The deployment of IoT devices in densely deployed wireless networks presents critical challenges related to interference and collisions. Long Range-Frequency Hopping Spread Spectrum (LR-FHSS) modulation promises to enhance network resilience in comparison with LoRa modulation, particularly when combined with Successive Interference Cancellation (SIC). This study proposes an analytical model for the packet success probability in LR-FHSS with SIC, validated using extensive event driven simulation results. The latter indicate that a strategic choice of probabilistic header repetition can significantly improve reliability in dense IoT networks. This work paves the way to the LR-FHSS performance optimization, thus enabling robust communication for large-scale IoT applications.

The rapid expansion of Internet of Things (IoT) deployments demands wireless protocols that combine high scalability with robust performance. Long Range–Frequency Hopping Spread Spectrum (LR-FHSS) extends LoRaWAN by increasing capacity and resilience through frequency hopping and redundancy. However, current deployments require packet reconstruction at a single gateway, limiting the benefits of LRFHSS. This paper proposes a macro-diversity reception strategy where multiple gateways collectively receive and combine payload fragments. We develop a stochastic geometry-based analytical model that captures the impact of header repetition, payload fragmentation, and coding redundancy. Closed-form expressions quantify success probabilities under interference, and numerical evaluations demonstrate significant capacity gains over nearest-gateway reception. These results highlight the potential of fragment-level macro-diversity to improve scalability and reliability in future LPWAN deployments.

In 6G, the network is envisioned as a service embedded within a unified digital infrastructure that also provides computing, storage, and control. This infrastructure spans both horizontal and vertical planes and integrates with the Cloud Continuum, including edge data centers. The Internet of Edges aims to deploy 6G nodes with embedded micro data centers, forming a distributed edge cloud positioned near end-users or within terminal devices. Computing task requests are handled at the end-users whenever possible; otherwise, they are escalated hierarchically to higher layers. This paper addresses the challenge of identifying the best layer at which Resource-Intensive computation is to be processed, especially computation related to Machine Learning (ML) tasks. In particular, Federated Learning (FL) will enable edge devices to collaboratively train ML models while preserving data privacy. However, constrained devices face challenges such as high prediction errors due to limited dataset sizes and energy consumption cost associated with FL. To address these issues, this paper proposes to adequately characterise the computing placement depending on the device characteristics. More energy constraint devices need to choose between local learning (on device) or federated learning through an edge server. Conversely, devices doted with more capacity need to federate the ML cooperatively in learning coalitions. Both cases will be considered through the lens of game theory. Extensive numerical results highlight the effectiveness of our approach, showcasing its ability to enhance learning while minimizing energy cost.

This paper presents the first comprehensive real-world measurement campaign comparing LR-FHSS and LoRa modulations within LoRaWAN networks in urban environments. Conducted in Halifax, Canada, the campaign used a LoRaWAN platform capable of operating both modulations in the FCC-regulated US915 band. Real-World measurements are crucial for capturing the effects of urban topology and signal propagation challenges, which are difficult to fully replicate in simulations. Results show that LR-FHSS can achieve up to a 20% improvement in Packet Reception Rate (PRR) over traditional LoRa in dense urban areas. Additionally, the study investigated path loss and Received Signal Strength Indicator (RSSI), finding that LR-FHSS achieved a minimum RSSI of −138 dBm compared to LoRa’s −120 dBm. The findings demonstrate that the introduction of LR-FHSS enhances communication robustness and reliability under regulatory limitations and suggest promising applications in LoRaWAN networks.

Federated Learning (FL) enables edge devices to collaboratively train machine learning models while preserving data privacy. However, in IoT networks, constrained devices face challenges such as high prediction errors due to limited dataset sizes and the computational costs associated with FL tasks. To address these issues, this paper proposes a two-level resource management framework for IoT Federated Learning. The first level focuses on forming homogeneous learning clusters with mandatory minimal dataset size where devices with similar computational power and data distribution are grouped together to optimize learning performance. The second level employs a two-stage Stackelberg game to incentivize devices to contribute larger datasets by offering monetary rewards, balancing the trade-off between prediction accuracy, computational costs, and energy consumption. Our framework shows significant improvements in prediction accuracy and overall cost reduction compared to the flat network approach, where all devices are grouped together.

The exponential growth of IoT devices presents critical challenges in bandwidth-constrained wireless networks, particularly regarding efficient data transmission and privacy preservation. This paper presents a novel federated semantic communication (SC) framework that enables collaborative training of bandwidth-efficient models for image reconstruction across heterogeneous IoT devices. By leveraging SC principles to transmit only semantic features, our approach dramatically reduces communication overhead while preserving reconstruction quality. We address the fundamental challenge of client selection in federated learning environments where devices exhibit significant disparities in dataset sizes and data distributions. Our framework implements three distinct client selection strategies that explore different trade-offs between system performance and fairness in resource allocation. The system employs an end-to-end SC architecture with semantic bottlenecks, coupled with a loss-based aggregation mechanism that naturally adapts to client heterogeneity. Experimental evaluation on image data demonstrates that while Utilitarian selection achieves the highest reconstruction quality, Proportional Fairness maintains competitive performance while significantly reducing participation inequality and improving computational efficiency. These results establish that federated SC can successfully balance reconstruction quality, resource efficiency, and fairness in heterogeneous IoT deployments, paving the way for sustainable and privacy-preserving edge intelligence applications.

This work proposes a novel approach to channel sensing in cognitive radio systems, drawing inspiration from reinforcement learning theory. While the adversarial Multi-Armed Bandit framework is commonly used to manage diverse channel sampling, it struggles to accurately assess resource occupancy due to geographical variations in channel availability across devices. To address this limitation, collaboration among devices is essential and can be effectively achieved through Federated Learning (FL). FL integrated into a Multi-Armed Bandit framework addresses key challenges, including data heterogeneity, the high cost of data centralization, privacy concerns, and biased learning. We enhance the widely-used Exponential-weight algorithm for exploration and exploitation (EXP3) by incorporating federation, allowing learning devices to collectively identify channels with less interference. Our simulation results demonstrate the effectiveness of the federated EXP3 (F-EXP3) algorithm by comparing it with the traditional EXP3 and the federated Upper Confidence Bound (UCB). The experiments reveal that F-EXP3 overcomes the limitations of individual learning, leading to superior channel selection performance.

