We study Constrained Online Convex Optimization with Memory (COCO-M), where both the loss and the constraints depend on a finite window of past decisions made by the learner. This setting extends the previously studied unconstrained online optimization with memory framework and captures practical problems such as the control of constrained dynamical systems and scheduling with reconfiguration budgets. For this problem, we propose the first algorithms that achieve sublinear regret and sublinear cumulative constraint violation under time-varying constraints, both with and without predictions of future loss and constraint functions. Without predictions, we introduce an adaptive penalty approach that guarantees sublinear regret and constraint violation. When short-horizon and potentially unreliable predictions are available, we reinterpret the problem as online learning with delayed feedback and design an optimistic algorithm whose performance improves as prediction accuracy improves, while remaining robust when predictions are inaccurate. Our results bridge the gap between classical constrained online convex optimization and memory-dependent settings, and provide a versatile learning toolbox with diverse applications.

As touch-enabled teleoperation applications envisioned in the Tactile Internet are highly sensitive to latency, minimizing Medium Access Delay is essential. Two commonly employed resource scheduling schemes for this purpose are Semi-Persistent Scheduling (SPS) and Grant-Free Access (GFA) with M-repetition transmission. While SPS is well-suited for periodic traffic and GFA is for sporadic transmissions, the bursty and dynamic nature of haptic traffic in the Tactile Internet necessitates a hybrid scheduling policy that adapts to varying traffic states. To address this challenge, we propose a Diffusion-based Offline Reinforcement Learning framework that efficiently learns effective scheduling policies from static datasets, rather than online interaction. Using synthetic traffic datasets from the human experiment and the 5G emulator, the simulation results show a general improvement over other offline RL benchmarks.

International audience

This paper proposes a goal-oriented design framework for Integrated Sensing and Communications (ISAC) at the network layer. Devices equipped with dual-functional radar and communication (DFRC) transceivers that jointly perform a sensing task and carry data traffic are considered. The goal of the sensing application is to maximize the classification accuracy, while the network aims to preserve throughput for the enhanced Mobile Broadband (eMBB) service and to respect energy constraints. A cross-layer optimization problem that adapts the lower layer parameters (sensing power and scheduling policy) for maximizing the classification accuracy, while respecting constraints related to the available resources (eMBB throughput, device’s maximal power, and total energy), is formulated. A tractable relaxation of the problem is proposed, yielding an approximately optimal policy that is computable by solving successive convex optimization problems. Our numerical experiments, conducted both on a synthetic Gaussian mixture dataset and on a radar simulation dataset, show the superiority of our goal-oriented scheme policy to classical ISAC schemes.

Tactile Internet has emerged as a cutting-edge application in 5G, aligning with requirements of Ultra-Reliable Low-Latency Communications (URLLC), and is expected to be an essential service in 6G. TI service traffic is characterized by a high burstiness, and the design of effective resource reservation strategies remains an open research in this context. To address this challenge, we formulate the resource reservation problem in the Tactile Internet as an online decision-making task and address it by applying Optimistic Constrained Online Convex Optimization, profiting from Tactile Internet traffic prediction capabilities. We validate the proposed optimistic algorithm by utilizing a traffic dataset based on a self-developed haptic-visual testbed, demonstrating the optimistic algorithm’s superior performance compared to the non-optimistic baseline.

Applications requiring haptic flows are expected to grow in importance for 6G networks. While the haptic codec has been recently standardized and is based on the Just Noticeable Difference (JND) principle, performance models and associated network control mechanisms are still lacking. We propose in this work a queuing theoretical control framework to minimize the cost of haptic packet delivery, in terms of network resources, while respecting constraints on consecutive packet losses, an essential metric for such applications. The framework switches between different control policies, leveraging resource reservation and/or power control, depending on the network conditions. Each set of policies is parameterized, reflecting a trade-off between system cost and performance of the haptic service. Through this adaptive approach, we derive closed-form solutions that minimize the cost while ensuring adherence to the loss constraints.

Ensuring energy efficiency is an important requirement for mobile networks and sleep mode is an essential enabler for it. With the rise of open-RAN, the disaggregation of network functions at different locations has enabled to switch off baseband processing functions whenever possible. In this work, we aim to reduce the energy consumption of the radio access network by leveraging advanced sleep modes, namely micro, light, and deep, with different energy profiles. The main difficulty when implementing these sleep modes on small time scales originates from the non-negligible transition times. We model the problem of the processing units activation/deactivation that host the DUs as an MILP (Mixed Integer Linear Program). We offer an LPMIP (Linear Program - Mixed Integer Program) version, with binary and continuous variables. We propose exact solutions for the two formulated problems and an additional heuristic with lower complexity.

We study resource allocation for a metaverse user in 5G networks and beyond. To ensure an immersive experience, one should consider the multi-modality nature of the metaverse, where each user generates multiple coupled flows, namely visual and haptic, characterized by joint Quality of Service (QoS) requirements, for instance in terms of subjective Just Noticeable Difference (JND) metric. These flows can be transported via various 5G services, such as Ultra Reliable Low Latency Communications (URLLC) for the haptic flow and enhanced Mobile Broadband (eMBB) for the visual one. Furthermore, the metaverse user has to share the radio resources with classical eMBB users, characterized by an elastic nature. We formulate an optimization problem that determines the optimal resource sharing between flows, under various performance constraints. We show how to solve this problem in real-world scenarios where there is a discrete set of modulation and coding schemes (MCSs) and considering the various characteristics of the different flows.

In this paper, we tackle the problem of multi-connectivity for latency critical industrial IoT services in 5G and Beyond. Sending multiple copies of the same packet on different radio interfaces is a popular mean for reducing delay. However, systematic redundancy may lead to congestion and more dynamic scheduling schemes such as Join the Shortest Queue (JSQ) are not guaranteed to be optimal. In order to derive the optimal multi-connectivity scheme, we first develop a novel discrete-time queuing model for the system, that integrates the heterogeneity of radio conditions and can consider any multi-connectivity policy. We then formulate an associated Markov Decision Process (MDP) that learns, online, the optimal state/action combination. Our simulations demonstrate that the derived optimal scheme outperforms classical ones like systematic redundancy and JSQ and is able to adapt dynamically to different traffic conditions. Interestingly, our proposed scheme yields a clear advantage when the objective is to ensure a hard latency constraint compared with the case where the average delay is to be minimized.

We address radio resource allocation for the transport of extreme Ultra Reliable Low Latency and Reliability (URLLC) traffic. One illustrative use case is factory automation using 6G networks. In this context, extreme URLLC has very stringent Quality of Service (QoS) requirements: 0.1 ms for the delay and $10^{-7}$ for the reliability. Reliability can be even higher for other use cases. We model QoS in terms of outage probability, that is the likelihood of failing to serve at least one packet due to insufficient resources, and derive the minimal resource reservation that would meet such requirement. We formulate the problem as chance-constrained optimization and solve it assuming partial knowledge of arriving traffic distribution. We treat the case where traffic is described through its mean and variance, and make use of three approaches to find the optimal solution: distributionally robust using worst-case value at risk approach, distribution-based approximation and bounds from large deviation theory. We also solve the optimization problem using a data driven approach and propose a sliding window mechanism to perform it online. We compare the performance of the aforementioned approaches numerically and show the effectiveness of the data driven approach, accounting for user radio condition heterogeneity and thus different Modulation and Coding Schemes.

This paper tackles the problem of distributed sensing from a goal-oriented communications perspective. The case of sensors sampling sources in the environment and conveying the collected information to a fusion center over a wireless channel is considered. Herein, the objective of the communications is to achieve a reliable estimation of the sources, while preserving network resources. The fusion center task is formulated as a joint distributed sensing problem, where a scheduling policy has to be designed to optimize the applicative goal. Two distinct goals are considered: a) when the objective is to achieve the best possible estimation of the sources given a communication resource constraint; b) when the objective is to minimize the communication resource to estimate the sources up to a pre-determined precision. For both cases, the optimal scheduling policy is formulated as the solution to a constrained optimization problem. Greedy iterative algorithms are proposed to efficiently solve those problems for the particular case of independent sources. Numerical results illustrate the tradeoff between achievable estimation accuracy and resource consumption, and provide insights on the impact of the network structure on the achievable performance.

We propose a novel resource allocation framework for latency-critical traffic, namely Ultra Reliable Low Latency Communications (URLLC), in mobile networks which meets stringent latency and reliability requirements while minimizing the allocated resources. The Quality of Service (QoS) requirement is formulated in terms of the probability that the latency exceeds a maximal allowed budget. We develop a discrete-time queuing model for the system, in the case where the URLLC reservation is fully-flexible, and when the reservation is made on a slot basis while URLLC packets arrive in mini-slots. We then exploit this model to propose a control scheme that dynamically updates the amount of resources to be allocated per time slot so as to meet the QoS requirement. We formulate an optimization framework that derives the policy which achieves the QoS target while minimizing resource consumption and propose offline algorithms that converge to the quasi optimal reservation policy. In the case when traffic is unknown, we propose online algorithms based on stochastic bandits to achieve this aim. Numerical experiments validate our model and confirm the efficiency of our algorithms in terms of meeting the delay violation target at minimal cost.

