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.

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.

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.

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.

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.

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.

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.

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.

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.

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.