Unmanned Aerial Vehicles (UAVs) increasingly enhance the Quality of Service (QoS) in wireless networks due to their flexibility and cost-effectiveness. However, optimizing UAV placement in dynamic, obstacle-prone environments remains a significant research challenge due to their complexity. Reinforcement Learning (RL) offers adaptability and robustness in such environments, proving effective for UAV positioning optimization.This paper introduces RLpos-3, a novel framework that integrates standard RL techniques and simulation libraries with Network Simulator 3 (ns-3) to facilitate the development and evaluation of UAV positioning algorithms. RLpos-3 serves as a supplementary tool for researchers, enabling the implementation, analysis, and benchmarking of UAV positioning strategies across diverse environmental conditions while meeting user traffic demands. To validate its effectiveness, we present use cases demonstrating RLpos-3’s performance in optimizing UAV placement under realistic conditions, such as urban and obstacle-rich environments.

The increasing complexity of wireless environments, driven by user mobility and dynamic obstructions, poses significant challenges to maintaining Line-of-Sight (LoS) connectivity. Mobile base stations (gNBs) offer a promising solution by physically relocating to restore or sustain LoS. This paper explores how reinforcement learning (RL) can be applied to gNB mobility control within vision-aided network systems. As part of the CONVERGE project, we present the CONVERGE Chamber Simulator (CC-SIM), a 3D environment for developing, training, and testing gNB mobility control algorithms. CC-SIM models user and obstacle mobility, visual occlusion, and Radio Frequency (RF) propagation while supporting both offline reinforcement learning and real-time validation through integration with OpenAirInterface (OAI). Leveraging CC-SIM, we trained a Deep Q-Network (DQN) agent that proactively repositions gNBs under dynamic conditions. Across three representative use cases, the agent reduced LoS blockage by up to 42% compared to static deployments, highlighting the potential of RL-driven mobility control and positioning CC-SIM as a practical platform for advancing adaptive, next-generation wireless networks.

The Integrated Access and Backhaul (IAB) 5G network architecture, introduced in 3GPP Release 16, leverages a shared 5G spectrum for both access and backhaul networks. Due to the novelty of IAB, there is a lack of suitable implementations and performance evaluations. This paper addresses this gap by proposing EMU-IAB, a mobility emulator for private standalone 5G IAB networks. The proposed emulation environment comprises a 5G Core Network, an IAB-enabled Radio Access Network (RAN), leveraging the Open-RAN (O-RAN) architecture. The RAN includes a fixed IAB Donor, a mobile IAB Node, and multiple User Equipments (UEs). The mobility of the IAB Node is managed through EMU-IAB, which allows defining the path loss of emulated wireless channels. The validation of EMU-IAB was conducted under a realistic IAB node mobility scenario, addressing different traffic demand from the UEs.

Unmanned Aerial Vehicles (UAVs) have emerged as adequate platforms to carry communications nodes, including Wi-Fi Access Points and cellular Base Stations. This has led to the concept of flying networks composed of UAVs as a flexible and agile solution to provide on-demand wireless connectivity anytime, anywhere. However, state of the art works have been focused on optimizing the placement of the access network providing connectivity to ground users, overlooking the backhaul network design. In order to improve the overall Quality of Service (QoS) offered to ground users, the placement of Flying Gateways (FGWs) and the size of the queues configured in the UAVs need to be carefully defined to meet strict performance requirements. The main contribution of this article is a traffic-aware gateway placement and queue management (GPQM) algorithm for flying networks. GPQM takes advantage of knowing in advance the positions of the UAVs and their traffic demand to determine the FGW position and the queue size of the UAVs, in order to maximize the aggregate throughput and provide stochastic delay guarantees. GPQM is evaluated by means of ns-3 simulations, considering a realistic wireless channel model. The results demonstrate significant gains in the QoS offered when GPQM is used.