We propose a new performance model for transport of time-critical Ultra Reliable Low Latency Communications (URLLC) traffic in 5G networks and Beyond and apply it to dimensioning of such systems. The Quality of Service (QoS) requirement is formulated in terms of an outage probability which is defined as the probability that the latency exceeds a maximal allowed budget, and which should be kept very low. We develop a generic queuing model to compute this outage probability and adapt it to integrate the specificity of the 5G radio interface, taking into account the heterogeneity of users radio conditions and thus their Modulation and Coding Schemes (MCS) as well as retransmissions due to errors on the radio link. We also propose a low complexity method to calculate it using a geometric tail approach to approximate the tail distribution of the queue, for relevant arrival distributions: Poisson and Binomial. We show numerically the performance of our exact model and approximation and that they yield very accurate performance against simulations, and in comparison with other models from the state of the art. We also show the system dimensioning in terms of required resources to satisfy the outage constraint.

In the context of Industrial Internet of Thing (IIoT) applications, network administrators must use their available bandwidth to both identify best 5G NR (New Radio) configuration and best transmission schemes. In this paper we derive optimal channel access and packet replication schemes for Ultra Reliable Low Latency Communications (URLLC) traffic in 5G and Beyond networks. Based on typical system configurations, we identify three main regimes, characterized by the delay and the consequent presence or absence of feedback received from the network. In particular, we identify (a) extreme situations where there is only one time slot opportunity for packet transmission with possible replications in the frequency dimension, (b) blind repeated replication with several transmission slots opportunities, with no received ACK before the delay expiration, and (c) far-sighted scenarios where, additionally, some ACKs may be received, acknowledging older replicas of the packet. We propose adapted replication models for each radio scenario and develop corresponding mathematical models from which the transmission schemes may be optimized. The proposed policies are semi-distributed, in the sense that the optimal policy is communicated to each device by the network, that then applies it autonomously in order to respect the stringent timing constraints of URLLC. We then show the corresponding system dimensioning for achieving the target reliability and identify the best NR configurations to be deployed in the controlled industrial environments.

The wide appeal of fuel-efficient transport solutions is constantly increasing due to the major impact of the transportation industry on the environment. Platooning systems represent a relatively simple approach in terms of deployment toward fuel-efficient solutions. This paper addresses the reduction of fuel consumption in platooning systems attainable by dynamically switching between two control policies: Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC). The switching rule is dictated by a Deep Reinforcement Learning (DRL) technique to overcome unpredictable platoon disturbances and to learn appropriate transient shift times while maximizing fuel efficiency. However, due to safety and convergence issues of DRL, our algorithm establishes transition times and minimum periods of operation of ACC and CACC controllers instead of directly controlling vehicles. Numerical experiments show that the DRL agent outperforms both static ACC and CACC versions and the threshold logic control in terms of fuel efficiency while also being robust to perturbations and satisfying safety requirements.

In this paper, we consider resource allocation for vehicular safety traffic. In 5G, this traffic is carried by using the Ultra-Reliable and Low-Latency Communications (URLLC) service as it needs stringent Quality of Service (QoS) requirements in terms of latency and reliability. Since URLLC services may require specific numerology and/or channel access and retransmission strategies, network slicing has been proposed as a solution for QoS requirements and its coexistence with other services such as enhanced Mobile Broad-Band (eMBB). In order to accommodate URLLC traffic, one can opt for static resource reservation, however this is not optimal as it does not follow the real URLLC traffic present in the cell and can impact negatively eMBB traffic. Reactive, on-demand resource reservation is not feasible either as it requires reconfiguration which introduces extra delay that makes it prohibitive to meet URLLC delay requirements. This paper proposes proactive resource reservation schemes that anticipate slice demand. Resource reservation is computed per gNodeB based on the expected traffic and radio conditions. We show how field measurements and trajectory predictions can be used to achieve URLLC objectives with low impact on eMBB performance.

This article investigates the usage of Artificial Intelligence (AI) techniques in the prediction of network performance for Industrial Internet of Things (IIoT). In industrial environments, 5G Ultra Reliable Low Latency Communications (URLLC) are intended for serving critical services with very stringent latency requirements, such as those involving collaborative robots. Even if the flexible 5G New Radio (NR) design is able to achieve the target IIoT performances, the necessary spectrum resources need to be available and reserved for URLLC. A Quality of Service (QoS) prediction scheme is thus needed for anticipating performance degradation and undertake necessary actions, such as network resource provisioning or application adaptation, e.g. by entering an adapted mode. We explore the design of AI algorithms for QoS prediction in industrial environments, and compare different tools for regression and classification, including Neural Networks (NN) and K Nearest Neighbors (K-NN). We explore prediction based on Signal to Interference and Noise Ratio (SINR), or simply based on the position of robots within the plant. As the latency degradation event is rare in general, we observe that the training data is highly imbalanced leading to a low prediction accuracy. We show how the prediction performance can be enhanced by importance sampling techniques and by a modified detection threshold in what we call M-KNN scheme.

Critical services in wireless networks opt for performance objectives depending on their application. For in-stance, the defined class of services Ultra-Reliable Low-Latency Communication (URLLC) in 5G networks targets machine automation applications and aims to achieve a high reliability during small latency. A second set of critical services, used generally in monitoring applications, where the receiver is interested in getting as fresh information as possible, aims to minimize the age of the received packets. In this paper, we study the transport of these two sets of services over unlicensed spectrum. We propose a transmission scheme which adapts the transmission power depending on the packet status, in order to achieve the target performance objective while preserving energy. We show that different objectives lead to opposite policies with either increasing or decreasing power levels with respect to delay.

We design a multi-connectivity scheme based on the cooperation between the networks of the fourth generation (4G) and the fifth generation (5G) of mobile technologies, to improve the reliability of Ultra-Reliable Low-Latency Communication (URLLC) services. While 5G system is characterized by a short Transmission Time Interval (TTI) and fast retransmissions within the delay budget, 4G system has a large TTI so that only blind retransmissions are possible. We develop an optimization problem that couples this multi-connectivity with an adapted number of replications, scheduled by exploiting Power Domain Non-Orthogonal Multiple Access. The resulting optimal scheme is evaluated in an outdoor urban macro scenario for different reliability targets and user locations in the cell. Results show that the scheme minimizes the number of replicas while achieving the performance targets for both URLLC and eMBB users.

We study in this paper the design of the vehicle-to-everything (V2X) network for vehicle platooning scenarios. We focus on the impact of packet relaying using Road Side Units (RSU) on the application-level performance, namely the inter-vehicle distance in the platoon. The RSU extends the coverage range of the platoon leader and allows handling the well-known Predicted Cooperative Adaptive Cruise Control (PCACC) scheme. We develop a Markov model for evaluating the performances of the different communication links (inter-vehicle and vehicle-to-RSU), using licensed or unlicensed spectrum. We then develop a cross-layer approach that adapts the application layer (PCACC control parameters) to the observed Medium Access Layer (MAC) performance. The simulations results prove that relaying allows to greatly reduce both the communication link failures and the inter-vehicular distances while introducing minimal delay in the system. Furthermore, even if using licensed spectrum for the relay link it drastically reduces the Packet Error Rate (PER) compared to the unlicensed spectrum case, the robustness of the application layer scheme makes the latter case viable for the considered scenario.

This article studies cooperative vehicle platooning under different communications approaches and introduces a new dynamic control scheme based on the Predicted Cooperative Adaptive Cruise Control (PCACC). We start by a performance comparison of different communication technologies including VLC (Visible Light Communication) and V2V (Vehicle-to-Vehicle), with or without relaying via RSU (Road Side Unit), and assess their suitability to fully cooperative and semi-autonomous control schemes. Different from the state of the art, our main design goal is the robustness in terms of an extremely low probability of emergency braking under varying communication conditions and bursts of packet losses. In the light of the performance results, we propose an adaptive control mechanism that tunes the control gains as a function of the observed radio link quality and demonstrate, via extensive simulations, a significantly better performance compared to static control strategies.

Ultra-Reliable Low Latency Communications (URLLC) is a key service in fifth generation (5G) networks, that requires stringent Quality of Service (QoS) in terms of latency and reliability. As URLLC services may require specific numerology and/or specific channel access and re-transmission strategies, network slicing has been proposed as a solution for multiplexing them with other services such as enhanced Mobile Broadband (eMBB). Once the URLLC slice is configured and resources are dimensioned and allocated to it, URLLC performance targets should be attained thanks to the 5G New Radio (NR) low latency and high reliability features. However, in vehicular services such as safety message exchange, URLLC slice resource dimensioning cannot be static due to the varying number of vehicles in the cell. We show in this paper how the delay for slice reconfiguration alters the URLLC performance and propose a proactive resource reservation scheme that anticipates slice needs and allows ensuring URLLC targets. In order to reduce the impact of this proactive reservation on eMBB performance, we make use of vehicle trajectory prediction and show that limiting anticipated reservation to fewer cells allows reaching the target URLLC QoS with a limited degradation of the network capacity.

With the rapid deployment of Internet of Things (IoT) applications, video processing and streaming requirements are increasing, and edge computing is a good candidate to cope with strong latency and throughput expectations. In this paper, we consider the optimal routing, placement and scaling of IoT-based service function chains for object detection. We propose an edge networking approach dealing with limited CPU and network bandwidth resources in a joint optimization based on Integer Linear Programming for small problem instances, and a graph-based approximation to cope with scalability issues. We evaluate the efficiency of our algorithms through simulations and show that the graph-based approach converges towards a near-optimal solution in negligible time and is thus suitable for real-time function chain placement.

