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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.

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.

International audience; 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.

International audience; 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 the performance of packet scheduling schemes for Ultra-Reliable Low-Latency Communications (URLLC) services. In order to ensure a low queuing time, we exploit the redundant coverage in many 5G Radio Access Networks (RAN) scenarios, where two frequency layers or Radio Access Technologies (RAT) are integrated. We consider three packet scheduling and redundancy schemes, namely Jointhe- Shortest-Queue (JSQ), systematic Redundancy (RED), and redundancy with Cancellation upon completion (CAN). On the basis of queuing theory results, we develop expressions for the reliability, defined as the probability that the packet is transmitted before some given target delay. We show that RED has a good performance at low load, while JSQ is better when the load increases; CAN outperforms all other schemes. We then show how the obtained results can be used for the dimensioning of needed 5G radio resources and discuss the trade-off between performance and implementation complexity.

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.

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 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.

URLLC services are very demanding in terms of quality of service (QoS). We study in this paper dual-connectivity as an enabler for ultra reliability and specifically consider combining 4G and 5G spectrum for delivering URLLC packets. We compare different dual connectivity schemes, including a best link selection scheme and a maximum ratio combining scheme. We consider first an outdoor scenario with users served by macro base stations, where 4G and 5G transmitters are co-localized, and show that ultra-reliability is difficult to achieve everywhere unless the network is densified, or packets are replicated. We then study an industry 4.0 scenario where UEs are localized within a factory and served by indoor 5G small cells, in addition to coverage from outdoor 4G base stations, and show that URLLC requirements are achievable in this scenario.

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.

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.

The 5-th Generation (5G) of wireless networks is expected to support a wide set of use cases with ultra-reliable and very-low latency requirements. New Radio (NR) system design and different numerologies (subcarrier spacings) are adopted in the 3rd Generation Partnership Project (3GPP) standards in order to satisfy these stringent requirements. In this paper we study i) the user-plane latency performance of Ultra-Reliable and Low-Latency Communications (URLLC) for 15, 30, 60 and 120 kHz numerologies; ii) the service availability that is considered as essential metric for measuring the performance of the 5G networks; iii) the impact of allocating a dedicated bandwidth to URLLC services on the latency targets of URLLC packets and on the throughput of the coexisting evolved Mobile Broadband (eMBB) services.

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.

International audience; 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 Virtual-Radio 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

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.

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.

Mobile networks will support increasing numbers of connected vehicles. Successive generations of mobile networks have reduced the cost of data rate, in terms of spectrum usage and power consumption at the base station, by increasingly exploiting the concept of channel state information at the transmitter. Unfortunately, beyond a limiting velocity (which depends on the carrier frequency), networks are no longer cost efficient, since such information is not usable. Recently, channel prediction techniques requiring several antennas on the car roof have been introduced to solve this problem. In this paper, for the first time, we determine the most cost efficient configurations, in terms of numbers of antennas on the car roof and carrier frequency, in various scenarios (highway and dense urban). Our studies show that with a simple prediction technique based on predictor antennas, the network can use twice less spectrum and around 20 dB less power, for cars with 3 antennas on their tops than for cars with the same number of antennas and not using prediction.

International audience; 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

One of the objectives of METIS-II project is to facilitate discussion on scenarios, use cases, KPIs and requirements for 5G, building upon the comprehensive work conducted in the METIS-I project and taking the work of other European projects as well as other bodies such as ITU-R, NGMN, etc. into account. This paper analyses the landscape of 5G use cases and presents METIS-II 5G use cases that cover the main 5G services, have stringent requirements and whose technical solutions are expected to serve other similar use cases as well. It also links these use cases to the business cases defined by 5G PPP so that requirements of vertical industries can be taken into account when designing the 5G Radio Access Network (RAN).

International audience; 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.

