, to evaluate serving RATs. In comparison with existing MCDM solutions, our algorithm meets user needs (e.g., traffic class, throughput demand, cost tolerance), avoiding inadequate decisions. A particular attention is then addressed to the network to make sure it broadcasts suitable decisional information, so as to better exploit its radio resources while mobiles maximize their own utility. We present two heuristic methods to dynamically derive what to signal to mobiles. While QoS parameters are modulated as a function of the load conditions, radio resources are shown to be efficiently exploited. Moreover, we focus on optimizing network information. Deriving QoS parameters is formulated as a semi-Markov decision process, and optimal policies are computed using the Policy Iteration algorithm. Also, and since network parameters may not be easily obtained, a reinforcement learning approach is introduced to derive what to signal to mobiles. The performances of optimal, learning-based, and heuristic policies are analyzed. When thresholds are pertinently set, our heuristic method provides performance very close to the optimal solution. Moreover, although lower performances are observed, our learning-based algorithm has the crucial advantage of requiring no prior parameterization.Pour faire face à la croissance rapide du trafic mobile, différentes technologies d'accès radio (par exemple, HSPA, LTE, WiFi, et WiMAX) sont intégrées et gérées conjointement. Dans ce contexte, la sélection de TAR est une fonction clé pour améliorer les performances du réseau et l'expérience de l'utilisateur. Elle consiste à décider quelle TAR est la plus appropriée aux mobiles. Quand l'intelligence est poussée à la périphérie du réseau, les mobiles décident de manière autonome de leur meilleur TAR. Ils cherchent à maximiser égoïstement leur utilité. Toutefois, puisque les mobiles ne disposent d'aucune information sur les conditions de charge du réseau, leurs décisions peuvent conduire à une inefficacité de la performance. En outre, déléguer les décisions au réseau optimise la performance globale, mais au prix d'une augmentation de la complexité du réseau, des charges de signalisation et de traitement. Dans cette thèse, au lieu de favoriser une de ces deux approches décisionnelles, nous proposons un cadre de décision hybride: le réseau fournit des informations pour les mobiles pour mieux décider de leur TAR. Plus précisément, les utilisateurs mobiles choisissent leur TAR en fonction de leurs besoins et préférences individuelles, ainsi que des paramètres de coût monétaire et de QoS signalés par le réseau. En ajustant convenablement les informations du réseau, les décisions des utilisateurs répondent globalement aux objectifs de l'opérateur. Nous introduisons d'abord notre cadre de décision hybride. Afin de maximiser l'expérience de l'utilisateur, nous présentons une méthode de décision multicritère (MDMC) basée sur la satisfaction. Outre leurs conditions radio, les utilisateurs mobiles tiennent compte des paramètres de coût et de QoS, signalées par le réseau, pour évaluer les TAR disponibles. En comparaison avec les solutions existantes, notre algorithme répond aux besoins de l'utilisateur (par exemple, les demandes en débit, la tolérance de coût, la classe de trafic), et évite les décisions inadéquates. Une attention particulière est ensuite portée au réseau pour s'assurer qu'il diffuse des informations décisionnelles appropriées, afin de mieux exploiter ses ressources radio alors que les mobiles maximisent leur propre utilité. Nous présentons deux méthodes heuristiques pour dériver dynamiquement quoi signaler aux mobiles. Puisque les paramètres de QoS sont modulées en fonction des conditions de charge, l'exploitation des ressources radio s'est avérée efficace. Aussi, nous nous concentrons sur l'optimisation de l'information du réseau. La dérivation des paramètres de QoS est formulée comme un processus de décision semi-markovien, et les stratégies optimales sont calculées en utilisant l'algorithme de Policy Iteration. En outre, et puisque les paramètres du réseau ne peuvent pas être facilement obtenues, une approche par apprentissage par renforcement est introduite pour dériver quoi signaler aux mobiles

WiMAX Double Movable Boundary Scheme in the vehicle to Infrastructure Communication Scenario

International audienceWiMAX is an interesting technology that will be applied in vehicular networks due to the provisioning of high mobility, wide coverage, and different classes of service. In this paper, we investigate the problem of vehicular applications mapping in the Vehicular to Infrastructure scenario and propose a resource allocation algorithm applied in WiMAX networks. The proposed algorithm is a double movable boundary scheme which is based on dynamic sharing of resources between different traffic categories provided by a common resource pool. We provide as well a mathematical model of the mechanism and investigate the impact of critical resource allocation parameters on the overall performance. Performance results show that the algorithm respects the priority of real-time connections and prevents least-priority classes starvation problem. In fact, we strive to achieve two major components: fairness to different classes of service and service differentiation

A Four-Sided Matching Game for Energy-Efficient Scheduling in Narrowband-IoT Networks

This paper studies the uplink resource allocation problem in NB-IoT networks. We aim to maximize the network’s energy efficiency to serve devices in need for long battery lifetime. Also, the network energy efficiency is affected by the associated UEs to candidates, carrying the scheduling information for UEs. For this reason, we investigate the joint resource allocation and Candidate-UE association problem in the uplink (UL) in NB-IoT. The resource allocation problem allocates the UEs with the suitable scheduling delay value and subcarrier indication field. The Candidate-UE association problem selects the appropriate UE for each candidate. The optimization variables involved in the joint problem are divided into binary association variables and binary resource allocation variables. This makes it hard to solve the joint problem since its optimization variables are of combinatorial nature. To this end, we formulate the joint problem as a four-sided matching game that ranks the UEs, candidates, the scheduling delay values and subcarrier indication fields while considering maximizing the energy efficiency as a preference metric. We then describe a four-sided matching game algorithm to solve the problem. The simulation results highlight the proposed algorithm’s effectiveness compared to other algorithms in the obtained network energy efficiency