We consider in this paper a wireless network powered by a smart grid. The base stations are endowed with backup battery in order to prevent possible power failures in the grid. The battery can also be used for arbitrage purposes wherein the operator can purchase electricity from the grid with a certain price and store it in order to use it later when prices go up. Our aim is to design, on a daily basis, an optimal energy storage strategy, taking into account the electricity price fluctuations in order to reduce the operator's daily energy acquisition cost while satisfying the users traffic requirements and a maximal number of cycles to be performed per day in order to extend the battery lifetime. We do so using a Markov Decision Process (MDP) formulation. With respect to the battery type (Lithium, Lead-acid), cycle and calendar life, we set, on a long-term basis, an optimal battery cycling strategy using a simple exhaustive search algorithm. The aim of this long-term policy is to optimize the operator's return on investment taking into account the battery performance degradation, the increase of user traffic and the seasonality of the electricity prices.

In this paper, we exploit the use of unlicensed spectrum in industrial scenarios for UltraReliable Low-Latency Communication (URLLC). We consider a set of automated machines communicating with a central base station and competing to the radio resources among themselves and with enhanced Mobile Broadband (eMBB) traffic. Observing the impact of Listen-Before-Talk (LBT) on the stringent reliability and delay requirements, we propose a progressive bilevel power transmission policy for URLLC, where a high power transmission can be regarded as a preemption of eMBB packets with lower power. We formulate the objective as a stochastic optimization problem under constraints and solve it in the simple Aloha-like case, and in the more complex LBT case. We then use this model to build an online (re-)transmission algorithm which enables the URLLC stations to determine in a distributed manner the optimal policy using observations of the environment parameters.

Many Industrial Internet of Things (IIoT) use cases are characterized by a large number of users transmitting sporadically packets to a central controller. The transmitted packets have to be conveyed within a very short time so that it is not possible to make per-packet resource reservation, and contention-based access is needed, which is conflicting with the very stringent reliability constraints. In order to achieve both reliability and latency constraints, we consider in this paper the combination of contention-based access with blind replication of packets. Knowing the limited, but large, number of potential users in the system, we propose a semi-centralized channel access scheme where each user is pre-allocated positions for its replicas in case he has a packet to convey. We show, using coding theory, how to design sequences for users so that the number of collisions is minimized. We further exploit our pre-allocation scheme to develop a successive interference cancellation method where the base station tries to decode a packet based on the knowledge of the already decoded colliding packet. We show that the proposed schemes succeed to attain very low loss rates with low resource reservation.

We study in this paper the coexistence of Ultra Reliable Low Latency Communications (URLLC) and enhanced Mobile Broadband (eMBB) services in unlicensed spectrum for the uplink transmission in a 5G smart-factory scenario. We first model the medium access for both services coexisting in unlicensed spectrum and evaluate their performance metrics: high reliability and stringent delay budget for URLLC and throughput for eMBB. The results show that URLLC requirements cannot be met even for low eMBB traffic load. In order to cope with this, we explore a preemptive approach where URLLC packets are transmitted with high power when their delay approaches the delay constraint, increasing their chance of being successfully received. This approach enhances URLLC performance, with a little impact on that of eMBB. We finally show that with a good calibration of some system parameters can lead to an optimal performance for both services.

We focus in this paper on the transport of critical services in unlicensed spectrum, where stringent constraints on latency and reliability are to be met, in the context of Ultra-Reliable Low Latency Communication (URLLC). Since contention-based medium access performs poorly in the case of high traffic load, we propose a new transmission scheme where the transmitter can increase its transmission power when the delay of the packet approaches the delay constraint, increasing by that its chance of being decoded even in case of collision with other lower-power packets. We are however interested in minimizing the usage of high power transmissions, mainly to conserve energy for battery-powered devices and to limit the range of interference. Therefore, we define a transmission policy that makes use of a delay threshold after which the high-power transmission starts, and propose a new online-learning approach based on Multi-Armed Bandit (MAB) in order to identify the policy which achieves minimum energy consumption while guaranteeing reliability. However, we observe that the MAB converges slowly to the optimal policy because the loss event is rare in the load regime of interest. We then propose a model-aided learning approach where a simple analytical model helps estimating the longterm reliability resulting from an action and thus its reward. Our results show a significant enhancement of the convergence towards the optimal policy.

In this paper, we study DownLink Preemption (DLP), a feature enabling dynamic scheduling for Ultra Reliable Low Latency Communications (URLLC) in the presence of ongoing enhanced Mobile Broad Band (eMBB) flows. We design a DLP technique with the objective of maximizing the admission of URLLC packets while minimizing the impact of preemption on the eMBB throughput. We propose two different approaches to solve the DLP problem. A first, the Offline Preemption Approach (OPA), is formulated as a multi-objective Integer Linear Program (ILP) and considers a perfect knowledge of traffic dynamics. We further propose the Impact-Aware Preemption Approach (IAPA), an algorithm solving the DLP problem and performing joint admission and preemption decisions on the fly. We conduct extensive simulations using a system level simulator in realistic 5G Network settings. Our numerical results demonstrate the efficiency of IAPA in guaranteeing a close-to-optimum performance.

This article addresses communication and control aspects of platooning systems with the related challenges introduced by the overlap of both areas. The main objective is to provide a dynamic control mechanism where the parameters of the well-known Predicted Cooperative Adaptive Cruise Control (PCACC) are adapted based on the observed quality of the V2V (Vehicle-to-Vehicle) communication links. Different from the state of the art, our main design goal is the minimization of inter-vehicular distances while being robust in terms of an extremely low probability of emergency braking. A new adaptive control scheme based on the offline optimization of the control gains is proposed. We evaluate the new approach in a highway scenario and show the improvements obtained by the dynamic adaptation of the control parameters over static control strategies.

Network slicing has emerged as a promising technical solution to ensure the coexistence of the various 5G services. While the evolution of the 5G architecture for supporting slicing has been thoroughly studied, the impact of the architectural options on RAN resource allocation efficiency is still unclear. This article fills a gap in this area by assessing the impact of architecture choices on the quality of service of different services, with a focus on ultra-reliable low-latency communication applications. We propose architectural options based on the placement of the entities responsible for implementing these functions. We then assess their impact on the radio resource allocation flexibility when slices span two radio access technologies with redundant coverage. Our numerical experiments show that the placement of the slice management functions plays a crucial role in the choice of the radio resource allocation scheme that best fits URLLC slices.

We propose a novel resource allocation scheme for Ultra- Reliable and Low Latency communication (URLLC) traffic in the uplink of 5G systems. We consider industrial scenarios where a set of machines transmit packets intermittently to a central controller via a base station. We consider contention-based access in realistic scenarios where the delay budget and the 5G New Radio (NR) design allows the reception of few Acknowledgements (ACK), but always waiting for ACKs before retransmission is insufficient to ensure high reliability. This may be seen as a problem of Automatic Repeat Request (ARQ) with delayed feedback. The goal is to minimize the expected number of retransmissions subject to a reliability constraint within a delay budget. We derive the optimal policy that exploits the tradeoff between waiting for the ACK and achieving the target reliability.

We consider in this paper the transport of Ultra-Reliable Low-Latency Communication (URLLC) uplink traffic over unlicensed spectrum. We specifically consider Licensed Assisted Access (LAA) system and study the feasibility of the strict reliability and delay requirements by the means of an exact formulation which incorporates the use of a timer tracking the lifetime of each packet and identifying delay budget violation. When the delay constraint is too tight and traffic density is high, unlicensed spectrum alone is not sufficient, hence we propose the use of 5G licensed spectrum to compensate for the lacking resources. We incorporate this usage of licensed spectrum for delayed packets in the model and show how to calculate performance metrics for a given amount of additional licensed resources using grant-free allocation. We validate our analytical models with simulations and dimension the joint unlicensed/licensed system resources so as to meet both reliability and delay constraints.

The future Radio Access Network (RAN) is being engineered to accommodate the emergence of Ultra Reliable Low Latency Communications (URLLC) and to handle the possible coexistence between URLLC and enhanced Mobile Broad Band (eMBB) services. To achieve this, new features are being implemented in the future RAN to fulfill the strict requirements of the URLLC traffic in terms of latency and reliability. In this paper, we exploit the radio frame features in the New Radio (NR) and evaluate the users' performances in Downlink (DL) scheme. We conduct extensive system level simulations in realistic network deployment considering the Industry 4.0 use case. We study the impact of 1) different radio configurations and 2) the dedication of a reserved bandwidth to the critical traffic, on the URLLC achieved user plane latency and packet loss probability. The coexistence of URLLC and eMBB traffic is also evaluated by measuring the eMBB users' throughput.

Low latency targets for ultra-reliable low latency communications (URLLC) may be conflicting with their stringent reliability requirements due to the need for re-transmissions. We explore, in this paper, the different resource allocation schemes for transmissions and re-transmissions depending on the requirements of the underlying service and on the traffic characteristics, focusing on the Industrial Internet of Things. We, namely, consider schemes with individual reservation versus a pool of contention-based reserved resources. We provide novel resource allocation schemes for initial transmissions and re-transmissions and derive corresponding analytical models for loss rates. We then show how to set the system parameters that allow meeting the URLLC requirements with low resource consumption.