The rapid growth in demand for video streaming applications is stressing the performance of wireless access networks. To alleviate congestion, vendors currently propose devices to be placed in the operator's network that transcode videos to a lower rate in order to reduce traffic volume in case of congestion. The devices are also able to cache popular videos, both to reduce the burden of transcoding and to alleviate backhaul load. The paper proposes a model of this augmented radio access network enabling an evaluation of the performance benefits for given transcoding and caching capacities. Our results show that a gain in cell capacity of 15% can be realized with moderate transcoding and cache capacities.

International audience; 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

International audience; 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.

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 in- telligent 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.

International audience; —The major motivation behind this work is to optimize the sleep mode and transmit power level strategies in a small cell cluster in order to maximize the proposed energy efficiency metric. We study the virtual multiple input multiple output (MIMO) established with each base station in the cluster equipped with one transmit antenna and every user equipped with one receive antennas each. The downlink energy efficiency is analyzed taking into account the transmit power level as well as the implementation of sleep mode schemes. In our extensive simulations, we analyze and evaluate the performance of the virtual MIMO through zero-forcing schemes and the benefits of sleep mode schemes in small cell clusters. Our results show that for certain configurations of the system, implementing a virtual MIMO with several transmit antennas can be less energy efficient than a system with sleep mode using OFDMA with a single transmitting antenna for serving multiple users.

International audience; 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

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 X 2 interface. 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 X 2 link 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 X 2 bandwidth. We also study the impact of traffic management schemes (admission control and QoS differentiation) and of transport protocols on X 2 dimensioning.

International audience; Our purpose in this paper is to obtain the \emph{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 \emph{prefetched}. With this goal, we propose two approaches, one of which is based on \emph{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.

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.

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.

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.

International audience; 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

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.

I n this paper 1 , we focus on developing energy- efficient radio resource management (RRM) algorithms for heterogeneous networks. Our aim is to optimize energy con- sumption at the network scale, while preserving the Quality of Service (QoS) perceived by users. We develop a system selection algorithm that finds the optimal traffic allocation for the different systems that minimizes power consumption while insuring the target QoS. We apply our algorithm to a cooperative 2G/3G network and show that only a little gain can be obtained, as base stations still have a large energy consumption even if they have no traffic. We then propose a network scale "sleep mode" where an entire system (2G or 3G) can be desactivated for some traffic scenarios. We show that this mode, together with the energy- aware system selection, achieves large energy reductions. consumption of the base stations. We then propose in Section III an energy-aware system selection scheme that splits the traffic between the two systems in an optimal way so that the energy consumption is minimized subject to a Quality of Service (QoS) constraint. We show that some energy savings up to 10% can be achieved using this proposed scheme. In order to further reduce energy consumption of the access network, we study in Section IV the impact of implementing a sleep mode on the systems. We show that large energy savings can be achieved provided that a small degradation of the QoS. Section V eventually concludes the paper.

In this paper, we propose a cross-layer analytical model to assess the capacity of a cellular network offering both TV and unicast (voice, data) services. Different mobile TV deployment strategies are then studied. This includes unicast-based TV and two broadcast/multicast approaches based on 3GPP MBMS, namely, Single Cell Point to Multipoint (SC-PtM) and MBSFN (MBMS over Single Frenquency Networks). We compare the different strategies from two points of view: 1. the Quality of Service (QoS) they insure for TV users and their impact on unicast services and 2. the trade off between the QoS needs of the different services is discussed and the best TV deployment strategy is given for different traffic scenarios.

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.

International audience; 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 ACK/NACKs. 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; 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 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 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 study the inter-cell interference in the uplink of 3G LTE systems. The multiple access is based on SC-FDMA. This is an orthogonal access (no intra-cell interference). However, inter-cell interference remains as there is no orthogonality between cells. We then model the inter-cell interference by calculating the distribution of the number of collisions, taking into account the impact of power control. We then use this model to calculate the user capacity of the system, by mean of a Markovian analysis. We finally use the developed model to evaluate and compare different interference mitigation schemes.

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.

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.