Edge computing is an efficient way to help constrained IoT devices by offloading heavy tasks on edge servers, especially computing tasks related to Machine Learning (ML). Moreover, such devices can only store a limited amount of data because of their reduced capacity. Consequently, ML is bound to be smeared with relatively high error prediction as these devices resort to a small training dataset for their learning. To mend that issue, IoT devices can group in clusters and resort to Federated Learning (FL) with their pairs in the same cluster or coalition. However, the learned model needs to be transmitted repeatedly over a wireless access network, which is energy consuming. Hence, although learning collectively through FL can reduce the learned model variance, it inflicts a communication cost, dependent on the coalition size, that must be taken into account. Therefore, a cost function is devised astutely by factoring in both the prediction error and communication cost in a learning cluster. Then, a coalition formation game is conceived to minimize the devised cost function. Autonomous IoT devices will engage in the proposed game leading to coalitions of optimal size. Once clusters are formed, distributed FL is applied in any cluster in order to reduce the learning error of participating devices while curbing their communication cost.

Computational offloading is an efficient way to help constrained IoT devices by performing heavy tasks on Edge servers, especially tasks related to Machine Learning. Moreover, due to their limited learning capacity and memory size, such devices can only store a limited amount of data as a training set for their learning. Consequently, learning prediction is bound to be smeared with relatively high error. To mend that issue, IoT devices can federate the learning process with their pairs via an Edge server. However, offloading repeatedly the learning model through a wireless access network is time consuming. Hence, although learning collectively can reduce the learned model variance, it inflicts a communication cost depending on the selected Edge server. Therefore, in this paper, we model the Edge Selection problem as a non-cooperative game where devices autonomously and efficiently select an Edge server to reduce both their learning error and their communication cost. Depending on the characteristics of the dataset, we discern two different types of games. For each game type, we implemented and compared a semi-distributed algorithm based on Best Response dynamics. We compared the obtained results with the optimal centralized approach and with a less computationally intensive meta-heuristics, to assess the price of anarchy. Our numerical analysis shows that the Best Response algorithm strikes a good balance between efficiency and swift convergence.

Computational offloading is a pivotal solution to several Internet of Things (IoT) issues as it helps subdue the constrained nature of IoT devices. By harnessing the large capacity at the Edge, IoT devices with limited battery and storage can delegate certain tasks, especially those related to Machine Learning. Because of their restricted capacity, such devices can only store a limited amount of data as a training set for their learning, leading to a faulty prediction with high error rate. To tackle that issue, IoT devices can federate the learning process with other devices while the Edge server acts as an aggregator. However, selecting the appropriate Edge is a significant challenge. In fact, although learning collectively can reduce the prediction error, it also brings about a communication cost that depends on the selected Edge. Thus, in this paper, we propose a Non-Cooperative game where devices autonomously and efficiently select an Edge server in order to reduce both their learning error and communication cost.

With Fifth Generation (5G) mobile networks, the concept of Bandwidth Part (BWP) was introduced to support flexible Orthogonal Frequency-Division Multiplexing (OFDM) subcarrier spacing, also deemed numerologies to achieve lower latency for delay stringent services. A BWP is a set of contiguous Physical Resource Blocks (PRBs) associated to a numerology which is scanned by the User Equipment (UE) to retrieve or submit its data. Additionally, a UE can be connected to multiple slices or services simultaneously thanks to slicing which was introduced in 5G to ensure isolation among multiple co-existing services on the same network. In this context, a BWP switch is required for such users when their services use different numerologies. In this paper, we propose three innovative methods to optimize the BWP Switching process for users connected to multiple slices where both enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communications (URLLC) services are considered. These mechanisms rely on the modification of the Downlink Control Information (DCI) which carries the BWP indicator for users as well as the BWP Inactivity Timer that triggers a Default BWP switch. The performance evaluation demonstrates the high efficiency of our solutions in terms of latency for URLLC services, throughput for eMBB services and number of DCIs scanned compared to the baseline approach.

In this paper, we propose and implement LoRaDANCE, a mechanism that allows a LoRaWAN end device to join a LoRaWAN network with reinforced security compared to the standard LoRaWAN protocol. LoRaDANCE uses the DNS-Based Named Entities (DANE) protocol to enable mutual authentication between the end device and the backend servers. It also uses asymmetric cryptography to avoid the use of pre-shared secret keys. We illustrate LoRaDANCE, implement it on real devices, and evaluate its performance. Our results show that while LoRaDANCeallows devices to successfully join the network with enhanced security, it introduces an overhead, particularly in terms of join process duration and power consumption. However, the overhead is deemed acceptable, given the significant security improvements that LoRaDANCE provides.

Federated learning provides access to more data which is paramount for constrained LoRaWAN devices with limited memory storage. Learning on a larger data set will reduce the variance of the learned model, hence reducing its error. However, federating the learning process incurs a communication cost among learning devices that must be taken into account. In this paper, we formulate a Cooperative Hedonic game and introduce a new cost function that captures both the learning error and communication cost. LoRaWAN devices engage in the devised game by identifying if they should keep their learning local or federate with other devices in order to reduce both their learning error and communication cost. We compute the optimal size of formed coalitions and assess their stability. Then, we show through extensive simulations that devices have incentive to form learning coalitions depending on the data characteristics at hand and the communication cost in LoRaWAN.

This paper addresses the multi-tenant radio access network slicing in 5G networks. The infrastructure provider (InP) slices the physical radio resources so as to meet differentiated service requirements, and the mobile virtual network operators (MVNOs) then dynamically request and lease isolated resources (from the slices) to their services. In this context, we propose a two-level single-leader multi-follower Stackelberg game to jointly solve the resource allocation and pricing problem. The InP prices its radio resources taking into account MVNO allocations, which in turn depend on the resource cost. Simulation results show that, in comparison with the Static Slicing approach, our solution achieves an efficient trade-off between MVNO satisfaction and InP revenue, while accounting for 5G service diversity and requirements.