We focus in this paper on energy management strategies for mobile networks equipped with battery storage capacity and powered by a smart grid. We consider the case where the network operator signs an incentive based demand response contract with the energy provider, in which case the former should stop using the grid if the latter tells it to do so, typically in periods of high energy consumption. The operator gets in this case a premium, e.g. in the form of a discount on its energy bill. However, if the operator fails to comply with the energy reduction requests, it would pay a penalty. We formulate the problem using Markov Decision Process (MDP) and apply dynamic programming algorithm to devise an optimal offline policy which minimizes the network operator energy expenditure, by indicating to it when to buy energy and when to operate its network on the smart grid or on its own energy storage. We also show how the operator should manage its battery in order to respond to the energy provider requests to stop consuming from the grid at high electricity load.

Ultra-Reliable and Low Latency Communications (URLLC) is a challenging class of services to be supported by the fifth generation of mobile networks (5G). Among the URLLC services, many use cases, especially those related to factory automation, involve communications with relatively static radio conditions and a periodic generation of control or data packets. The transmission of these packets requires extremely low latency and ultra-reliable communication to enable realtime control of automation processes. In this paper, we discuss a mechanism of deterministic resource allocation to meet the URLLC requirement in terms of reliability and latency, including initial transmissions and controlled retransmissions. A joint resource allocation and modulation and coding schemes selection is performed so that the resource consumption is minimized, subject to latency and reliability constraints. We show that when applying the proposed resource allocation technique it is possible to achieve very low error rates.

In this work, we develop tractable flow level performance models for data traffic in cellular networks when users are moving inside the cell and among cells. Due to mobility radio conditions may change during transfer and classical flow level models can no more be used. We first consider an exact Markovian model that models explicitly the mobility between zones of different radio conditions during the data transfer and derive performance metrics such as sojourn and downloading times. However, as this Markovian model does not scale with the number of radio conditions and the number of admissible users, we propose a closed-form approximation model, based on a multi-class Processor Sharing. Numerical results show that the proposed model provides a very tight approximation. We then use this model in order to study the impact of mobility on different performance metrics.

We focus in this paper on energy management strategies for mobile network, equipped with battery storage capacity as well as local energy production capability, and powered by a smart grid. At each time instant, the mobile network operator has to decide whether to operate its network based on its own energy resources or the smart grid ones, with a possibility to sell energy to the smart grid as well. We formulate our problem using Markov Decision Process (MDP) and derive an optimal policy, which minimizes the telecommunication operator energy bill, using dynamic programming algorithm. We show the optimality of our solution by numerical comparison with the case of being exclusively powered by the grid. Our numerical applications allow to further understand when the operator has an incentive to buy energy, whether it is beneficial for him to act as aggregator (energy seller) as well as the size of the battery to deploy.

As video accounts for larger wireless traffic, improving users' quality of experience becomes important for network service providers. In this paper, we apply supervised machine learning technique to predict one objective QoE metric, video starvation, with the users' features, recorded at the beginning of each video session. We show that static users and adaptive streaming users have less starvation events and that mobile users are more difficult to predict their video starvation. In terms of users' features, we show that system parameters such as channel conditions and number of active users are two important features which contribute to better prediction performance. Prediction with the two features can provide sufficient accuracy for static users but not sufficient for mobile users. We also demonstrate that the two information, number of users served in a cell and the number of users experiencing video starvation, provide similar prediction accuracy.

Cloud radio access network (CRAN) has been proposed as a promising evolution of mobile network architecture where baseband processing functions of a base station are split/decoupled from the radio unit (RU) and centralized. However, rigid bandwidth and latency requirements are incurred by fronthaul, i.e., transport link, which connects RU to the central cloud. Therefore, new functional splits are discussed for CRAN with dual-site processing, which we call Hybrid-RAN (H-RAN), where some functions remain distributed while others are centralized. In this work, from a perspective of minimizing the total cost of ownership (TCO) for H-RAN, we present a techno-economic study to find the optimal functional splits for a base station (BS), with a given configuration. A configuration of a BS represents frequency layers, carrier bandwidths, and MIMO schemes, associated with different frequency bands. For each functional split, we present a model to calculate the requirement of computational resources and fronthaul bandwidth. We formulate a TCO minimization model using constraint programming. Numerical results show that the optimal functional split depends on BS configuration, fiber ownership, and data transmission direction. H-RAN with optimal functional split can achieve lower TCO than both classical Distributed RAN and CRAN.

The paper elaborates on the technological and architectural innovations researched and developed by 5G-PPP Phase 1 projects and covering innovation areas such as 5G system design and evaluation, novel air interfaces, network management and security as well as virtualization and service deployment aspects.

We consider in this paper a heterogeneous wireless network where the cell sites, known as Remote Radio Heads, are connected to a central processing unit composed of a pool of Base Band Units, so-called VirtualRadio Access Network (V-RAN). We evaluate its performance, in terms of user flow throughput, system stability and fairness under two resource allocation strategies: Dominant Resource Fairness (DRF) and a heuristic based on Proportional Fairness (PF), which we apply locally on each access network; both allocations being applied jointly on radio and processing resources. We consider the case when the V-RAN is restrictive in terms of processing resources as compared to the radio ones, and compare it to the case when the V-RAN is not restrictive. Our results show that locally-applied PF achieves higher throughput and system stability than DRF, which achieves better fairness.

In this work, we combine base station sleep mode strategies and device to device (D2D) communications to design solutions for 5G green mobile networks. The basic idea is that switching-off base stations allows to save energy while D2D technology guarantees coverage. We consider the realistic case of multi-layer cellular network and explore two sleep mode mechanisms: partial sleep mode (PSM) where only the frequency layer guaranteeing coverage at cell-edge is turned off, and deep sleep mode (DSM) where the base station is switched-off completely. Once a base station is switched-off multi-hop D2D communications are enabled to provide coverage. Cellular and D2D communications are modeled using queuing theory and sharing radio resources among them in an optimal way guaranteeing the stability of the system. We evaluate the time period of sleeping base stations in function of device battery discharging and hence the corresponding network energy savings. Results show that the combination of sleep mode strategies and D2D communications provides energy savings using an acceptable device battery discharge.

Today, mobile networks are witnessing an exponential growth of traffic volumes, linked to new services, especially for smart cities and smart-grid. The European Celtic-Plus SooGREEN project, started mid 2015, is targeting to reduce the energy consumption of the services in different mobile architectures in interaction with smart-grid. So GREEN is focused on the services energy consumption modelling and measurement, the dynamic optimization of the mobile access network and of the content delivery, the design of an Energy Efficient Virtualized and Centralized Radio Access Network (RAN), and the bi-directional interaction of the mobile network with the smart-grid. This paper presents insight into the project after its first year, and discusses research trends in green communication networks for the future.

We investigate streaming over multiple links. A file is split into small units called chunks that may be requested on the various links according to some policy and received after some random delay. After a start-up time called pre-buffering time, received chunks are played at a fixed speed. There is starvation if the chunk to be played has not yet arrived. We provide lower bounds (fundamental limits) on the starvation probability of any policy. We further propose simple, order-optimal policies that require no feedback. For general delay distributions, we provide tractable upper bounds for the starvation probability of the proposed policies, allowing to select the pre-buffering time appropriately. We specialize our results to: 1) links that employ Carrier Sense Multiple Access (CSMA) or opportunistic scheduling at the packet level; 2) links shared with a primary user; and 3) links that use fair rate sharing at the flow level. We consider a generic model, so that our results give insight into the design and the performance of media streaming over: 1) wired networks with several paths between the source and the destination; 2) wireless networks featuring spectrum aggregation; and 3) multi-homed wireless networks.

We investigate streaming over multiple links. We provide lower bounds on the starvation probability of any policy and simple, order-optimal policies with matching and tractable upper bounds.

We study in this work user impatience by adopting a realistic realization: a user gets impatient when its throughput falls below a certain threshold for a given patience duration. We model such a behavior in a cellular network, transporting elastic flows, for a realistic user configuration with random arrivals, following a Poisson process, and departure after a finite duration, either upon transfer completion or because of impatience. We derive several performance metrics, pertaining to the probability of abandoning the transfer as well as transport efficiency in terms of traffic efficiently transported until service completion versus wasted resources because of abandonment. We further implement a control mechanism using two approaches: a preventive one, based on blocking of users when their number exceeds a certain threshold, and a reactive one, based on dropping of users when the network experiences congestion, and show how the system performance gets enhanced in both cases.

This paper presents an analytical performance evaluation framework for 4G/WiFi multihoming techniques. Several multihoming flavors have been compared, with a focus on user centric allocation strategies. In these strategies the user splits his packets between two radio interfaces following a policy that depends on the amount of information he gets. First, the simplest policy is considered in which the user sends an amount of traffic on each interface proportional to the latter's peak rate. Second, the optimal selfish policy is investigated in which the user has complete information about the traffic intensities. This latter policy has been proved to achieves a global optimum. The numerical results show that an optimized user centric policy achieves good gains for both the system capacity and the user throughput compared to a peak maximization strategy. The results also show that multihoming provides large performance gains for multi-homed users while degrading neither the capacity of the system nor the performance of single-homed users.

Although adaptive streaming is becoming a preponderant technology for online streaming providers, the design of video chunks has not been well examined in the literature. This issue becomes more important in a mobile context characterized by a large heterogeneity of radio conditions between users. We develop in this paper some flow-level models to assess the impact of different video chunk durations. Our analytical and simulation results show that smaller chunk durations can improve the video smoothness and reduce buffer starvation events. However, this would require to increase HTTP signaling and to manage many headers and URLs of video chunks. To resolve this challenge, we propose to transmit multiple chunks instead of one chunk in an HTTP request. We show that our proposal can achieve better video smoothness as the case of deploying shorter chunk duration while keeping the same number of video chunks.

