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.

With the rapid development of Internet of Things (IoT) technologies, the sub-GHz unlicensed spectrum is increasingly being shared by protocols such as Long Range (LoRa), Sig-fox, and Long-Range Frequency-Hopping Spread Spectrum (LR-FHSS). These protocols must coexist within the same frequency bands, leading to mutual interference. This paper investigates the physical-layer impact of two types of narrowband signals (BPSK and GMSK) on LoRa demodulation. We employ symbol-level Monte Carlo simulations to analyse how the interference-to-noise ratio (INR) affects the symbol error rate (SER) at a given signal-to-noise ratio (SNR) and noise floor, and then compare the results with those for additive white Gaussian noise (AWGN) of equal power. We demonstrate that modelling narrowband interference as additive white Gaussian noise (AWGN) systematically overestimates the SER of Chirp Spread Spectrum (CSS) demodulation. We also clarify the distinct impairment levels induced by AWGN and two types of narrowband interferers, and provide physical insight into the underlying mechanisms. Finally, we fit a two-segment function for the maximum INR that ensures correct demodulation across SNRs, with one segment for low SNR and the other for high SNR.

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.

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.

LoRaWAN has emerged as a promising and suitable technology for massive machine-type communications. The emerging trend of green communication networking will play a significant role in minimizing power consumption for different battery-powered nodes. The Aloha access scheme used in LoRaWAN leads to a high probability of collision, reducing the network throughput on a large scale. Thus, enhancing the energy efficiency of LoRaWAN networks has become paramount. In this work, we propose a novel spreading factor selection and power control algorithm, named EE-LoRa, to improve the energy efficiency of LoRaWAN networks with multiple gateways. We first devise an optimization problem that maximizes the energy efficiency of the network, defined as a benefit-cost ratio between the network throughput and the consumed energy. Then, power control is applied to tune the transmission power at nodes to the lowest possible levels without affecting the reliability of communications. The simulation results show that EE-LoRa significantly improves the energy efficiency of LoRaWAN networks when compared to legacy LoRaWAN and relevant state-of-the-art algorithms.

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.

Recently, LoRaWAN has become the most employed Low Power Wide Area Network (LPWAN) for a wide range of Internet of Thing (IoT) applications. The main power source for IoT nodes are batteries. However, such disposable power sources are short-lived and impact the environment. In addition, battery replacement is costly and dangerous in some applications, where nodes are located in hard-to-reach areas. Battery-less nodes equipped with energy harvesting impose themselves as a promising environmental friendly solution for a sustainable IoT. In this paper, we propose an optimization problem to find the suitable capacitor and packet generation rate which ensures that the battery-less node can effectively perform its tasks using the harvested energy. Then, we devise an algorithm for improving the throughput of LoRaWAN networkswith battery-less nodes. Numerical examples validate the effectiveness of our algorithm where we obtained a tiny capacitor size and packet arrival rate suitable for IoT applications. Furthermore, our proposed approach enhances the network throughput and the packet delivery ratio compared to Legacy LoRaWAN and other state-of-the-art algorithms.

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.

This paper provides a detailed study on NB-IoT link adaptation, for realistic channel estimation, under different objectives. NB-IoT supports link adaptation parameters of multiple types of resource units (RU) and number of repetitions to extend coverage while achieving reliable transmissions. The paper focuses on the impact of these parameters on NB-IoT performance. We provide a mathematical formulation of link adaptation following three different approaches: maximizing the rate, minimizing the energy, and maximizing the energy efficiency. Simulation results shed the light on the effect of each link adaptation parameter (modulation and coding scheme (MCS), number of repetitions and the type of RU) on the NB-IoT transmission in the different approaches and under different coverage extensions.

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.

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.