For a seamless deployment of the Internet of Things (IoT), lightweight self-organizing solutions are needed to overcome the challenges of IoT, mainly stemming from their constrained nature. The present work is devoted to a specific IoT context, that of LoRaWAN, where devices communicate with the access network via pure ALOHA-type access. In fact, the conception of LoRaWAN advocates simplicity in order to reduce drastically the battery consumption. This simplicity in the contention access method severely degrades reliability. To address that shortcoming, the adoption of Successive Interference Cancellation (SIC) in conjunction with packet repetition has been lately considered as a promising solution for the particular setting of IoT. However, the proposed solutions were devised for synchronised systems and particular care is needed to fit these solutions for the asynchronous LoRaWAN networks. In this paper, we put forward such a tailored mechanism, coined RALI (Repetition in ALOHA for LoRaWAN with Iteration), assess its performances analytically and demonstrate through extensive simulations its notable efficiency.

In this paper, we aim to tackle the challenges of resource block scheduling and power allocation in the context of multi-cell full-duplex wireless networks. This is a more realistic setting than the single cell scenario, and it better envisions how full-duplex wireless communications could eventually be implemented. We propose an optimal queue-aware joint scheduling and power allocation algorithm for full-duplex wireless networks in a multi-cell scenario. Because of its mathematical intractability, we decouple the problem and solve it for scheduling first, and for power allocation second. We consider both indoor and outdoor scenarios and show that the gains of multi-cell full-duplex wireless networks, with respect to their half-duplex counterparts, are not always prevalent. Furthermore, we highlight the importance of inter-cell cooperation when it comes to scheduling resources and show that depending on the scenario at hand, interference mitigation from inter-cell cooperation can improve the performance of user equipment in terms of throughput and waiting delay. Finally, we show that power allocation can improve user equipment throughput with its efficiency being tied to the deployment scenario at hand.

Dense deployment of small cells operating on different frequency bands based on multiple technologies provides a fundamental way to face the imminent thousand-fold traffic augmentation. This heterogeneous network (HetNet) architecture enables efficient traffic offloading among different tiers and technologies. However, research on multi-tier HetNets where various tiers share the same microwave spectrum has been well-addressed over the past years. Therefore, our work is targeted towards novel multi-tier HetNets with disparate spectrum (microwave and millimeter wave). In fact, despite the huge capacity brought by millimeter-wave technology, the latter will fail to provide universal coverage, especially indoor, and so mmWave will inevitably co-exist with a traditional sub-6GHz cellular network. In this work, we propose a coordinated user association and spectrum allocation by resorting to non-cooperative game theory. In fact, in such an arduous context, efficient distributed solutions are imperative. Extensive simulation results show the precedence of our coordinated approach in comparison with state-of-the-art heuristics. Moreover, we evaluate the impact of various network parameters, such as mmWave density, cell load, and user distribution and density, offering valuable guidelines into practical 5G HetNet design. Finally, we assess the benefit brought by massive MIMO for mmWave in such a highly heterogeneous setting.

The Internet of Things (IoT) aims to connect billions of devices, most of which are power and memory-constrained. Such constraints require efficient network access. “Irregular Repetition Slotted Aloha” (IRSA) meets such requirements. In this paper, we optimize IRSA using Deep Reinforcement Learning to obtain Deep-IRSA, and introduce variants that allow retransmission and user priority classes. We observe the learned degree distribution and throughput, showing that Deep-IRSA performs excellently, is generic, and could well replace known approaches for smaller frame sizes and IRSA variants.

Fifth generation mobile networks (i.e, 5G) are not only characterized by higher capacity and throughput, but also by the support of heterogeneous services, characterized by different Quality of Service (QoS) requirements in terms of throughput, delay or energy consumption. Such multi-service networks require the partitioning of the available radio resources into mutliple slices, where each service occupies one slice in an isolated manner. However, a static isolation scheme among slices might have a negative impact on radio resource efficiency. For example, when one slice is highly loaded, while the second is lowly loaded, the resources of the first slice are over-utilized, whereas the resources of the second are under-utilized. Consequently, a Dynamic Radio Access Networks (RAN) slicing algorithm is paramount so as to adapt to the different load conditions of services. In that way, the radio resource utilization efficiency is increased, while the different QoS requirements of services are satisfied. In this paper, we tackle this problem by proposing a Dynamic RAN Slicing algorithm based on a crowding game. The algorithm efficiently allocates the resources among heterogeneous RAN slices in a way to satisfy the different QoS requirements of users belonging to different services while increasing the resource utilization efficiency. The results show that our proposed solution overcomes the Static Scheme in terms of QoS guarantee and utilization efficiency.

Faced with the limitations of the Aloha random access scheme and spread spectrum techniques, LoRaWAN is yet to realize its potential as the flagship technology for large-scale Internet-of-Things applications. LoRaWAN allows for low power and long range communications. Nonetheless, concurrent transmissions on the same spreading factors (SFs), increased with the inevitable densification of device deployment, will lead to collisions and degradation in performance. The problem is further amplified due to the shortage in radio resources, with multiple operators utilizing the same unlicensed frequency bands. In this article, we investigate different interoperator cooperation schemes and devise multiple algorithms for SF assignment in a multioperator LoRaWAN deployment scenario. We start by proposing a proportional fair optimal formulation for the assignment with the objective of maximizing the logarithmic sum of the normalized throughput per SF. Under the assumption of partial operator cooperation, we propose a gradient ascent-based iterative algorithm for solving the SF assignment problem, and a game theory-based approach, wherein each network operator seeks to maximize its own normalized throughput. Finally, and with cooperation between different operators bound to be limited, we use recurrent neural networks to enable the prediction of the success rate per SF. This prediction allows the different operators to assign SFs with minimum cooperation. We simulate our proposals and compare them to the legacy LoRaWAN approach as well as others in the state of the art, highlighting the gains they produce in terms of total normalized throughput and packet delivery ratios.

Due to highly variant traffic in downlink (DL) and uplink (UL) in heterogeneous networks (HetNets), dynamic time-division duplexing (TDD) is proposed to dynamically allocate UL and DL resources. Under the same circumstances, downlink and uplink decoupled access (DUDA) is introduced to balance between UL and DL transmissions and to further improve the system performance. Rather than belonging to a specific cell, a mobile user can receive the downlink traffic from one base station (BS) and send uplink traffic through another BS. In this article, we analytically investigate a joint TDD and DUDA statistical model with multiple small cells deployment. This model is based on a geometric probability approach. Taking all possible TDD subframes combinations between the macro and small cells, coupled and decoupled cell associations strategies are investigated in details. We derive analytical expressions for the capacity and the interference, considering a network of one macro cell and multiple small cells. We build on the derived capacity expressions to measure the decoupling gain and thus, identify the location of the interferer small cell where the decoupled mode maintains a higher gain in both DL and UL. Monte-Carlo simulations results are presented to validate the accuracy of the statistical model.

