This paper extends a framework for load balancing in large Wi-Fi networks we previously proposed. The framework partitions the network into clusters and assigns a sub-controller to each cluster. The sub-controllers employ a Deep Q-Learning-based algorithm to balance the load on the access points in the cluster. The sub-controllers collaborate by exchanging their training updates through a database. However, in highly dynamic Wi-Fi networks, the frequency of these exchanges may cause control plane overhead. In this paper, the training updates are uploaded only when their “quality” meets a condition dependent on a threshold. High threshold values reduce overhead but compromise local learning performance, and vice versa. Therefore, finding the optimal threshold values is formulated as a multi-objective optimization problem. Two metrics are designed to quantify the overhead and instability. These metrics are stochastic, scenario-dependent, and time-consuming to estimate. Randomly generated scenarios of large and dynamic 802.11ax networks are simulated to collect realizations of the metrics. The objectives are obtained by smoothing the realizations with Gaussian kernel regression. The problem is then solved with a genetic algorithm to estimate the Pareto-optimal threshold values.

Wireless local area networks (WLAN) and recent Wi-Fi standards, are used since a few decades for a large variety of network applications. Since a few years, the $5^{\text {th }}$ generation wireless cellular networks (5 G) are being deployed and used, most of the time for similar applications. Both technologies and their evolution, are expected to be deployed within the wireless ecosystem by 2030 and for decades beyond. The Wi-FIP project invokes these two wireless networking solutions and their evolution, to further enhance the wireless data networks regarding sustainable and scalable quality of hybrid network services. This paper presents the visions and objectives of the Wi-FIP project, targeting to enhance synergies and integration of Wi-Fi in 5G/beyond 5G networks, through the convergent B2B/B2C usage model concept, sustainable multi-connectivity, hierarchical Software Defined Networking (SDN) and orchestration. The paper analyzes the functionalities to develop for sustainable, secure and hybrid WLAN/cellular networking.

The increasing complexity of modern WiFi networks aligns them more closely with cellular systems. This convergence underscores the need for WiFi-specific LLMs, akin to ongoing efforts in 5 G. An essential initial step is the design of a WiFi dataset compatible with LLM requirements, structurally coherent, and containing both technical and general information to ensure broad applicability. This work introduces WiFiQnA, a curated dataset of WiFi-related multiple-choice questions designed for LLM fine-tuning. We define two multiple-choice question (MCQ) formats: general knowledge and procedural configuration/troubleshooting questions. We develop a multi-step generation framework using three LLMs for question generation and four for validation. The process integrates filtered telecom datasets, WiFi-specific sources, and tailored prompts, ensuring semantic diversity, accuracy, and reliability.

Developing efficient load-balancing techniques remains a persistent research challenge as modern WiFi networks evolve into increasingly complex environments, incorporating new enhancements in their standards. For instance, DQL-MultiMDP is a load-balancing algorithm that learns an optimal STA-to-AP association policy to ensure user fairness and optimize network performance in dense and dynamic WiFi networks. The algorithm leverages a Multi-Markov Decision Process (MultiMDP) strategy to accommodate the fluctuating number of devices caused by their switching on/off. However, scalability challenges arise due to the exponential expansion of the action space. In this paper, we propose a divide-and-conquer approach that extends the algorithm to operate in extremely large deployments: a dynamic partitioning mechanism divides the network into clusters and assigns a sub-controller to manage the STA-to-AP association in each cluster independently, and a coordination mechanism enables them to exchange their training updates. Experimental investigations validate the effectiveness of the approach and motivate future work.

In this paper, we present our primary version of DQL-MultiMDP, a flexible load balancing approach designed to ensure long-term user satisfaction in modern WiFi networks (WiFi 7 and beyond). The algorithm quasi-simultaneously solves multiple Markov Decision Processes (MDPs) by switching between them depending on the environmental configu-ration, specifically the number of Access Points (APs) and user Stations (STAs). Leveraging Deep Q-Learning (DQL), it utilizes sophisticated state representations with advanced metrics to determine the optimal AP-to-STA association through autonomous decision-making. Experimental results validate the effectiveness of the approach and lay the foundation for further improvements.