This paper investigates the network offloading capability of Device to Device (D2D) communication when combined with content caching. We consider a mobile network where a proportion of users are equipped with caches from which other users can retrieve contents using direct D2D communications. In this case, the offloading capacity depends both on the cache performance and the quality of the D2D links. As of the cache performance, we develop an analytical model for the cache hit rate for different content prefetching strategies. We then inject this hit rate in a queuing theory analysis of the network capacity where the macro base station and the D2D links are modeled as coupled Processor Sharing (PS) queues. We develop closed-form solutions for the stability region and the performance gains for both one-hop and two-hop D2D. Our simulation results illustrate the offloading capacity and present optimal cache and network parameters that maximize the benefit of D2D communications.

We study in this work the quality of experience of live mobile TV users in the presence of a playout buffer at the receiver side. We specifically consider an LTE network delivering both unicast and broadcast services, and study two cases: i. lossless channels where interruptions in mobile TV delivery are due to the absence of resources at the base station because they are totally consumed by higher priority real-time voice services, and ii. lossy channels, where we propose to exploit the existence of feedback channels to request, if possible, retransmission of the lost video frame. We derive in both cases several QoS metrics pertaining to mobile TV performance, such as interruption frequency and duration as well as video frame loss probability. Our results show the trends of these metrics as a function of several system parameters and yield design rules for network planning as well as for the playout buffer so as to enhance efficiently the live TV watching experience.

The Quality of Experience (QoE) of streaming service is often degraded by frequent playback interruptions. To mitigate the interruptions, the media player prefetches streaming contents before starting playback, at a cost of initial delay. We study the QoE of streaming from the perspective of flow dynamics. First, a framework is developed for QoE when streaming users join the network randomly and leave after downloading completion. We model the distribution of prefetching delay using partial differential equations (PDEs), and the probability generating function of playout buffer starvations using ordinary differential equations (ODEs) for constant bit-rate (CBR) streaming. The explicit form starvation probabilities and mean start-up delay are obtained by use of a matrix function approach. Second, we extend our framework to characterize the throughput variation caused by opportunistic scheduling at the base station, and the playback variation of variable bit-rate (VBR) streaming. Our study reveals that the flow dynamics is the fundamental reason of playback starvation. The QoE of streaming service is dominated by the first moments such as the average throughput of opportunistic scheduling and the mean playback rate. While the variances of throughput and playback rate have very limited impact on starvation behavior in practice.

This paper proposes a performance model for mobile networks carrying adaptive streaming traffic. The proposed model takes into account the flow dynamics in addition to the main parameters influencing the performance of adaptive streaming, such as the playout buffer and the video bit rates. We show how to compute several performance metrics like the average video bit rate, the deficit rate, defined as the probability of having an instantaneous throughput lower than the chosen video bit rate, and the average buffer surplus, related to the amount of data accumulated in the buffer. Considering the coexistence of multiple services and heterogeneous radio conditions that make the exact solution intractable, we propose a simple yet accurate approximation that is easy to integrate in the operator's dimensioning tools. Our numerical results investigate the performance trade-offs between the different parameters of the adaptive streaming service.

We consider in this paper an important Quality of Experience (QoE) indicator in mobile networks that is reneging of users due to impatience. We specifically consider a cell under heavy load conditions and compute the reneging probability by using a fluid limit analysis. By solving the fixed point equation, we obtain a new QoE perturbation metric quantifying the impact of reneging on the performance of the system. This metric is then used to devise a new pricing scheme accounting of reneging. We specifically propose several flavors of this pricing around the idea of having a flat rate for accessing the network and an elastic price related to the level of QoE perturbation induced by communications.

Cooperation in multi-access wireless networks have become an effective strategy for answering the rising data rate requests. The interworking is performed either by traffic steering, load balancing or yet radio resource aggregation, the so-called multi-homing. While most studies focused mainly on the first two techniques, we focus in this work on multi- homing, and target principally LTE and WiFi access networks. We specifically consider the co-existence of several classes of users in the system: single-homed users and multi- homed users, and study two network-centric, resource allocation strategies: proportional fairness applied to the global network as a whole or to each wireless access network in isolation of the other. We evaluate the system performance and show that both strategies improve the achievable throughput and stability conditions by applying a load balancing between accesses, thus increasing the overall network capacity.

During the last years, energy efficiency of wireless networks arises as a dominating concept to answer the huge energy demand due to the increase of mobile traffic. Currently, a new direction appears with powering mobile base stations by renewable energy sources. While most of the studies in this direction are based on simulation or theory, this paper analyzes the usage of renewable energy in a realistic environment. Our study is based on a live scenario, where real traffic measurements of a major European mobile network operator are used. The effects of renewable energy allocation and base stations switching- off are evaluated in terms of on-grid energy saving and electric bill reduction. Moreover, a new algorithm that combines an intelligent renewable energy allocation and a switch-off mechanism based on renewable energy generation, storage state and price of on-grid energy is proposed. Our results show that switching-off bases stations can achieve about 19 % energy demand reduction. Moreover, equipping base stations with renewable energy sources that generate 20 % of the total network energy demand can lead to 51 % reduction in the electric bill.

In this paper, we discuss several Coordinated Multi-Point (CoMP) schemes proposed for LTE- Advanced systems. We investigate their benefits in a multi-antenna beamforming system where multiple cells may share their resources and jointly coordinate their transmissions to improve the performance at cell edge and the overall system capacity. We evaluate the system performance by combining flow-level analysis with numerical results from LTE-Advanced network simulator. We show that the intra-site coordination brings significant gains in beamforming systems, especially with the joint transmission scheme where the user throughput and the system capacity are improved.

It is important for mobile operators to take video services into consideration for dimensioning. The major difficulty when considering real-time video service is the presence of two time scales: the scale of arrival/departures of flows and the packet arrival scale. In this paper, we combine flow-level dynamics with an M/D/1 model for studying the packet delay performance of real-time video services in mobile networks. We show that for the services like live TV video, the video delay constraint is large enough that it becomes of low importance when dealing with the system capacity. Using fluid model with flow level can be sufficient for calculating the maximum system load; it thus simplifies the calculations in the general case of a mix of different codecs and different radio conditions. However, for services with more stringent delay constraints, detailed packet modeling is still needed.

Carrier aggregation and multi-flow transmission are among the most important features of HSPA+. While the former allows users to be served simultaneously by several carriers in the same sector, the latter enables adjacent sectors to simultaneously schedule different data streams to the same user in their overlapping region. In this paper, we investigate the inter-operation of these two features. We evaluate the flow-level performance using a method based on network simulation coupled with Markov chain analysis. Results in single-carrier mode show an improvement in throughput at low load and an efficient load balancing across sectors at high load. In multi-carrier mode, we show that coordination is no more recommended since it does not achieve any throughput gain over the classical multi-carrier system. This is due to the actual status of the standard that limits the number of carriers that can be used for the multi-flow transmission to two. However, if this restriction is released in the standard, our results show that multi-flow transmission would bring significant gains.

In this paper, we propose analytical models to derive the performance of dual carrier mobile HSDPA mobile networks. Specifically, we analyze the flow-level performance of two inter-carrier load balancing schemes and the gain engendered by Carrier Aggregation (CA). CA is one of the most important features of HSPA+ networks; it allows devices to be served simultaneously by several carriers. We propose Volume Balancing (VB) and Join the Fastest Queue (JFQ), a load-balancing mechanism that allows the traffic of non-CA users to be distributed over the aggregated carriers. We then evaluate the performance of both CA and non-CA users by means of analytical modeling. We show that the proposed schemes achieve efficient load balancing. We also investigate the impact of mixing traffic of CA and non-CA users in the same cell and show that performance is practically insensitive to the traffic mix.

Among the several features that are foreseen for increasing the capacity of cellular systems, Multi-user multiple-input multiple-output (MU-MIMO) is considered as a key enabling technology. Particularly, MU-beamforming is a powerful means of increasing the system capacity and throughput by creating several spatial signals to different users on the same time/frequency resource. In this paper, we develop an analytical model for MU-beamforming based on queuing theory combined with network simulations. The proposed framework is used for evaluating the potential gains in terms of capacity and throughput in LTE-Advanced system. Several scenarios are studied, with and without beamforming. Results show an important gain of SU-beamforming over the classical system without beamforming and a further gain of MU-beamforming, especially at high loads.

We propose in this paper, some practical algorithms to deliver Mobile TV in a hybrid network where coexist both LTE and DVB-NGH technologies and two types of demanded video qualities: a basic single definition and another high definition. Those algorithms aim to manage the continuous reception of Mobile TV by using both infrastructures and taking decisions of TV scheduling on those operators. Two decisions policies are explored: one based on instantaneous actions and a second based on predictive actions. Results shows that the latter, which uses the Kalman filter, decreases the ping-pong between the HD reception delivered by LTE and the SD one offered by DVB-NGH, if compared to the former policy.

Our purpose in this paper is to characterize buffer starvations for streaming services. The buffer is modeled as a FIFO queue with exponential service time and Poisson arrivals. When the buffer is empty, the service restarts after a certain amount of packets are prefetched. With this goal, we propose two approaches to obtain exact distribution of the number of buffer starvations, one of which is based on Ballot theorem, and the other uses recursive equations. The Ballot theorem approach gives an explicit result. We extend this approach to the scenario with a constant playback rate using Tàkacs Ballot theorem. The recursive approach, though not offering an explicit result, allows us to obtain the distribution of starvations with non-independent and identically distributed (i.i.d.) arrival process in which an ON/OFF bursty arrival process is considered. We further compute the starvation probability as a function of the amount of prefetched packets for a large number of files via a fluid analysis. Among many potential applications of starvation analysis, we show how to apply it to optimize objective quality of experience (QoE) of media streaming, by exploiting the tradeoff between startup/rebuffering delay and starvations.