Dynamic time-division duplexing (TDD) enables flexible adjustments of uplink (UL) and downlink (DL) resources according to the instantaneous traffic load. However, it also brings new challenges in heterogeneous cellular networks (HetNets) because of the introduction of cross-link interference i.e., uplink to downlink interference and downlink to uplink interference. One step further in the optimization of HetNet, is the interdependency between UL and DL and how the association policies affect the system performance on both links in a way to mitigate the cross-link interference. In classical HetNets, coupled UL/DL access (CoUD) mode is adopted, where each user is associated in downlink and uplink with a single cell. However, the power imbalance between the macro cells and the small cells motivates the decoupling of both links. In the next generation HetNets, instead of being connected to a specific cell, a mobile user can independently receive the downlink traffic from one base station (BS) and transmit uplink traffic through another BS. This situation is referred to as decoupled uplink and downlink (DeUD) access. The optimization of a HetNet based system according to time-variant traffic loads necessitates finding a system level simulator where we can present the motivation and accurately assess the role of both decoupling and dynamic TDD techniques. In this paper, we resort to a system level simulator under which we develop a new module that investigates the dynamic TDD technique along with multiple association policies in a dense HetNet deployment. We create appropriate simulation environment that is relative to real scenarios i.e. simulations where multiple small cells are deployed in a heavy loaded HetNet system and under various traffic loads.

For a seamless deployment of the Internet of Things (IoT), self- managing solutions are needed to overcome the challenges of IoT, including massively dense networks and careful management of constrained resources in terms of calculation, memory, and battery. Leveraging on artificial intelligence will enable IoT devices to operate autonomously by using inherently distributed learning techniques. Fully distributed resource management will free devices from draining their limited energy by constantly communicating with a centralized controller. The present work is devoted to a specific IoT context, that of LoRaWAN, where devices communicate with the access network via ALOHA-type access and spread spectrum technology. Concurrent transmissions on different spreading factors increase the network capacity. However, the bottleneck is inevitable with the expected massive deployment of LoRa devices. To address this issue, we resort to the popular EXP3 (Exponential Weights for Exploration and Exploitation) algorithm to steer autonomously the decision of LoRa devices towards the least solicited spreading factors. Furthermore, the spreading factor selection is cast as a proportional fair optimization problem used as a benchmark for the learning-based algorithm. Extensive simulations were run in a realistic environment taking into account physical phenomena in LoRaWAN such as the capture effect and inter- spreading factor collision, as well as non- uniform device distribution. In such a realistic setting, we evaluate the performances of the EXP3.S algorithm, an efficient variant of the EXP3 algorithm, and show its relevance against the fair centralized solution and basic heuristics.

The benefits of full-duplex wireless communications, specifically with respect to their half-duplex counterparts, are now well under examination. As a result, research into the corresponding scheduling and power allocation algorithms has thrived. In this paper, we propose a non-cooperative game theoretic algorithm for power allocation in full-duplex orthogonal frequency division multiple access networks. The game is played between user equipment on the uplink, and the base station on the downlink. The objective of the game is two-fold: maximizing the signal-to-noise-plus-interference ratio, while hindering the harmful interferences resulting from full-duplex operation. We prove that our game is super-modular. For such a game, a best response algorithm is capable of attaining a Nash equilibrium. We simulate our proposal along with a fairness based scheduling algorithm and show that it improves user equipment throughput and reduces the waiting delay.

In 5G networks, Mobile Edge Computing (MEC) has been proposed to enable computation and storage capabilities at the edge of Radio Access Networks. Proactive content caching in MEC is crucial to guarantee users' Quality of Experience thanks to the reduction of traffic latency. Predicting content popularity plays a key role in the effectiveness of proactive caching. In this paper, we propose a generic and flexible recommendation framework which allows recommending suitable learning and prediction algorithms among available ones, in order to predict content popularity. The investigated algorithms are categorized into two main classes: tree-based regressors and recurrent neural networks. Through the study case of YouTube video solicitation profiles, our proposed method, called Imputation-Boosted Collaborative-Filtering based Recommending Prediction Method (IBCF-RPM) shows its effectiveness in the prediction of content popularity for various popularity profiles. By running only 30% of the prediction algorithms, randomly chosen, on a given content profile, the proposed recommending method is able to estimate the accuracy of the other predictors and recommend a well-suited predictor for content popularity.

Time-division duplexing (TDD) systems can allow a dynamic adjustment of uplink and downlink resources according to the time-variant traffic loads. The interference generated by the concurrent uplink and downlink transmission in different cells, brings a new challenge to interference modeling in cellular networks. In this paper, an analytical framework is developed to evaluate the performance of a TDD system in heterogeneous networks (HetNets) which considers a concurrent uplink and downlink transmission in two different types of cells, macro and small cells. Firstly, we derive an analytical expression for the distribution of the interferer location considering all possible interference scenarios that could occur in TDD- based networks while taking into account the harmful impact of interference. Secondly, based on the latter result, we derive the distribution and moment generating function (MGF) of the uplink and downlink inter-cell interference considering a network consisting of one macro cell and one small cell. Finally, we build on the derived expressions to analyze the average capacity of the reference cell in both uplink and downlink transmissions. Monte- Carlo simulation results are provided to demonstrate the accuracy of the derived analytical expressions, in various realistic scenarios.