Congestion in the Internet of Things (IoT) networks arises when multiple flows share the same network, which can significantly impede the performance of IoT networks. This problem is exacerbated by the limitations of low-power lossy networks (LLNs), resulting in increased latency, high packet losses, reduced goodput, and other capacity-related issues. To ensure a high quality of service (QoS), and network reliability, it is crucial to implement effective congestion control mechanisms in IoT networks. Congestion in a network leads to an increase in packet losses. The loss ratio represents the proportion of lost packets to the total number of transmitted packets and is a critical metric for assessing the network's traffic load and congestion levels. This paper emphasizes the significance of studying IoT application-generated traffic to predict the loss ratio accurately. For instance, reliable data transfer is essential for IoT applications such as health monitoring, which are highly susceptible to performance degradation due to congested traffic and packet loss. This study proposes a novel approach that uses time series data and Deep Learning (DL) models to predict loss ratio in IoT networks. Our approach involves the implementation of a sliding window technique, as well as the validation and comparison of various DL models using data generated by the Cooja/Contiki framework.

Energy Efficiency (EE) is a key performance metric to design future wireless networks. Since Directional Antennas (DAs) focus the transmission energy towards the destination, it has been shown as a cost-effective solution when used in a backhaul network. In this paper we propose a new joint optimization framework of energy consumption and throughput in backhaul Wireless Mesh Networks (WMNs) equipped with DAs. We first formulate the joint optimization problem as a Mixed Integer Linear Problem (MILP) using a weighted objective function of both the consumed energy and the throughput. Then, we propose to use the Ant-Q algorithm, a Reinforcement Learning (RL) based approach, to reduce the solution complexity and enhance its convergence. Considering a discrete power control scheme, we define a new routing scheme based on the Ant-Q heuristic to select jointly the transmission beam and the transmission power. Using ILOG Cplex to find the optimal solution and NS-3 to conduct extensive simulations, we show the effectiveness and the accuracy of the proposed routing algorithm. Moreover, we analyze the optimization tradeoff depending on the beamwidth, the network topology, the gateway position and the optimization weight factor.

5G Wireless Networks are expected to increase substantially data rates and quality of service the users will experience, with a similar or a lower power consumption as todays 4G networks. The Joint Transmission Coordinated MultiPoint (JTCoMP) is a promising scheme to enhance throughput by reducing the interference, especially for cell-edge users. However, some additional energy for hardware circuit and resource information is consumed by this technology. Meanwhile, the performance evaluation of energy efficiency (EE) in dense networks with JTCoMP approach becomes a hard task in terms of time expense to conduct simulations. To evaluate the EE metric in cellular networks with JT-CoMP scheme and to capture the major factors involved in the energy consumption process, representative and accurate models are needed. In this paper, we develop a tractable and efficient model of EE based on spatial fluid modeling when JT-CoMP is applied. Simulations results show that EE is improved with the raise of the number of coordinated BSs in case of a constant backhauling power cost. Similar EE improvement is also observed in case of variable backhauling power cost, while adding a new coordinated BS. Furthermore, the EE is significantly enhanced in femto cellular networks compared to macro cellular ones, making thereby, JT-CoMP scheme more effective in small cells.

Since femtocells are target to reduce the energy consumption in future cellular networks. In this paper, we analyze the joint impact of shadowing and path-loss exponent on the energy efficiency of such networks based on an analytical tractable model of the spatial fluid modeling. We first develop a closed-form expression of SINR threshold of a user equipment located at a given distance from its serving base station through a polynomial curve fitting method, while considering the impact of shadowing and path-loss exponent as well as a fixed coverage probability. Taking advantage of this expression, we then establish a tractable and efficient model based on spatial fluid framework which reduces the analysis complexity. Moreover, the effectiveness and the accuracy of the proposed model are highlighted through a comparison with the results obtained by Monte Carlo simulations. The results point out that the energy efficiency is significantly impacted by the shadow fading, and decreases with the raise of the standard deviation value of the lognormal shadowing.