The LTE/DVB convergence to offer users a seamless Mobile TV service is at the heart of the emerging DVB-NGH standard. Most of the works addressing this issue focused solely on the impact of such convergence in terms of capacity gains brought by LTE and coverage gains brought by DVB. To the best of our knowledge, the impact of such convergence on the Mobile TV users' QoS has not been addressed yet and is the focus of our present work. We specifically evaluate the users' QoS in terms of quantifiable metrics, notably the probability of service degradation due to non availability of LTE bearers, its frequency and duration, and show how these metrics evolve with varying traffic load and in different network configurations.

The Quality of Experience (QoE) of streaming service is often degraded by frequent playback interruptions. To mitigate the interruptions, the media player prefetches streaming contents before starting playback, at a cost of delay. We study the QoE of streaming from the perspective of flow dynamics. First, a framework is developed for QoE when streaming users join the network randomly and leave after downloading completion. We compute the distribution of prefetching delay using partial differential equations (PDEs), and the probability generating function of playout buffer starvations using ordinary differential equations (ODEs). Second, we extend our framework to characterize the throughput variation caused by opportunistic scheduling at the base station in the presence of fast fading. Our study reveals that the flow dynamics is the fundamental reason of playback starvation. The QoE of streaming service is dominated by the average throughput of opportunistic scheduling, while the variance of throughput has very limited impact on starvation behavior.

We study in this work user impatience and quantify its impact on the performance of mobile networks, notably LTE networks, in the presence of data flows experiencing heterogeneous radio conditions. We consider a dynamic user setting where users come to the system at different time instants and leave it after a finite duration, either after completion of their data transfers or earlier, at the expiry of some patience duration. We derive closed-form analytical expressions for the system stationary probability distribution, in general and for some particular distributions for the impatience duration and file size, and obtain several performance metrics such as system load and mean transfer times, taking into account the heterogeneity of users locations in the cell. Our numerical results quantify these metrics, as a function of the impatience rate, as well as the traffic region where the impatience impact becomes preponderant.

High Speed-Single Frequency Network (HS-SFN) is one of the possible multipoint transmission techniques proposed in the 3GPP standard for High Speed Downlink Packet Access (HSDPA) in order to improve network performance, especially at the cell edge. It allows neighboring cells to transmit simultaneously the same data stream to a User Equipment (UE) in the Handover region (HO). In order to evaluate the user-level performance of this technique, we develop a method based on network simulation coupled with Markov chain analysis. It shows that when the HS-SFN technique is performed in the HO region between adjacent cells, the user data rates increase significantly. However, this is true only when involved cells are partially loaded, which is not always the case. We thus propose an optimized approach that adapts the coordination area based on the average offered traffic observed in the network. Network performance is then improved at any load.

Our aim in this work is to develop profit sharing strategies for coooperative 4G Long Term Evolution (LTE) and Digital Video Broadcasting - Next Generation Handheld (DVB-NGH) systems offering mobile TV service. We first study the capacity of the hybrid system for offering such a service and derive the associated cost for each network. We then focus on the sharing of profit between LTE and DVB operators as well as TV channels providers, taking into account the subscription revenues as well as the infrastructure and operation costs. We consider two cases: one in which both LTE and DVB networks are managed by a single operator and one in which the operators are separate. In both cases, we derive closed-form expressions for each player profit share using coalition game concept Shapley value. We further obtain the players optimal strategies, at the Nash equilibrium, where each player tries to maximize its own profit.

In this work, we propose a new energy efficiency metric which allows one to optimize the performance of a wireless system through a novel power control mechanism. The proposed metric possesses two important features. First, it considers the whole power of the terminal and not just the radiated power. Second, it can account for the limited buffer memory of transmitters which store arriving packets as a queue and transmit them with a success rate that is determined by the transmit power and channel conditions. Remarkably, this metric is shown to have attractive properties such as quasi-concavity with respect to the transmit power and a unique maximum, allowing to derive an optimal power control scheme. Based on analytical and numerical results, the influence of the packet arrival rate, the size of the queue, and the constraints in terms of quality of service are studied. Simulations show that the proposed cross-layer approach of power control may lead to significant gains in terms of transmit power compared to a physical layer approach of green communications.

Our purpose in this paper is to obtain the exact distribution of the number of buffer starvations within a sequence of N consecutive packet arrivals. The buffer is modeled as an M/M/1 queue. When the buffer is empty, the service restarts after a certain amount of packets are prefetched. With this goal, we propose two approaches, one of which is based on Ballot theorem, and the other uses recursive equations. The Ballot theorem approach gives an explicit solution, but at the cost of the high complexity order in certain circumstances. The recursive approach, though not offering an explicit result, needs fewer computations. We further propose a fluid analysis of starvation probability on the file level, given the distribution of file size and the traffic intensity. The starvation probabilities of this paper have many potential applications. We apply them to optimize the quality of experience (QoE) of media streaming service, by exploiting the tradeoff between the start-up delay and the starvation.

Dual carrier is a feature introduced in HSPA+ that allows scheduling a user on two carriers simultaneously. While this feature has been largely studied, and deployed, in the downlink and it has been proved that it provides significant capacity gains, its performance in the uplink is still to be investigated. This paper fills this gap by estimating the radio capacity improvement provided by dual-carrier feature in the uplink. The proposed method combines link budget simulations to assess the throughputs for a fixed number of users in the cell, and a queuing theory-based statistical capacity model, thereby providing a reliable estimate of the network radio capacity. Simulation results show that dual carrier significantly increases uplink radio capacity.

A central objective in designing LTE networks was to provide a better quality of experience for the end user. One of the implemented enhancements is a direct connection between neighbouring eNodeBs through the X2interface. This interface enables mobility management and the transfer of the user's data in case of a handover. The aim of this paper is to provide an analytical model to dimension the X2link bandwidth under certain performance objectives. We take into account radio interface conditions in order to establish handover rates and we use the Kaufman-Roberts formula to compute the needed X2bandwidth. We also study the impact of traffic management schemes (admission control and QoS differentiation) and of transport protocols on X2dimensioning.

We study, in this work, optimal sleep/wake up schemes for the base stations of network-operated femto cells deployed within macro cells for the purpose of offloading part of its traffic. Our aim is to minimize the energy consumption of the overall heterogeneous network while preserving the Quality of Service (QoS) experienced by users. We model such a system at the flow level, considering a dynamic user configuration, and derive, using Markov Decision Processes (MDPs), optimal sleep/wake up schemes based on the information on traffic load and user localization in the cell, in the cases where this information is complete, partial or delayed. Our results quantify the energy consumption and QoS perceived by the users in each of these cases and identify the tradeoffs between those two quantities. We also illustrate numerically the optimal policies in different traffic scenarios.

This paper studies the radio capacity improvement provided by an HSPA+ key feature: dual-cell, combined with MIMO. The proposed method combines drive test measurements, link-level simulations, and a queuing theory-based statistical capacity model, thereby providing a reliable estimate of the network radio capacity. Simulation results show that dual cell combined with MIMO and non-linear receivers using Successive Interference Cancellation (SIC) significantly increases network radio capacity. Those results confirm that HSPA networks evolutions are promising.

In this paper, we investigate network sleep mode for reducing energy consumption of radio access networks. We propose an offline-optimized controller that associates to each traffic an activation/deactivation policy that maximizes a multiple objective function of the Quality of Service (QoS) and the energy consumption. We focus on practical implementation issues that may affect the QoS and the stability of the system. We namely consider the activation time issue that results in the degradation of the throughput and the ping-pong effect that results in unnecessary ON/OFF oscillations. We illustrate our results numerically in beyond 3G networks.

In this paper, we show the impact of femtocells deployment on the capacity of LTE-Advanced networks. We analyze the Erlang-like capacity of a network composed of macro networks only and study the impact of introducing femtocells. We study the ability of open and closed access femtocells to offload traffic from the macro network and draw the capacity gains that are expected from this offload. Knowing that femtocells are not managed by the operator, but installed by clients, we take into account the random nature of the resulting deployment in the capacity analysis. Our results quantify the gains thus achieved, in terms of user QoS and cell load, as well as energy efficiency, as compared to a pure macro network.

In this paper, we show the impact of deploying picocells on the capacity and energy efficiency of LTE-A networks. We analyze the Erlang-like capacity of a network composed of macro networks only and study the impact of introducing a number of picocells per site. Knowing that the capacity is not the only factor that will drive the evolution of the network, we also consider the energy efficiency as a Key Performance Indicator (KPI). Our results show that, in some scenarios, introducing picocells is a good network densification method as they achieve a higher network capacity with good energy efficiency.

This paper proposes two novel contributions: a fast statistical method for scheduling gain calculation is introduced, and we show how it can be combined with queuing theory and real network measurements in order to calculate the flow-level performance of a cellular network that uses Proportional Fair (PF) scheduling. A statistical method to evaluate the scheduling gain is proposed and a proof of its convergence is given. This method is three orders of magnitude faster than full system simulation. Based on these results, a queuing theory analysis is performed and is applied to the pedestrian A 3km/h channel model and LMMSE receiver. The evaluation uses drive tests measurements to reflect the realistic distribution of radio conditions. The derived flow-level performance of PF scheduling, including blocking rate, outage rate and flow throughput, is of major interest for network operators.