Unmanned Aerial Vehicles (UAVs) acting as Flying Access Points (FAPs) are being used to provide on-demand wireless connectivity in extreme scenarios. Despite ongoing research, the optimization of UAVs' positions according to dynamic users' traffic demands remains challenging. We propose the Traffic-aware UAV Placement Algorithm (TUPA), which positions a UAV acting as FAP according to the users' traffic demands, in order to maximize the network utility. Using a DRL approach enables the FAP to autonomously learn and adapt to dynamic conditions and requirements of networking scenarios. Moreover, the proposed DRL methodology allows TUPA to generalize knowledge acquired during training to unknown combinations of users' positions and traffic demands, with no additional training. TUPA is trained and evaluated using network simulator ns-3 and ns3-gym framework. The results demonstrate that TUPA increases the network utility, compared to baseline solutions, increasing the average network utility up to 4x in scenarios with heterogeneous traffic demands.

5G has become increasingly popular nowadays, mainly due to its characteristics which enable high data rates and low latency. At the same time, mobile robotic platforms, such as drones and robots, appeared as suitable platforms to carry radio stations, enabling the on-demand placement of 5G communications cells. The main contribution of this paper is an obstacle-aware on-demand 5G network. The proposed solution consists of a 5G radio station (gNB) carried by a mobile robotic platform capable of providing obstacle-aware wireless connectivity to 5G User Equipments (UEs), leveraged by a novel virtual network function - On-Demand Mobility Management Function (ODMMF). ODMMF is designed to integrate the 5G Core network and it allows to monitor the radio conditions provided to the served UEs, while enabling the positioning of the mobile robotic platform remotely by taking advantage of the visual information provided by on-board video cameras. The proposed solution was validated using an experimental prototype, under a representative networking scenario.

The ability to operate virtually anywhere and carry payload makes Unmanned Aerial Vehicles (UAVs) perfect platforms to carry communications nodes, including Wi-Fi Access Points (APs) and cellular Base Stations (BSs). This is paving the way to the deployment of flying networks that enable communications to ground users on demand. Still, flying networks impose significant challenges in order to meet the Quality of Experience expectations. State of the art works addressed these challenges, but have been focused on routing and the placement of the UAVs as APs and BSs serving the ground users, overlooking the backhaul network design. The main contribution of this paper is a centralized traffic-aware Gateway UAV Placement (GWP) algorithm for flying networks with controlled topology. GWP takes advantage of the knowledge of the offered traffic and the future topologies of the flying network to enable backhaul communications paths with high enough capacity. The performance achieved using the GWP algorithm is evaluated using ns-3 simulations. The obtained results demonstrate significant gains regarding aggregate throughput and delay.

The ability to operate anywhere, anytime, as well as their capability to hover and carry cargo on board make Unmanned Aerial Vehicles (UAVs) suitable platforms to act as Flying Gateways (FGWs) to the Internet. The problem is the optimal placement of the FGWs within the flying network, such that the Quality of Service (QoS) offered is maximized. The literature has been focused on optimizing the placement of the Flying Access Points (FAPs), which establish high-capacity small cells to serve the users on the ground, overlooking the backhaul network design, including the FGW placement. The FGW placement problem is exacerbated in highly dynamic flying networks, where the dynamic traffic demand and the movements of the users may induce frequent changes in the placement of the FAPs.The main contribution of this paper is a fast gateway placement (F-GWP) algorithm for flying networks that determines the optimal position of a FGW. With F-GWP, backhaul communications paths with high enough capacity are established between the FAPs and the FGW, in order to accommodate the traffic demand of the users on the ground. Simulation and experimental results show F-GWP is two orders of magnitude faster than its state of the art counterpart, while ensuring the same flying network performance.

Besides the large amount of traffic that radio access and backhaul networks need to accommodate, the interest in low-latency communications is emerging. The goal is to reliably transmit high bitrates through the network under controlled delays, thus enabling human and machine-oriented communications. A critical aspect that must be addressed is the latency introduced by network queues. The literature has been focused on studying the queue size in wired networks, but wireless networks bring up additional challenges due to their dynamic characteristics. The problem is exacerbated in high dynamic networks, such as Flying Networks (FNs) composed of Unmanned Aerial Vehicles (UAVs), which have emerged to provide communications anywhere, anytime. The main contribution of this paper is a Proactive Queue Management (PQM) solution for FNs with controlled topology. PQM takes advantage of the knowledge of the future FN topologies and the offered traffic to define in advance the queue size of the communications nodes over time, in order to maximize the throughput with stochastic delay guarantees. The FN performance achieved using PQM is evaluated by means of ns-3 simulations, showing gains regarding aggregate throughput and average delay.