The advent of the fifth generation of wireless networks forecasts an incredible increase in throughput, number of simultaneous connections and low latency to provide the full set of capabilities. But this unprecedented increase in capacity must not lead to an energy crunch. Therefore, 5G requirements specify it must be achieved at a similar or lower power consumption as todays networks. To evaluate the performance of large representative cellular networks and to capture the major factors involved in the energy consumption process, representative and accurate models are needed. In this paper, we develop a tractable and efficient model based on spatial fluid modeling which reduces the analysis complexity. Moreover, the effectiveness and accuracy of the proposed model are shown through a comparison with the results obtained by Monte Carlo simulations. Afterwards, our simulations results point out that energy efficiency is better in a small cell network compared to a macrocell one and is not related to the density of user equipments.

Directional Antennas (DAs) provide higher gain, and reduce interference by directing beams toward the desired receiver. In this paper, we propose a new joint optimization framework considering the energy consumption and throughput in DAs Wireless Mesh Networks (WMNs). We formulate the joint optimization problem as a Mixed Integer Linear Problem (MILP) using a weighted objective function of both the consumed energy and the throughput. We use ILOG Cplex [1], a software based on branch and cut method, to find the optimal solution of the optimization problem. Results prove the efficiency of using DAs in WMNs in the considered scenarios and show that the consumed energy increases with the beamwidth and decreases with the number of power levels when using power control.

Energy Efficiency is an important feature in poor-covered areas where not only the access to a cellular network is scarce but also energy sources are limited. In this paper, we consider a wireless mesh network to act as a local backhaul network to cover rural and remote villages. The shortage of energy in these settlements motivates to optimize the energy consumption of the wireless backhaul network. Therefore, we propose to use Directional Antennas (DAs) to improve the throughput and the network energy consumption. DAs focus the RF signals toward the desired destination, to reduce the collisions and the number of hops between the source and the destination. We provide for both OAs and DAs networks, the number of links (hops) that a packet passes through to reach the destination. Using extensive simulation, we evaluate the network performance in terms of packet loss, mean throughput, mean energy consumption and energy efficiency for the chain and grid topologies. Simulations results show that using DAs improves the throughput and the energy efficiency, and reduce the mean loss ratio, and the consumed energy.

A 1-tier network composed by multimode low power nodes (LTE/Wifi) is considered as a cost-efficient solution for operators to improve services in poorly or uncovered rural areas. Using an interference coordination technique, network performance can be further improved. Stochastic geometry gives a set of tools to model the location of base stations and user equipments in such wireless networks. Using a spatial model we analyse the network performance in terms of coverage probability and data rate. To realistically model multimode node locations, a new point process model, called the r-l square point process (p.p.), is used in this work. The model of downlink communication including the coordination technique is developed and it allows to evaluate the system performances in term of coverage probability and throughput. Results show that cooperation among nodes improves the network performance.

In this paper, we propose to use the Joint Transmission approach of Coordinated Multipoint (JT-COMP) of cellular networks to reduce the interference in dense Very High Throughput (VHT) wireless LANs. VHT WLANs are based on wider channel bandwidth, efficient modulation techniques and support for spatial streams using MIMO schemes. However, the interference problem persists despite these approaches, and thereby prevents mobile stations from fully reaping the capacity improvement of such networks. In order to optimize the coverage and minimize the cell overlap in dense stadium scenario, AP locations must be planned carefully. To this end, we model positions of nodes using a spatial stochastic model called the r-l square point process. Then, we derive the coverage probability and throughput expressions and investigate the benefit of Joint Transmission coordination technique. Using simulation, we characterize the performance metrics for different sizes of coordinated set and carrier sensing domain of access points. Our results show that JT-CoMP is a promising scheme for dense WLANs.