Distributing Radio Resource Management (RRM) in heterogeneous wireless networks is an important research and development axis that aims at reducing network complexity. In this context, RRM decision making can be delegated to mobiles by incorporating cognitive capabilities into mobile handsets, resulting in the reduction of signalling and processing burden. This may however result in inefficiencies (such as those known as the "tragedy of commons") that are inherent to equilibria in non-cooperative games. Due to the concern for efficiency, centralized network architectures and protocols keep being considered and being compared to decentralized ones. From the point of view of the network architecture, this implies the co-existence of network-centric and terminal-centric RRM schemes. Instead of taking part within the debate among the supporters of each solution, we propose in this paper hybrid schemes where the wireless users are assisted in their decisions by the network that broadcasts aggregated load information. At some system's states, the network manager may impose his decisions on the network users. In other states the mobiles may take autonomous actions in reaction to information sent by the network. In order to improve the performance of the non-cooperative scenario, we investigate the properties of an alternative solution concept named Stackelberg game, in which the network tries to control the users' behavior by broadcasting appropriate information, expected to maximize its utility, while individual users maximize their own utility. We derive analytically the utilities related to the Quality of Service (QoS) perceived by mobile users and develop a Bayesian framework to obtain the equilibria. Numerical results illustrate the advantages of using our hybrid game framework in an association problem in a network composed of HSDPA and 3G LTE system that serve streaming and elastic flows.

Motivated by the fact that when selfish users choose their policies independently without any coordination mechanism, Nash equilibria could result in a network collapse, we develop in this paper a hierarchical distributed learning framework for decision-making in heterogeneous cognitive networks. We introduce the Nash-Stackelberg fuzzy Q-learning, with the network as leader that aims at maximizing its utility (revenue) and the mobiles as followers that have their individual objectives (maximizing their QoS). We validate our results through extensive simulations of the algorithm in a practical setting of a geographical area covered by a global HSDPA and 3G LTE system that serves both streaming and elastic traffic.

Hierarchical modulation is a means to better use the overall resources of a given system by superposing, in terms of modulation, a user with better radio conditions on one with inferior radio conditions so as to advantageously transfer some resources from the latter to the former. This in turn impacts the overall performance of the system, which we consider in this work at the flow level, for a realistic dynamic setting where users come to the system and leave it after a finite duration corresponding, for instance, to the completion of a file transfer. We quantify, in this work, analytically and via simulations, the gain thus achieved and propose a new scheme in which users with bad radio conditions are also superposed on ones with better radio conditions so as to enhance the system performance even further.

This paper studies the performance and coverage improvements introduced by relay deployment in LTE-Advanced systems. Different relay deployment strategies are investigated, namely, amplify-and-forward (AF) repeaters and decode-and-forward (DF) relays (orthogonal and non-orthogonal modes). First, the expressions of peak throughput (i.e., assuming allocate all resources in a cell to one user) for different relay deployment strategies are derived. Next, the calculation of resource partition between e-Node B (eNB) and relay nodes for the repeater and relay scenarios are detailed. A capacity evaluation method for relay deployment is then proposed; this includes the analysis of user-perceived Quality of Service (QoS) based on cell traffic. In addition, we investigate a more advanced adaptive resource sharing strategy for more efficient resource utilization among relays in a cell. Finally, the performances of the different relay deployment strategies are compared using simulation results.

The area of networking games has had a growing impact on wireless networks. This reflects the recognition in the important scaling advantages that the service providers can benefit from by increasing the autonomy of mobiles in decision making. This may however result in inefficiencies that are inherent to equilibria in non-cooperative games. Due to the concern for efficiency, centralized protocols keep being considered and compared to decentralized ones. From the point of view of the network architecture, this implies the co-existence of network-centric and terminal centric radio resource management schemes. Instead of taking part within the debate among the supporters of each solution, we propose in this paper hybrid schemes where the wireless users are assisted in their decisions by the network that broadcasts aggregated load information. We derive the utilities related to the Quality of Service (QoS) perceived by the users and develop a Bayesian framework to obtain the equilibria. Numerical results illustrate the advantages of using our hybrid game framework in an association problem in a network composed of HSDPA and 3G LTE systems.

In this paper, we analyze phenomena related to user clumps and hot spots occuring in mobile networks at the occasion of large urban mass gatherings. Our analysis is based on observations made on mobility traces of GSM users in several large cities. Classical mobility models, such as the random waypoint, do not allow one to represent the observed dynamics of clumps in a proper manner. This motivates the introduction and the mathematical analysis of a new interaction-based mobility model, which is the main contribution of the present paper. This model is shown to allow one to describe the dynamics of clumps and in particular to predict key phenomena such as the building of hot spots and the scattering between hot spots, which play a key role in the engineering of wireless networks. We show how to obtain the main parameters of this model from simple communication activity measurements and we illustrate this calibration process on real cases.

In this paper, we focus on practical issues when implementing sleep mode in base stations. Our aim being to reduce energy consumption without affecting the Quality of Service (QoS) perceived by users, we expose some problems that my occur when applying sleep mode and propose practical solutions to cope with the resulting QoS degradation. We namely consider activation time issue that may result in blocking new or handover calls, and the ping-pong effect resulting in unnecessary ON/OFF oscillations. We show, using a realistic large-scale simulator, how to choose sleep mode implementation that achieves the best tradeoff between QoS preservation and energy consumption reduction.

This paper deals with mobile-centered decision making in heterogeneous networks, where intelligent mobile terminals take autonomous decisions about the JRRM actions, consisting to connect to one of the available systems. This distributed decision-making is possible due to Q-learning algorithms implemented within the mobile terminals that enable them to profit from their past experience in order to enhance their subsequent decisions. We develop an original Markovian model that allows analyzing analytically the evolution of the Q-learning process and show how the the performance is enhanced until convergence.

In this paper, we compare the capacities of WiFi networks in their different possible implementations. These implementations include hierarchical WiFi/WiMAX networks where WiFi APs are connected to the Internet using WiMAX links, and WiFi mesh networks where WiFi APs may play the role of routers that send packets to other APs, acting as Internet gateways. At the packet level, we model both WiFi and WiMAX throughputs, taking into account inter-cell interference and radio conditions heterogeneity over the network. At the flow level, we provide a Markovian analysis that calculates the performance metrics like the blocking rate and the user-perceived throughput. The interaction between the different cells in the network, at the access and backhaul layers, is solved by a fixed point approach. Our numerical results illustrate the capacity of the network and compare the proposed solutions in different traffic scenarios.

In this paper, we evaluate the capacity gains expected when introducing HSUPA+ capable devices in the network. We first develop a flow level capacity for a cell carrying legacy UMTS devices in addition to HSPA and HSPA+ ones. We show how to calculate the QoS for both real time and elastic traffic using queuing theory analysis. Our model is next applied to assess the user-perceived QoS, i.e. the blocking probability for real time traffic and the throughput percentiles for elastic calls. We thus quantify the gains when introducing HSPA+ devices and identify the deployment scenarios that allow maximizing the operator's benefits from this introduction.

In this paper, we develop new energy-efficient, radio resource management schemes for green wireless networks. Our goal is to optimize energy consumption at the network scale while preserving the Quality of Service (QoS) perceived by users. We specifically propose two new sleep mechanisms for base station where a number of resources in system can be shut down for some traffic scenarios in one of two ways: a dynamic way where resources are activated/deactivated in real-time as a function of the instantaneous load of the system, and a semi-static one where resources are kept unchanged during longer time intervals, in the order of one hour. We apply the proposed schemes to 2G and HSPA (High Speed Packet Access) systems and show that the dynamic one achieves larger energy reductions while the semi-static one has an acceptable performance with low complexity.

In this paper, we assess the capacity of hierarchical WiFi/WiMAX networks, where end users are connected to the Internet via WiFi access points while WiMAX plays the part of backhaul. At the packet level, we model both WiFi and WiMAX throughputs, taking into account inter-cell interference and radio conditions heterogeneity over the network. At the flow level, we provide a Markovian analysis that calculates the performance metrics like the blocking rate and the user-perceived throughput. The interaction between the different cells in the network, at the access and backhaul layers, is solved by a fixed point approach. Our numerical results illustrate the capacity of the network and the impact of replacing the wired backhaul by a WiMAX one.

In this paper, we evaluate analytically the capacity gain if we use the HSDPA system in band VIII. This work is based on a statistical approach that calculates the cell capacity knowing the throughput at each distance to the base station. This throughput is obtained by thorough simulations that take into account the propagation, environment (urban/rural, indoor/outdoor) and different mobile categories. We find out that, while a large coverage gain can be expected in all situations, large capacity gains are obtained whenever the system is limited by noise rather than interference (e.g. in deep indoor situations or in rural environments). These capacity gains result, not only from propagation issues, but also from the statistical nature of elastic traffic where a larger throughput alleviates the load of the network, leading thus to even larger throughputs and capacity.

In this paper, we focus on mobile-centered decision making in heterogeneous networks. We study the case where the JRRM decision is completely distributed so that mobile users have to decide to which of the available systems it is best to connect. To optimize their decision over the time, the mobiles implement a Q-learning algorithm that enables them to profit from their past experience. We study the performance of this decision-making framework in the case of a WiMAX/HSDPA heterogeneous network and show that the mobile decision is progressively enhanced until convergence.