Due to load traffic disparity in downlink (DL) and uplink (UL) in heterogeneous cellular networks (Het-Nets), dynamic time-division duplexing (TDD) is proposed to dynamically allocate UL and DL resources. Under the same circumstances, decoupled UL/DL access is introduced to balance between UL and DL transmissions and to further improve the overall system performance. In classical HetNets, coupled UL/DL access (CUDA) mode is adopted, where each user is associated in downlink and uplink with a single cell. In the next generation HetNets, rather than belonging to a specific cell, a mobile user can receive the downlink traffic from one base station (BS) and send uplink traffic through another BS. This situation is referred to as downlink and uplink decoupled access (DUDA). In this paper, we analytically investigate a joint TDD and DUDA statistical model based on a geometric probability approach. Taking all possible TDD subframes combinations between the macro and small cells, four coupled and decoupled cell associations strategies are investigated in details. We derive inter-cell interference and capacity expressions for each of these association policies to assess the improvement brought by DUDA mode to TDD HetNets. In this particular DUDA mode, users are associated in UL with the small cell and in DL with the macro cell. Moreover, we identify the small cell offset under which the decoupled mode performs significantly better and maintains a higher spectral efficiency in both DL and UL. Monte-Carlo simulations results are presented to validate the accuracy of the analytical model.

Cloud Radio Access Network (C-RAN) is an evolution in the base station architecture, mainly composed of two elements: The Base Band Unit (BBU) and the Remote Radio Head (RRH). This new architecture separates the two elements (the BBUs from RRHs), to group all the BBUs into one cloud, while RRHs are distributed across many sites. Unlike in conventional architecture, many RRHs can be associated to one BBU when traffic load is low, allowing better exploitation of radio resources. A dynamic re-association between BBUs and RRHs is crucial, since coping with traffic load can enable reduction in power consumption and enhancement in energy efficiency. However, re-associations between BBUs and RRHs would cost frequent handovers for users connected to re-associated RRHs. Unlike the existing studies, the aim of this paper is to solve the dynamic aspect of the BBU-RRH association. To that end, we introduce a tunable bi-objective optimization problem formulated as a Set Partitioning Problem (SPP). The purpose is to jointly minimize the power consumption and the re-association rate of users experiencing a BBU change, subject to a minimum guarantee of Quality of Service (QoS). A heuristic approach is also introduced and provides close performance to the optimal solution.

The target of this paper is to propose a practical low-complexity power allocation algorithm that strikes a good balance between Spectral Efficiency (SE) and power saving for the downlink of interference-limited cellular networks. Because abundant interference usually results from dense frequency reuse and high power transmission, power optimization schemes are critical to interference management in wireless systems. Powerful power optimization schemes can be efficiently implemented in the framework of Self-Organizing Network (SON). In this context, we resort to non-cooperative game theory to devise two distributed power allocation schemes. By only considering SE, our first Power Control Game (PCG) algorithm, deemed SE-PCG, provides high SE but push autonomous eNBs into consuming all available power. To address this shortcoming and enhance Energy Efficiency (EE), we put forward another PCG algorithm, deemed EE-PCG, which inflicts a penalty on power consumption. The originality of our scheme lies in deriving the power penalty through a signaling-free heuristic. We have analyzed the proposed algorithms through extensive numerical simulations and compared them with the state-of-the-art approaches. The results have shown that our algorithms outperform the latter.

Cloud Radio Access Network (C-RAN) is a promising technology to improve user quality of service and reduce network capital and operating costs. The key concept behind C-RAN is to break down the conventional base station into a Base Band Unit (BBU) and a Remote Radio Head (RRH), and to pool BBUs from multiple sites into a single geographical point. Moreover, to achieve statistical multiplexing gain, RRHs should be efficiently clustered: many RRHs may be mapped into a single BBU. In this article, RRH clustering is formulated as a coalition formation game where RRHs collaborate and organize themselves into disjoint independent clusters, in a way to optimize network throughput, power consumption, and handover frequency. An optimal centralized solution, based on exhaustive search, is presented. We also propose a distributed algorithm, based on the merge-and-split rule, to form RRH clusters. Simulation results show that our centralized solution adapts to network load conditions and outperforms the no-clustering method, where only one RRH is assigned to each BBU, and the grand coalition method, where all RRHs are assigned to a single BBU. More importantly, our distributed algorithm achieves very close performance to the optimal solution, with significantly lower computational complexity.

The focus of this paper is targeted towards multi-cell 5G networks composed of High Power Node (HPNs) and of simplified Low Power Node (LPNs) co-existing in the same operating area and sharing the scare radio resources. Consequently, high emphasis is given to Inter-Cell Interference Coordination (ICIC) based on multi-resource management techniques that take into account user association to cells. Beside user association, this paper takes also power control and scheduling into consideration. This complex problem is remained largely unsolved, mainly due to its non- convex nature, which makes the global optimal solution difficult to obtain. We address the user association challenge according to the two broadly adopted approaches in wireless networks: the network-centric approach where user association is allocated efficiently in a centralized fashion; and the user-centric approach where distributed allocation is used for reduced complexity. The scheduling and ICIC power control are solved in a centralized fashion, in order to reach an optimal solution of the joint optimization problem.

Mobile network operators are facing the challenge to increase network capacity and satisfy the growth in data traffic demands. In this context, long‐term evolution (LTE) networks, LTE‐advanced networks, and future mobile networks of the fifth generation seek to maximise spectrum profitability by choosing the frequency reuse‐1 model. Owing to this frequency usage model, advanced radio resource management and power allocation schemes are required to avoid the negative impact of interference on system performance. Some of these schemes modify resource allocation between network cells, while others adjust both resource and power allocation. In this study, the authors introduce a cooperative distributed interference management algorithm, where resource and power allocation decisions are jointly made by each cell in collaboration with its neighbouring cells. Objectives sought are: increasing user satisfaction, improving system throughput, and increasing energy efficiency. The proposed technique is compared with the frequency reuse‐1 model and to other state‐of‐the‐art techniques under uniform and non‐uniform user distributions and for different network loads. They address scenarios where throughput demands are homogeneous and non‐homogeneous between network cells. System‐level simulation results demonstrate that their technique succeeds in achieving the desired objectives under various user distributions and throughput demands.

The emphasis of this paper is put on 5G multi-cell networks which are composed of dense and mutually interfering evolved NodeBs (eNBs) sharing the scarce radio resources. Consequently, greater focus is given to resource management techniques that take Inter-Cell Interference (ICI) into account, in particular to power control. Beside power control, this paper tackles also user association and scheduling. Despite the relevance of the addressed problem, it has remained largely unsolved, mainly due to its non-convex and combinatorial nature. We address this multifaceted challenge in a distributed fashion for reduced complexity.