This paper explores the possibility of having confirmed traffic in LoRaWAN networks under channel-oblivious jamming. Our results show that a LoRaWAN cell can handle up to 500 end-devices with a relatively good message success probability (∼ 0.8) if the network is strongly jammed (60% of the time) by using a maximum of 16 re-transmissions. We have also proved that, using a channel for downlink transmissions operating in the lowest SF is a major weakness in the LoRaWAN specification. Indeed, our results suggest that for a LoRaWAN cell with 600 end-devices the network goodput can be decreased by ∼ 53.06% when ACK transmissions on the second receive window are allowed. This was done by using an open-source network simulator that allows to evaluate many scenarios that can help LoRaWAN operators to better scale their networks in order to be more resilient against jamming attacks before actual deployments.

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.

Heterogeneous Network (HetNet) is among the most important challenges for the upcoming cellular wireless networks. In this paper, we propose a method to optimize the Radio Access Technologies (RATs) selection and resource allocation in multitechnology wireless networks during a time period. We optimized on a realistic topology of Base Stations (BS) with overlaps of the cellular coverage and dynamic users traffic (arrival and departure). The optimization takes into account the requested services, different users' contract, and user satisfaction. Furthermore, in the proposed approach, we add constraints to ban the session drops and handovers for static users. For each instance, we formulate the problem as a linear optimization problem and we optimize it successively. The aim of the optimization is to jointly maximize the overall user satisfaction and the number of users connected. Compared with a legacy approach, numerical results show that our solution outperforms in terms of user satisfaction.

Recently, LoRaWAN has emerged as a promising technology for the Internet of Things (IoT), owing its ability to support low-power and long-range communications. However, real-world deployment and network optimization require accurate path-loss (PL) modeling, so as to estimate network coverage, performance, and profitability. For that reason, in this paper, LoRaWAN radio channel is investigated in the 868 MHz band. Extensive measurement campaigns were carried out in both indoor and outdoor environments at urban and rural locations in Lebanon (Saint Joseph University of Beirut campus, Beirut city, and Bekaa valley). Based on empirical results, PL models are developed for LoRaWAN communications and compared with widely used empirical models. Moreover, the performance and the coverage of LoRaWAN deployment are evaluated based on real measurements. The results show that the proposed PL models are accurate and simple to be applied in Lebanon and other similar locations. Coverage ranges up to 8 and 45 km were obtained in urban and rural areas, respectively. This reveals the reliability of this promising technology for long-range IoT communications.

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.

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.

In Half-Duplex (HD) systems, at a given time instant, a radio resource is exclusively assigned to one User Equipment (UE) either for transmission or for reception. Full-Duplex (FD) networks promise to increase the system's capacity by allocating resources simultaneously to two UEs: one uplink UE and one downlink UE. In order to enhance the network's spectral efficiency, the system needs to deal with two major types of interference: self-interference and co-channel interference. This is one of the main challenges of scheduling in FD systems. In this article, we propose two algorithms for scheduling in FD Orthogonal Frequency Division Multiple Access Systems (FD-OFDMA). First, we propose an FD Maximum Signal-to-Interference-plus-Noise ratio (FD Max-SINR) algorithm, which allocates resources in FD depending on the SINR values of the UEs. Second, we propose a Hybrid Max-SINR scheduling algorithm. This hybrid algorithm chooses astutely between allocating the resources either in HD or FD, in a manner that maximizes the SINR. We evaluate these algorithms and compare them to HD Max-SINR in terms of UE throughput and average waiting delay. According to the simulation results, FD Max-SINR provides, in comparison with its HD counterpart, increased throughput for uplink UEs. It also almost doubles the throughput for the downlink ones. FD Max-SINR reduces the waiting delay that UEs undergo. Furthermore, for relatively low values of Self-Interference Cancellation (SIC), our Hybrid Max-SINR algorithm can still provide higher network throughput compared to HD Max-SINR.

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.

Reliability was, and still is, a major concern for telecoms operators. Network outage can potentially produce high penalty in terms of revenue and user quality of experience. In this paper, we advocate that the data plane reliability can be easily improved when the Software Defined Networking (SDN) concept is incorporated into LTE/EPC networks. As a way to ensure user connectivity be available even in case of network equipment failures, we propose two recovery modes: (i) automatic tunnel re-establishment and (ii) on-demand tunnel re-establishment. Through simulations, we show that those mechanisms outperform the recovery mechanism previously proposed in 3GPP standards.