In this paper, we develop a novel analytical model to examine the coverage and capacity of MBSFN (MBMS over a single frequency network) OFDMA, proposed to deliver TV services to mobile users in forthcoming 3G LTE networks. First, analytical expression for SINR at a given point in a cell is derived, which takes into account the SFN gain and propagation delay values. Next, the shadowing effect of propagation on MBSFN is considered. In addition, dynamic MBSFN, an advanced MBSFN technique, in which the network cells are dynamically activated based on user traffic, is modelled. Finally, the performance of MBSFN is evaluated based on the proposed analytical model. This includes the investigation of MBSFN related parameters such as the modulation and coding scheme (MCS) values and the length of cyclic prefix, as well as the verification on the accuracy of the proposed model and the impact of shadowing. Dynamic MBSFN is also analyzed and we show its effect on the coverage for different traffic scenarios.

In this paper, we focus on the joint radio resource management (RRM) for HSDPA/WiMAX cooperation. We use a generic Markovian model to model the evolution of data calls in a multi-cell and multi-access context. We apply this model to a cooperative WiMAX/HSDPA network considering both inter-cell and vertical handovers and calculate the steady-state probabilities and the performance indicators taking into account intra-cell and inter-cell mobility. Then, we detail two RRM approaches: a game-theory based RRM approach and a RRM approach based on the maximization of the global network utility. In the first approach, the RRM decision is taken by the mobile terminals (MTs) but the network takes over if necessary, while the second approach is completely network-centric. Finally, we compare both RRM approaches on the basis of the defined performance indicators. The main results show that the game-theoretic method achieves better performances than the global-utility maximization method.

In this paper, we study the joint design of uplink and downlink resources in OFDMA-based systems. We first analyze the interactions between uplink and downlink, due essentially to the usage of time division duplexing (TDD) and the TCP ACKs. This analysis is based on analytical models for interference and throughputs that we develop for both directions. We next study the capacity of the system using a Markovian analysis and matrix geometric solutions. Our performance analysis study allows us to determine the optimal proportion of resources that has to be allocated to the uplink so as to minimize blocking. In addition to that, we show that an admission control policy reserving some uplink resources to TCP ACKs may improve the overall capacity of the system.

In this paper, we develop a generic Markovian model to study the dynamics of data service in a multi-cell context, and apply it to cells served by HSDPA and WiMAX access networks. We first present analytical models for interference and throughputs in WiMAX and HSDPA. Then, we consider a cooperative WiMAX/HSDPA network considering both inter-cell and vertical handovers and show how to calculate the steady-state probabilities and the performance indicators. Our calculations take into account mobility within the cell and between different cells and make us able to calculate intra and inter-cell dropping. We compare the performances of three joint Radio Resource Management (RRM) policies: HSDPA/WiMAX traffic distribution policy, HSDPA filling policy and WiMAX filling policy. Our numerical results show that the policy favoring WiMAX access and considering HSDPA as a rescue system (WiMAX filling policy) offers the best performances.

In this work we develop Markovian models to study the dynamics of elastic calls in a cell served by HSDPA and WiMAX systems. We first present analytical models for interference and throughputs in WiMAX and HSDPA. We then consider two different strategies of joint radio resource management (JRRM) with or without inter-system vertical handovers and show how to calculate the steady-state probabilities and the performance measures (blocking probabilities, mean sojourn times, loads). Our numerical results compare the different JRRM strategies and point out the advantages and drawbacks of each of them. We find out that, although JRRM with vertical handover achieves the best performance, the throughput maximising strategy without inter-system handovers has an acceptable performance and may be adopted in the first cooperative networks until constructors' handover solutions become more mature.

This paper presents a useful scheme to implement parallelization in 3G network planning tools. We propose a solution to the problem of interactions between the mobiles and the base stations, especially because of interference and macro- diversity in 3G systems that make direct parallelization unfeasible. The proposed solution decomposes the network into a grid of zones, and sequentially allocates independent zones (separated by a properly chosen distance) to different processors. The obtained results show a good acceleration of the simulations, thus leading to considerable time savings for operators during the phases of network planning and optimization.

Due to the introduction of new services, the volume of data transferred in mobile networks is rapidly growing and operators periodically face the necessity to upgrade their network. Such upgrades allow them to increase the capacity and provide adequate Quality of Service (QoS). In this paper we propose a general framework for deriving the optimal date for a network upgrade. We show that this date is the result of a compromise between the decrease of upgrade investment cost with time and the loss of profit generated by insufficient capacity. The upgrade should hence be performed when the loss of profit, derived using analytical capacity expressions, exceeds the expected discount. The model presented herein accounts for the randomness of the demand and upgrading cost functions, and results are given for a HSDPA network.

In this work, we study cooperation between UMTS and GSM systems. The aim is to find the best Joint Radio Resource Management (JRRM) strategies that achieve lowest blocking rates. We first present analytical models for capacity in GERAN and UTRAN. We then consider different strategies of Joint Radio Resource Management (JRRM) with or without inter-system (vertical) handovers and show how to calculate the steady-state probabilities and the performance measures (blocking probabilities, loads). Our numerical results compare the different JRRM strategies and point out the advantages and drawbacks for each of them. We show that the best performance is obtained when authorising vertical handovers. However, a simple strategy without vertical handover may, in some cases, achieve comparable performance with less signaling overhead.

In this paper, we study the performance of multi-cell OFDMA WiMAX systems. We calculate the QoS indicators (e.g. blocking rates, download time and bit error rates) taking into account the physical layer conditions (modulation and path loss), the MAC layer techniques (radio resource management algorithms) and the traffic characteristics, in a cross-layer approach. We finally evaluate the impact of using adaptive modulation and coding on the overall performance of the system. This analysis allows us to calculate the Erlang capacity of a WiMAX system. Our numerical applications then show how to choose the best modulation scheme that extends the Erlang capacity region.

In this paper, we calculate analytically the capacity of an OFDMA 802.16 system. We consider a multi-cell network and obtain the expected number of collisions for an arbitrary number of users in each interfering cell. We next obtain, for different SNR ratios corresponding to different modulation schemes, the probability of QoS degradation when collision occurs between subcarriers of two cells. We eventually calculate analytically, for a multi-service system, the proportion of symbols with poor QoS in steady-state under a given offered load. Our numerical applications show, for both adaptive and elastic traffics, how to evaluate the performance of a WiMAX system, and how to choose a modulation scheme that insures a good QoS.

In this paper, we propose a general methodology to study the performance of frequency allocation schemes in 3G LTE OFDMA systems. We first develop an analytical model for collisions in an OFDMA system for an arbitrary number of users in the different cells. We then calculate the capacity of the system using a Markov model and taking into account the inter-cell interference and its impact on the adaptive modulation. We finally apply this model to compare three frequency allocation schemes, namely reuse 1, reuse 3, and a mix of reuse 1 and 3. Our results show that a mix of reuse 1 and 3 schemes outperforms a reuse 1 scheme in terms of better cell-edge performance, and outperforms also a reuse 3 scheme by achieving an higher cell throughput

This study deals with the issue of discriminating between different classes of mobile velocity (incar, pedestrian, immobile), based on the multiresolution analysis of the measured radio signals. We make use of the decision theory in order to build an automatic classifier of radio signals. This begins by a feature extraction based on a wavelet decomposition, followed by a statistical classifier to decide about the class of the radio signal. Our method is applied on real measurement signals and the results prove that a simple wavelet-based mobility characterization is possible using the available radio measurements, even though they are averaged and under-sampled

In this paper, we focus on the capacity of a multicell UMTS system. We first determine an upper bound on the other cell interference and obtain novel expressions for the SIR and powers for both the uplink and the downlink. The former is an asynchronous CDMA system, often using one of three types of receivers : matched filter, minimum mean-square error (MMSE) and decorrelator. In the latter, the SIR depends on the distance between the user and the base station. We show that the inter-cell interference alters the capacity of the system for all kinds of receivers, though the effective bandwidth expressions remain identical to the ones obtained in the single-cell case. As an application, we show how to use this analytical model to develop a new efficient connection admission control (CAC) algorithm.

In this work, we focus on modeling of TCP in an end-to-end path where both wired and wireless, mobile sections of the network are present. The former is governed by loss and is subject to TCP retransmission. The latter is governed by error and path loss and is under data link layer control error detection/retransmission mechanism as well as UMTS-oriented error correction and power control. We first investigate the end-to-end performance of TCP in the outer-loop, with bit error ratio (BER) as a QoS metric. Second, we consider the inner-loop control case, and relate TCP performance to the signal-to-interference ratio (SIR) and the despread bit energy to interference density ratio E/sub b//N/sub 0/. Third, we review the power control algorithm and propose a novel one basing it on end-to-end TCP performance rather than UMTS channel measurements.

In this paper, we develop an efficient call admission control (CAC) algorithm for UMTS systems. We first introduce the expressions that we developed for signal-to-interference (SIR) for both uplink and downlink, to obtain a novel CAC algorithm that takes into account, in addition to SIR constraints, the effects of mobility, coverage as well as the wired capacity in the LMTS terrestrial radio access network (UTRAN). for the uplink, and the maximal transmission power of the base station, for the downlink. As of its implementation, we investigate the measurement-based approach as a means to predict future, both handoff and new, call arrivals and thus manage different priority levels. Compared to classical CAC algorithms, our CAC mechanism achieves better performance in terms of outage probability and QoS management.