Traditional design of wireless networks mainly focuses on system capacity and spectral efficiency. As green networking is an inevitable trend, energy-efficient design for future wireless networks becomes paramount. In this paper, we address energy-efficient resource management in downlink orthogonal frequency division multiple access networks. The focus is targeted toward multi-cell networks, which are composed of multiple base stations (BSs) sharing the available radio resources. Consequently, greater emphasis is given to techniques that take inter-cell interference into account. Resource management in our context refers to the task of allocating the radio resources in order to maximize energy efficiency. We devise resource management techniques that jointly tackle the problems of scheduling and power control. Accordingly, we adopt two different approaches: a centralized approach, where BSs coordinate in order to reach a globally optimal energy-efficient solution, and a distributed approach, where BSs selfishly strive to maximize their own energy efficiency. We portray the centralized approach as a convex optimization problem, whereas we have recourse to non-cooperative game theory to model the distributed approach. In particular, we show that the non-cooperative game converges to a unique Nash equilibrium in low- and high-interference scenarios. We perform thorough numerical simulations to quantify the discrepancy between the centralized and distributed approaches, and identify the conditions where they have precedence over the state of the art. Moreover, the simulation results highlight the fast convergence of our algorithms, which is a precious asset for realistic deployments.

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

One major concern for operators of Long Term Evolution (LTE) networks is mitigating inter-cell interference problems. Inter-Cell Interference Coordination (ICIC) techniques are proposed to reduce performance degradation and to maximize system capacity. It is a joint resource allocation and power allocation problem that aims at controlling the trade-off between resource efficiency and user fairness. Traditional interference mitigation techniques are Fractional Frequency Reuse (FFR) and Soft Frequency Reuse (SFR). FFR statically divides the available spectrum into reuse-1 and reuse-3 portions in order to protect cell-edge users, while SFR reduces downlink transmission power allocated for cell-center resources to protect vulnerable users in the neighboring cells. However, these static techniques are not adapted to non-uniform user distribution scenarios, and they do not provide guarantees on throughput fairness between user equipments. In this paper, we introduce a non-cooperative dynamic ICIC technique that dynamically adjusts resource block allocation according to user demands in each zone. We investigate the impact of this technique on throughput distribution and user fairness under non-uniform user distributions, using an LTE downlink system level simulator. Simulation results show that the proposed technique improves system capacity, and increases throughput fairness in comparison with reuse-1 model, FFR and SFR. It does not require any cooperation between base stations of the LTE network.

The focus of this paper is targeted towards multi-cell dense LTE and LTE-Advanced networks, which are composed of multiple evolved Node B (eNodeB) co-existing in the same operating area and sharing the available radio resources. In such scenarios, momentous emphasis is given towards the techniques that take Inter-Cell Interference (ICI) into account while allocating the scarce radio resources. In this context, we propose solutions for the problem of joint power control and scheduling in the framework of Inter-Cell Interference Coordination (ICIC) in the downlink of LTE OFDMA-based multi-cell systems. Two approaches are adopted to allocate system resources in order to achieve high performance: a centralized approach based on convex optimization and a semi-distributed approach based on non-cooperative game theory. The centralized approach needs a central controller to optimally allocate resources like in LTE CoMP (Coordinated Multipoint). In the semi-distributed approach, eNodeBs coordinate among each other for efficient resource allocation based on local knowledge conveyed by the X2 interface. It turns out that despite the lower complexity of the semi-distributed approach and its inherent adaptability, there is only a slight discrepancy of results among both approaches, which makes the distributed approach much more promising, in particular as a procedure of SON (Self Organized Network).

When several radio access technologies (e.g., HSPA, LTE, WiFi, and WiMAX) cover the same region, deciding to which one mobiles connect is known as the Radio Access Technology (RAT) selection problem. To reduce network signaling and processing load, decisions are generally delegated to mobile users. Mobile users aim to selfishly maximize their utility. However, as they do not cooperate, their decisions may lead to performance inefficiency. In this paper, to overcome this limitation, we propose a network-assisted approach. The network provides information for the mobiles to make more accurate decisions. By appropriately tuning network information, user decisions are globally expected to meet operator objectives, avoiding undesirable network states. Deriving network information is formulated as a semi-Markov decision process (SMDP), and optimal policies are computed using the Policy Iteration algorithm. Also, and since network parameters may not be easily obtained, a reinforcement learning approach is introduced to derive what to signal to mobiles. The performances of optimal, learning-based, and heuristic policies, such as blocking probability and average throughput, are analyzed. When tuning thresholds are pertinently set, our heuristic achieves performance very close to the optimal solution. Moreover, although it provides lower performance, our learning-based algorithm has the crucial advantage of requiring no prior parameterization.

In order to achieve high data rates in future wireless packet switched cellular networks, aggressive frequency reuse is inevitable due to the scarcity of the radio resources. While intra-cell interference is mostly mitigated and can be ignored, inter-cell interference can severely degrade performances with bad channel quality. Hence, Inter-Cell Interference Coordination (ICIC) is commonly identified as a key radio resource management mechanism to enhance system performance of 4G networks. This paper addresses the problem of ICIC in the downlink of Long Term Evolution (LTE) systems where the resource selection process is apprehended as a potential game. Proving the existence of Nash Equilibriums (NE) shows that stable resource allocations can be reached by selfish Base Stations (BS). We put forward a fully decentralized algorithm based on replicator dynamics to attain the pure NEs of the modeled game. Each BS will endeavor to select a set of favorable resources with low interference based on local knowledge only making use of signaling messages already present in the downlink of LTE systems.

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

Inter-Cell Interference Coordination (ICIC) is commonly identified as a key radio resource management mechanism to enhance system performance of 4G networks. This paper addresses the problem of ICIC in the downlink of cellular OFDMA systems where the power level selection process of resource blocks (RB) is apprehended as a sub-modular game. The existence of Nash equilibriums (NE) for that type of games shows that stable power allocations can be reached by selfish Base Stations (BS). We put forward a semi distributed algorithm based on best response dynamics to attain the NEs of the modeled game. Based on local knowledge conveyed by the X2 interface in LTE (Long Term Evolution) networks [1], each BS will first select a pool of favorable RBs with low interference. Second, each BS will strive to fix the power level adequately on those selected RBs realizing performances comparable with the Max Power policy that uses full power on selected RBs while achieving substantial power economy. Finally, we compare the obtained results to an optimal global solution to quantify the efficiency loss of the distributed game approach. It turns out that even though the distributed game results are sub-optimal, the low degree of system complexity and the inherent adaptability make the decentralized approach promising especially for dynamic scenarios.