Energy-Efficient Uplink Scheduling in Narrowband IoT

This paper presents a detailed study of uplink scheduling in narrowband internet of things (NB-IoT) networks. As NB-IoT devices need a long battery lifetime, we aim to maximize energy efficiency while satisfying the main requirements for NB-IoT devices. Also, as the NB-IoT scheduling problem is divided into link adaptation problem and resource allocation problem, this paper investigates the correlation between these two problems. Accordingly, we propose two scheduling schemes: the joint scheduling scheme, where the two problems are combined as one optimization problem, and the successive scheduling scheme that manages each problem separately but successively. Each scheme aims to maximize energy efficiency while achieving reliable transmission, satisfying delay requirements, and guaranteeing resource allocation specifications. Also, we investigate the impact of the selected devices to be served on the total energy efficiency. Accordingly, we propose two device selection techniques to maximize the total energy efficiency. The first technique exhaustively searches for the optimal devices, while the second sorts the devices based on a proposed priority score. The simulation results compare the successive and the joint scheduling schemes. The results show that the joint scheme outperforms the successive scheme in terms of energy efficiency and the number of served devices but with higher complexity. Also, the results highlight the impact of each proposed selection technique on the scheduling schemes’ performance

Radio Access Selection Approaches in Heterogeneous Wireless Networks

International audienceAlong with the rapid growth of mobile broadband traffic, multiple radio access technologies (RATs) are being integrated and jointly managed. To optimize heterogeneous network performance, efficient Common Radio Resource Management (CRRM) mechanisms need to be defined. This paper tackles the access technology selection -- a key CRRM functionality -- and proposes a hybrid approach that combines benefits from both network-centric and user-centric methods. Network information, that is periodically broadcasted, assists mobile users in their decisions. By broadcasting appropriate decisional information, the network tries to globally control users decision in a way to meet operator objectives. On the other hand, mobiles also integrate their needs and preferences to select their access technology so as to maximize their own utility. In comparison with other RAT selection techniques, including network-centric, hybrid and user-centric methods, simulation results prove the efficiency of our hybrid approach in enhancing resource utilization and maximizing user satisfaction

Satisfaction-based Radio Access Technology Selection in Heterogeneous Wireless Networks

International audienceIn this paper, a hybrid approach for Radio Access Technology (RAT) selection in heterogeneous wireless networks is proposed. This decision framework dynamically integrates operator objectives and user preferences, with a relatively reduced network complexity, signaling and processing load. By broadcasting cost and QoS parameters, the network assists mobile users in their decisions. Focusing on the user side, we present a satisfaction-based multi-criteria decision-making (MCDM) method. Based on their needs and preferences, individual users select their RAT avoiding inadequate decisions. Simulation results show that our MCDM method maximizes user utility and outperforms existing solutions

A Hybrid Approach for Radio Access Technology Selection in Heterogeneous Wireless Networks

International audienceIn heterogeneous wireless networks, different radio access technologies are integrated and may be jointly managed. To optimize composite network performance and capacity, Common Radio Resource Management (CRRM) mechanisms need to be defined. This paper tackles the access technology selection -- a key CRRM functionality -- and proposes a hybrid decision framework to dynamically integrate operator objectives and user preferences. Mobile users make their selection decision based on their needs and preferences as well as on the cost and QoS information signaled by the network. Appropriate decisional information should then be derived so that the network better utilizes its radio resources, while mobile users maximize their own utility. We thus present two tuning policies, namely the staircase and the slope tuning policies, to dynamically modulate this information. Simulation results illustrate the gain from using our tuning policies in comparison with a static one: they lead to better network performance, larger operator gain and higher user satisfaction

RALI: Increasing Reliability in LoRaWAN through Repetition and Iteration

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Study of LoRaWAN Networks Reliability

International audienceThe Internet of Things (IoT) is a rapidly evolving field that incorporates a wide range of technologies and applications, enabling the seamless integration of everyday objects into the digital world. The effective integration of IoT into various systems requires the implementation of lightweight solutions to overcome the challenges posed by highly dense networks and constrained resources, including computational power, memory capacity, and battery life. The present research is dedicated to investigating a specific context of the Internet of Things (Io’T), namely LoRaWAN, in which devices communicate with the access network using ALOHA-type access and spread spectrum technology. LoRaWAN advocates simplicity in order to reduce drastically the battery consumption, which severely degrades reliability. In this paper we introduce blind repetition in LoRaWAN: a packet is retransmitted a fixed number of times regardless of its good reception. Leveraging on existing data link layer functionalities, we compare this redundant mode to two existing modes, namely the unacknowledged mode and acknowledged mode. We run extensive simulations that consider the capture effect in LoRaWAN, in addition to the non-uniform distribution of devices. In such a realistic scenario, we perform thorough numerical simulations to quantify the discrepancy between the three modes, and identify the traffic conditions for which a given mode has precedence over the two others