The femtocell — Wi-Fi integration is a promising approach to solve the problem of inter-tier or intra-tier interference in heterogeneous networks. In this paper, multimode femtocell base stations are deployed which form a Wi-Fi mesh network to communicate between themselves while deploying cellular technology (e.g., LTE) for communications with mobile users, thus ensuring ultimately connectivity between mobile users and their macro base stations to improve the network coverage. We propose a tractable model for coverage/outage to evaluate the benefits of such integration in terms of SINR and received signal strength. Our work is based on point processes in two-dimensional plane that models locations of femtocell — Wi-Fi (also referred to as multimode) nodes. The proposed model is more realistic than the classical Poisson point process, as the distribution of points is more homogeneous and it ensures that the nodes are not too close to each other. The derivation of coverage/outage formula allows us to determine operational parameter ranges for the Wi-Fi network to form a mesh network. In addition, it helps in the design of the femtocell network to ensure a suitable coverage for users in terms of SINR and received signal strength.

Transport layer performance in IEEE 802.11 multihop wireless networks (MHWNs) has been greatly challenged by wireless medium characteristics and multihop nature which induce several types of packet loss including collision, random channel errors and route failures. In this paper, we propose a novel rate control scheme, called Bi-Metric Rate Control (BMRC), which regulates efficiently the source rate in MHWNs. BMRC's design is based on two MAC metrics: the Medium Access Delay used to detect the network contention level, and the Average Transmission Time used to estimate the effective packet sending rate by which the network will not be overloaded. The simulation results show that the adapted mechanism introduces significant performance improvement in terms of fairness, packet loss rate and delay in MHWNs.

Internet predominant transport protocols, such as TCP and TFRC, face performance degradation in Multihop Wireless Networks because of the high loss and link failure rates. Many solutions have been proposed to improve the transport layer operation. These solutions are either based on network state estimation or use information from MAC layer (called MAC metrics) in a cross-layer manner to better comprehend the network state. In this paper, we define the pertinent MAC metrics to reflect the network state, and provide a comparative study among each other, their expected usage and measurement methods at MAC layer. We also investigate the behaviors of MAC metrics through several experiments in order to reveal their effectiveness in reflecting network events such as contention, collision and loss. We show that the Medium Access Delay metric is the most effective to reflect faithfully the MAC states and to detect the network congestion and contention earlier than the other.

Transport layer performance in IEEE 802.11 mul-tihop wireless networks (MHWNs) has been greatly challenged by wireless medium characteristics and multihop nature which are the sources of several types of packet loss including collision, random channel errors and route failures. Rate control transport protocols, the candidates for multimedia streaming applications suffer from high loss rates and end-to-end delay in MHWNs. A common research direction is that the rate control mechanisms at transport layer should be aware of MAC layer contention to keep the network load at a reasonable level. In this paper, we introduce a new MAC metric which reflects the contention and congestion levels more accurately. The metric is then used to improve the rate control mechanism of a rate-based transport protocol in MHWNs. The simulation results show that the adapted mechanism introduces significant performance improvement in MHWNs.

In this paper, we present a performance analysis of a dynamic bandwidth monitoring method with flow control functionality, for intensive data-passing grid applications over wide area networks. The proposed method called flatness based trajectory tracking, adapts the grid users output rates to match expressive data-transfer SLA used to describe theirs guarantees parameters. The control considers time delay transfer by using delay predictor and provides value-added services for grid applications over Internet. This reactive monitoring uses purely control theoretic approaches which stabilize the network and avoid undesirable oscillations for the transmission of such critical flows. Moreover, discrete-time simulation under GridSim simulator illustrates its feasibility and efficiency to provide guarantees for critical grid traffics with timely execution requirements.

In grid computing, most of the SLA management systems focus on static SLAs, where the guarantee terms are defined as constants or bounds. None of them support dynamic SLAs based on analytical expressions. Therefore, to enforce dynamic SLAs and to improve services offered to applications, we propose a new monitoring method to control the resource utilization and to reconfigure them when necessary in order to match analytical expressions used to describe the service parameters. We are interested in the performance characteristics of grid computing at the network side, especially delay transfer, bandwidth and queue's lengths. The proposed method called flatness based trajectory tracking, considers time delay transfer by using delay predictor and provides value-added services for grid applications over Internet. Moreover, discrete-time simulation under GridSim simulator illustrates its feasibility and efficiency to provide guarantees for critical grid traffics with timely execution requirements and to match expressive data transfer SLAs.