Inter-Cell Interference Coordination (ICIC) is commonly identified as a key radio resource management mechanism to enhance system performance of 4G networks. This paper addresses the problem of ICIC in the downlink of cellular OFDMA systems where the resource selection process is apprehended as a congestion game. Proving the existence of Pure Nash equilibriums (PNE) shows that stable resource allocations can be reached by selfish Base Stations (BS). We resort to a fully decentralized algorithm proposed by Berenbrinck et al [1] to attain the PNEs of the modeled game. Each BS will strive to select a pool of favorable resources with low interference based on local knowledge only.

Nowadays, with the abundance of IEEE 802.11 access points (APs), a mobile user has the flexibility to choose one of several APs, each using a separate channel. Rather than relying on the simplistic standardized algorithm to select the AP, it would be preferable to use optimal algorithms that reduce the user data transfer time. In this paper, the AP selection process is apprehended as an ordinal potential game, which is a class of non-cooperative games known to possess at least one pure Nash Equilibrium (PNE). We put forward a fully decentralized algorithm based on replicator dynamics to attain those PNE. Further, to assess the loss in efficiency of the proposed selfish distributed algorithm, we compare its performances against a centralized optimal approach derived by solving a mixed integer linear program.

Growing energy demands, the increasing depletion of traditional energy resources, together with the recent surge in mobile internet traffic, all call for green solutions to address the challenge of energy-efficient wireless access networks. In this paper, we consider possible power saving by reducing the number of active BSs and adjusting the transmit power of those that remain active while maintaining a satisfying service for all users in the network. We thus introduce a joint optimization problem that minimizes the network power consumption of the network and the sum of network user transmission delays. Our formulation allows us to investigate the tradeoff between power and delay by tuning the respective weighting factors. Moreover, to reduce the computational complexity of the optimal solution of our non-linear optimization problem, we convert it into a Mixed Integer Linear Programming (MILP) problem. We provide extensive simulations for various decision preferences such as power minimization, delay minimization and joint minimization of power and delay. The results we present show that we obtain power savings of up to 16% compared to legacy network models.

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

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

With the abundance of diverse air interfaces in the same operating area, a mobile user is able to connect concurrently to different wireless access networks in order to meet more easily its target performance. In this paper, we consider the downlink of a multi-class hybrid network with two Radio Access Technologies (RAT): WiMAX and WiFi. We devise a distributed Radio Resource Management (RRM) scheme for elastic traffic that coexists with streaming traffic. The proposed scheduling policy is original in the sense that elastic users have a counterintuitive behaviour: they will try to occupy the least amount possible of bandwidth owing to their delay tolerance to accommodate QoS stringent streaming users. A non-cooperative submodular game is used to load balance the traffic of elastic users between the two available RATs aiming at minimizing their bandwidth consumption. We characterize the Nash Equilibriums (NE) of the resource management game and study the efficiency of a distributed algorithm based on best response dynamics to achieve those equilibriums. The game is played upon every new arrival and an admission control scheme is used to limit the number of ongoing connections so that admitted elastic flows are sustained with a guaranteed minimal rate.

Owing to the proliferation of different Radio Access Technologies (RAT) in the same operating area, a mobile user is capable of connecting concomitantly to diverse wireless networks in order to meet more easily its target performance. In this paper, we consider the downlink of a multi-class hybrid network with two RATs: WiMAX and 3G LTE. We put forward a semi-distributed Radio Resource Management (RRM) scheme for elastic traffic where both the system and mobile users intervene in the resource management policy. The proposed scheduling scheme is original in the sense that users with elastic traffic have a counterintuitive behavior: they will try to occupy the least amount possible of bandwidth to accommodate QoS stringent streaming traffic. A non-cooperative game is used to load balance the traffic of elastic users between the two available RATs aiming at minimizing their bandwidth consumption. We characterize the Nash Equilibriums (NE) of the RRM game and study the efficiency of a best response algorithm to achieve those equilibriums. Moreover, we propose a fully decentralized algorithm based on replicator dynamics to attain NEs. The system role is to apply an admission control algorithm that limits the number of ongoing connections so that elastic traffic is sustained with a guaranteed minimal rate.

In wireless heterogeneous networks, one of the most challenging problems is Radio Access Technology (RAT) selection that must be designed to avoid resource wastage. In this paper we adopt a hybrid model for RAT selection where the system allocates the downlink traffic between two different technologies in order to enhance global performance. We study the case of an integrated hybrid Wireless Local Area Network environment where the challenge we face is the high computational complexity necessary to obtain the global optimal solution. Therefore, we propose four distributed heuristic algorithms for RAT selection, where two of them are based on the distance between the user and the access points (APs), namely, distance based and probabilistic distance based algorithms. While the two others schemes are based on the peak rate that each user receives from these APs (peak rate based and probabilistic peak rate based algorithms). Results show that the proposed algorithms give efficient results compared to the optimal one depending on the spatial users distribution. Moreover these algorithms have a low computational complexity which makes them more advantageous compared to the optimal scheme in presence of a large number of users.

Nowadays, with the abundance of diverse air interfaces in the same operating area, advanced Radio Resource Management (RRM) is vital to take advantage of the available system resources. In such a scenario, a mobile user will be able to connect concurrently to different wireless access networks. In this paper, we consider the downlink of a hybrid network with two broadband Radio Access Technologies (RAT): WiMAX and WiFi. Two approaches are proposed to load balance the traffic of every user between the two available RATs: an individual approach where mobile users selfishly strive to improve their performance and a global approach where resource allocation is made in a way to satisfy all mobile users. We devise for the individual approach a fully distributed resource management scheme portrayed as a non-cooperative game. We characterize the Nash equilibriums of the proposed RRM game and put forward a decentralized algorithm based on replicator dynamics to achieve those equilibriums. In the global approach, resources are assigned by the system in order to enhance global performances. For the two approaches, we show that after convergence, each user is connected to a single RAT which avoids costly traffic splitting between available RATs.