In this paper, we tackle the problem of access point selection and resource allocation in heterogeneous networks including 3G/4G and Wi-Fi. We study the user satisfaction optimization in terms of throughput and blocking rate. More precisely, the aim is to associate users with the optimal Radio Access Technology (RAT) and to allocate them the optimal number of Resource Units (RUs) based on their requested services and contracts. Starting with a non-linear problem, we re-formulate it into a Mixed Integer Linear Programming (MILP) and assess it on a realistic network configuration. We also compare it with a legacy strategy of selection and allocation. We show that our approach improves the user satisfaction while maintaining a reasonable blocking rate.

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.

Segment Routing is a new architecture that leverages the source routing mechanism to enhance packet forwarding in networks. It is designed to operate over either an MPLS (SR-MPLS) or an IPv6 control plane. SR-MPLS encodes a path as a stack of labels inserted in the packet header by the ingress node. This overhead may violate the Maximum SID Depth (MSD), the equipment hardware limitation which indicates the maximum number of labels an ingress node can push onto the packet header. Currently, the MSD varies from 3 to 5 depending on the equipment manufacturer. Therefore, the MSD value considerably limits the number of paths that can be implemented with SR-MPLS, leading to an inefficient network resource utilization and possibly to congestion. We propose and analyze SR-LEA, an algorithm for an efficient path label encoding that takes advantage of the existing IGP shortest paths in the network. The output of SR-LEA is the minimum label stack to express SR-MPLS paths according to the MSD constraint. Therefore, SR-LEA substantially slackens the impact of MSD and restores the path diversity that MSD forbids in the network.

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.

Inter-cell interference problems arise in dense frequency reuse networks such as Long Term Evolution (LTE). They have harmful impact on system performance, especially for cell-edge users or users having bad radio conditions. Inter-Cell Interference Coordination (ICIC) schemes aim at mitigating the interference produced by nearby cells to enhance the performance of cell-edge users. ICIC techniques include static frequency reuse schemes and cell-coordinated schemes. In this paper, we propose a semi-static frequency allocation algorithm that exploits evolved-NodeBs communications via X2 interface to mitigate inter-cell interference. Each cell is divided into two zones: cell-center and cell-edge. Cell zone satisfaction is tracked, and the unsatisfied zone gets more frequency resource blocks in a distributed manner. The scope of this work is on the downlink of LTE networks using frequency division duplex transmission mode. An LTE downlink system level simulator is chosen to compare the performance of the proposed technique with the frequency reuse-1 model and the fractional frequency reuse technique. Simulation results show that our technique improves throughput cumulative distribution function, achieves a better throughput fairness, and reduces the percentage of unsatisfied users. It is a dynamic technique able to adapt with non-uniform user distributions and traffic demands.

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.

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.

In this paper, we propose a hybrid decision algorithm for the selection of the access in multi-operator networks environment, where competing operators share their radio access networks to meet traffic and data rate demands. The proposed algorithm guarantees the user satisfaction and a global gain for all cooperating operators. Simulation results prove the efficiency of the proposed scheme and show that the cooperation between operators achieves benefits to both users and operators; user acceptance as well as the operator resource utilization and the operator revenue increase.

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.

Achieving accurate, cost-efficient, and fast anomaly localization is a highly desired feature in computer networks. Prior works, examining the problem of single link-level anomaly localization, have claimed that a necessary condition for localizing anomalies unambiguously is to deploy resources that enable the monitoring of a set of paths distinguishing between all links of the network pairwise. In this paper, we show that the number of pair of links that are to be distinguished can be cut down drastically using an already established anomaly detection solution. This results in reducing the localization overhead and cost significantly. Furthermore, we show that all potential anomaly scenarios can be derived offline from the anomaly detection solution. Therefore, we compute full localization solutions, i.e. monitors that are to be activated and paths that are to be monitored, for all potential anomaly scenarios offline. This results in a significant minimization of the localization delay. We devise an anomaly localization technique that selects monitor locations and monitoring paths jointly; thereby enabling a trade-off between the number and locations of monitoring devices and the quality of monitoring paths. The problem is formulated as an integer linear program (ILP), and is shown to be NP-hard through a polynomial-time reduction from the NP-hard facility location problem. The effectiveness and the correctness of the proposed anomaly localization scheme are verified through theoretical analysis and extensive simulations.