Nowadays networks are characterized by the abundance of diverse Radio Access Interfaces (RAI) in the same operating area. The various wireless interfaces can belong to the same Radio Access Technology or not. In such a scenario, a mobile user will be able to select selfishly one of the available radio interfaces in order to enhance its own performance. Therefore, the radio access selection policy is vital and must be designed astutely to avoid resource wastage. In this paper, the RAI selection process is apprehended as a congestion game which is a class of noncooperative games in which users share a common set of limited resources. The cost sustained by a given user depends upon the congestion impact inflected by other users sharing the same resource. Devising distributed resource sharing schemes that optimize user air interface selection depends crucially on the existence of Nash equilibria for the modelling congestion games. In this paper, we model the downlink access for three main broadband technologies (WiMAX, WiFi and 3.5G HSDPA) and study the existence of pure Nash equilibria for various multi-RAI scenarios involving those technologies.

With the ever-increasing proliferation of air interfaces that coexist in the same operating area, advanced Radio Resource Management is crucial to take advantage of the available system resources. In this paper, we consider a downlink multi-class heterogeneous network where cells include two co-localized Radio Access Technologies (RAT): WiMAX [1] and UMTS [2]. We propose a Centralised Access Control algorithm responsible of assigning every arriving user to one of the two RATs, while taking into account the joint spatial distribution of already accepted users, the current load of each RAT, the location of the newly accepted user and its influence on global performance. The routing decision is formulated as a Semi Markov Decision Process (SMDP). We show how to obtain an optimal policy that maximizes a predefined reward function accounting for both the operator and users satisfaction.

In new generation cellular networks, opportunistic schedulers take advantage from the delay-tolerance of data applications to ensure that transmission occurs when radio channel conditions are most favourable. “Proportional Fair” (PF) is a well-known opportunistic scheduler that provides a good compromise between fairness and efficiency when transmitting long flows. Unfortunately, the PF algorithm is not efficient for short transfers, which represent the majority of data flows. Moreover, the lack of coordination between opportunistic scheduling and the congestion control mechanism of TCP induce very poor performance especially for short-lived flows. In this paper, we propose three enhanced scheduling approaches that significantly reduce the transfer time of short flows without a significant degradation of the QoS provided to long flows (which are much less sensitive to response time). The simulation results we present of the proposed mechanisms highlight the large benefits that can be obtained by implementing them.

The convergence of wireless networks is a key solution to deal with the ever-increasing need for bandwidth. In this compound radio environment, users can use concurrently diverse services through multiple RATs. In this paper, we consider a heterogeneous network where cells include two co-localized radio access technologies (RAT): WiMAX and UMTS. The predominance of these systems in nowadays mobile networks underlines the relevance of our choice. We consider the downlink channel for both RATs and two service classes: streaming and elastic traffic. We propose a joint radio resource management (JRRM) algorithm responsible of routing every arriving user to one of the two RATs, while taking into account the load of each RAT, the spatial distribution of already accepted users, the location of the newly admitted user, and its influence on global performance. In a study based on the semi Markov decision process (SMDP) theory, we show how to obtain an optimal policy that maximizes a predefined reward function accounting for both the operator and user satisfaction. In fact, the reward function consists of a financial gain component, an aggregate throughput component and a blocking cost.

In this paper, we propose an analytical model for dimensioning a hierarchical WiMax-WiFi network. Our model consists in replacing a finite number of nodes by an equivalent continuum. The key feature of the proposed model is that it accounts for the effect of interference and for the physical layer and channel characteristics in an easy and straightforward way. On the one hand, our model takes into consideration frequency planning and scheduling aspects; and on the other hand, it provides tractable formulae of the end user mean capacity and coverage probability to dimension properly the network.

In this paper, we propose an analytical fluid model for adhoc wireless networks. Our fluid model consists in replacing a finite number of nodes by an equivalent continuum - characterized by a density of nodes - and disseminated in the network according to some distribution function. The key feature of our model is that it takes into account the effect of interference, the CSMA/CA mechanism and radio propagation aspects in an easy and straightforward way. We will give closed form formulae of the Mean Capacity per node and the Coverage Probability, along with an evaluation of the impact of nodes density, network size and carrier sense range on overall performance.

Until recently, wireless networks had been struggling against fading effects. However, 3G wireless networks have learned to profit from radio channel variations to augment their capacity while serving data traffic. Opportunistic schedulers take advantage from the delay-tolerance of these applications to ensure that transmission occurs when radio channel conditions are most favourable. A well-known opportunistic scheduler that strikes a good balance between fairness and efficiency, in an idealistic environment, is the "proportional fair" (PF) scheduler. Nevertheless, the hypotheses according to which its good performances are obtained are not valid in real environments. In this paper, we propose a modified version of PF that allows for a fair allocation of resources in realistic environments and introduces flexibility in sharing these resources between active users

To this day, traffic in cellular networks is still mainly real-time. However, with the deployment of new technologies, such as HDR (High Date Rate) and HSDPA (High Speed Downlink Packet Access), this situation is bound to evolve rapidly and elastic traffic will significantly increase. These novel technologies implement opportunistic scheduling, taking advantage of the delay-tolerance of elastic traffic, to augment the global capacity of the system. Previous work has shown that "Proportional Fair" (PF) is an opportunistic scheduler that provides a good compromise between fairness and efficiency. Nevertheless, the hypotheses according to which these results are obtained are not always valid in real environments. In this paper, we propose a modified version of PF that not only introduces flexibility in sharing resources between active users but also allows for a fair allocation of resources in realistic environments.

The ongoing evolution towards the generalization of wireless access to multi-service networks stresses on the need for optimizing the control of radio resources and in particular, for designing efficient scheduling approaches. The scheduling mechanisms proposed for wireline links do not carry over to wireless interfaces due to the variability of radio links capacity. Efficient schedulers for the radio channel have to take into account information relative to the channel state when allocating resources to the various connected equipments. For the case of delay tolerant traffic, the scheduler may take advantage of terminals diversity. We propose in this paper a modified version of the weighted fair queueing (WFQ) algorithm, termed OWFQ (opportunistic WFQ), that notably increases the average system performance through opportunistic scheduling while fulfilling users' QoS needs in terms of minimum realized throughput.