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.

To reduce monitoring cost, the number of monitors to be deployed have to be minimized and the overhead of monitoring flows on the underlying network have to be reduced. In a recent work, we demonstrated, using ILP formulations, that there is a trade-off between theses two minimization objectives. However, we have shown that the trade-off could be efficiently balanced by jointly optimizing monitor location and anomaly detection costs. The problem is NP-complete, hence ILPs could not deliver solutions for large networks. In this paper, we address the scalability issues. We propose two greedy algorithms that jointly optimize monitor location cost and anomaly detection cost. The first algorithm is based on an exhaustive heuristic that explores all paths that are candidate to be monitored, in order to select a subset of paths that reduces the total monitoring cost. On the opposite, the second algorithm is based on a selective heuristic that avoids exploring all the candidate paths to further improve the scalability. The main challenge of this heuristic is to not degrade the solution quality. The two algorithms have been evaluated through extensive simulations on networks of hundred of billions of paths. The comparison of the solutions delivered by the two algorithms to each other and to the solutions delivered by the ILP demonstrates that the selective algorithm provides near-optimal solutions, while achieving a desirable scalability with respect to the network size and significant reduction of the computation time.

In this paper, we consider link-and-node failure recovery in dynamic multicast traffic in WDM networks. We extend the node protection concept of the p-cycle approach to achieve more efficient resource utilization. Then, we propose a novel algorithm that integrates our concept for the node protection, named node-and link protecting p-cycle based algorithm (NPC). We also propose a second algorithm, named node-and-link protecting candidate p-cycle based algorithm (NPCC). This algorithm deploys our concept for node protection and relies on a candidate p-cycle set to speed up the computational time. We compare our proposed algorithms to the ESHN algorithm, which is reported to be the most efficient algorithm for protecting dynamic multicast sessions. Extensive simulations show that the NPC algorithm achieves the lowest blocking probability, but has the highest computational time among the NPCC and ESHN algorithms. The NPCC algorithm outperforms the ESHN algorithm in terms of resource utilization efficiency and computational time.

Mutli-domain monitoring aims at guaranteeing QoS for services crossing several domains. It is often desirable to perform global monitoring to guarantee end-to-end QoS for services across domains and to reduce the monitoring cost. However, global monitoring might be infeasible due to confidentiality constraints. The alternative solution is to perform per-domain monitoring. In this work, we propose to evaluate global and per-domain monitoring techniques. For this end, we study the properties of multi-domain networks and the requirements of multi-domain monitoring. We formulate the problem as an Integer Linear Program (ILP). We show that it is a Nondeterministic Polynomial Time Hard (NP-Hard) problem, and therefore, we devise a heuristic that meets multi-domain properties. We show that confidentiality is far from being the only constraint to global multi-domain monitoring. In our evaluation, the confidentiality constraint has been relaxed, in order to investigate other performance metrics; namely, the monitoring cost, the quality of monitored paths, the anomaly detection delays, and the fairness of monitoring load distribution among domains. Simulation results on random topologies show that per-domain monitoring outperforms global monitoring for all these metrics, except the monitoring cost that is slightly lower for global monitoring.

The NP-hard QoS Multicast Routing (QoSMR) is one of the most challenging problems in recent networks. In this paper, we investigate the QoSMR problem as well as its different existing formulations. We analyze the limitations of these formulations and propose a novel one. The main advantage of our formulation is that it considers not only the quality of the multicast subgraph formed by the computed paths, but also takes into account the end-to-end quality of each of these paths. Moreover, we study the state of the art proposal for solving the QoSMR problem and show that the well-known MAMCRA algorithm can be very expensive in computation time. Therefore, we propose two efficient heuristics based on the computation of shortest paths. Extensive simulations are performed, and obtained results prove that these fast heuristics have bounded computation time and find satisfying solutions for the QoS requirements.

Achieving cost-effective systems for network performance monitoring has been the subject of many research works over the last few years. Most of them adopt a two-step approach. The first step assigns optimal locations to monitors, whereas the second step selects a minimal set of paths to be monitored. However, such an approach does not consider the trade-off between the optimization objectives of each step, and hence may lead to sub-optimal usage of network resources and biased measurements. In this paper, we propose to evaluate and reduce this trade-off. Toward this end, we come up with two ILP formulations for a novel monitoring cost model that apply for both passive and active monitoring. The aim is to jointly minimize the monitor location cost and the anomaly detection cost, thereby obtaining a monitoring solution that minimizes the total monitoring cost. Simulation results illustrate the interplay between the optimization objectives and evaluate the quality of the obtained monitoring solution.

Survivability becomes an important issue in optical networks due to the huge bandwidth offered by optical technology. Many works have studied network survivability. The majority of these works are typically destined to single-domain. In this work we address the survivability in multidomain optical networks. This paper provides a classification of the existing protection solutions proposed for multi-domain networks and analyses their advantages and limitations. We propose a new solution for multi-domain optical networks based on p-cycles. For scalability and security reasons, we also propose a topology aggregation model adapted to the p-cycles computation. This aggregation model allows our proposed solution to find a trade-off between two competing goals: efficient use of backup resources and small running time. Simulation results show that the proposed solution is a good trade-off between resource utilization and running time compared to the existing solutions.

The major challenge of p-cycle network design resides in finding an optimal set of p-cycles protecting the network for a given working capacity. Existing solutions (exact and heuristic approaches), for solving the problem, find the set of p-cycles protecting the network through two steps: one step for generating candidate p-cycles and a second step for selecting the efficient ones. In this paper, we present a novel heuristic approach, which computes an efficient set of p-cycles protecting the network in one step. Our heuristic approach takes into consideration two different criteria: the redundancy and the number of p-cycles involved in the solution. Simulation study shows that our approach necessitates a lower redundancy and fewer p-cycles to protect the network compared to state-of-the-art approaches.

In this work, we study the flow allocation problem in Next Generation Internet (NGI) networks. Given a capacitated network, flow allocation consists of a simultaneous routing and bandwidth allocation problem for flows realizing a set of source-destination demands. We consider the case where demands are elastic and the flow realizing one demand can be arbitrary split over multiple paths. Moreover, we ensure that the allocated flows are survivable to network failures using path protection. We investigate the possibility of sharing backup capacity, thus enabling more efficient resource consumption. Depending on the optimization objective, we provide two different approaches to the problem. The first approach maximizes the total allocated volume and the second one maximizes the minimum allocated volume. We build mathematical formulations based on generalizations of the maximum multicommodity flow and the maximum concurrent flow problems. Moreover, we provide new approximation algorithms based on a primal-dual approach. These algorithms compute a solution that is within a guaranteed factor of the optimal. Numerical results enable to get deeper insights on the quality and compared performance of the different approaches.

No abstract available.

In this paper, we present a novel polynomial time framework for the off-line flow allocation problem in MPLS-traffic engineering (MPLS-TE) networks. This framework, named FATE (flow allocation for traffic engineering), encom-passes different objectives that illustrate the operator needs for traffic engineering and provides advanced control over the quality of the solution. Typically, we consider minimizing congestion, bandwidth consumption and operational costs. FATE is based on linear programming and presents an efficient solution for the problem both in terms of computational complexity and approximation guarantees. Simulation results show that we can get an efficient trade-off between congestion and bandwidth consumption while having a reduced number of label switched paths (LSPs) for various known topologies