The use of Autonomous Underwater Vehicles (AUVs) is increasingly seen as a cost-effective way to carry out underwater missions. Due to their long endurance and set of sensors onboard, AUVs may collect large amounts of data, in the order of Gbytes, which need to be transferred to shore. State of the art wireless technologies suffer either from low bitrates or limited range. Since surfacing may be unpractical, especially for deep sea operations, long-range underwater data transfer is limited to the use of low bitrate acoustic communications, precluding the timely transmission of large amounts of data. The use of data mules combined with short-range, high bitrate RF or optical communications has been proposed as a solution to overcome the problem.In this paper we describe the implementation and validation of UDMSim, a simulation platform for underwater data muling oriented systems that combines an AUV simulator and the Network Simulator 3 (ns-3). The results presented in this paper show a good match between UDMSim, a theoretical model, and the experimental results obtained by using an underwater testbed when no localization errors exist. When these errors are present, the simulator is able to reproduce the navigation of AUVs that act as data mules, adjust the throughput, and simulate the signal and connection losses that the theoretical model can not predict, but that will occur in reality. UDMSim is made available to the community to support easy and faster evaluation of data muling oriented underwater communications solutions, and enable offline replication of real world experiments.

Electric Vehicles (EVs) sales are increasing in the recent years due to several factors such as cost reduction, fuel cost increase, pollution reductions, government incentives, among others. At the same time, Intelligent Transportation Systems (ITS) are continuously improving, and researchers use different simulators in order to test their proposals before implementing them in real devices. However, traditional communications-aimed simulations do not include fuel consumption issues that are a key factor in transportation systems. This paper presents the addition of Electric Vehicles consumption to the ns-3 simulator, which currently is one of the most used network simulators. Our proposal follows all the models, coding style, as well as engineering guidelines of ns-3, coupled with the characteristics of each vehicle, to accurately estimate the energy consumption. We also analyze the performance of our proposal while simulating a part of the E313 highway, located in Antwerp, Belgium. In particular, we compare the ns-3 results obtained in terms of energy consumption to those obtained in SUMO. In addition, we study the impact of our proposal on the overall simulation time.

The growing demand for broadband communications anytime, anywhere has paved the way to the usage of Unmanned Aerial Vehicles (UAVs) for providing Internet access in areas without network infrastructure and enhancing the performance of existing networks. However, the usage of Flying Multi-hop Networks (FMNs) in such scenarios brings up significant challenges concerning network routing, in order to permanently provide the Quality of Service expected by the users. The problem is exacerbated in crowded events, where the FMN may be formed by many UAVs to address the traffic demand, causing inter-flow interference within the FMN. Typically, estimating inter-flow interference is not straightforward and requires the exchange of probe packets, thus increasing network overhead.The main contribution of this paper is an inter-flow interference-aware routing metric, named I2R, designed for centralized routing in FMNs with controllable topology. I2R does not require any control packets and enables the configuration of paths with minimal Euclidean distance formed by UAVs with the lowest number of neighbors in carrier-sense range, thus minimizing inter-flow interference in the FMN. Simulation results show the I2R superior performance, with significant gains in terms of throughput and end-to-end delay, when compared with state of the art routing metrics.

Unmanned Aerial Vehicles (UAVs) acting as aerial Wi-Fi Access Points or cellular Base Stations are being considered to deploy on-demand network capacity in order to serve traffic demand surges or replace Base Stations. The ability to estimate the Quality of Service (QoS) for a given network setup may help in solving UAV placement problems. This paper proposes a Machine Learning (ML) based QoS estimator, based on convolutional neural networks, which estimates the QoS for a given network by considering the UAV positions, the user positions and their offered traffic. The ML-based QoS estimator represents a novel paradigm for estimating the QoS in aerial wireless networks. It provides fast and accurate estimations with reduced computational complexity. We demonstrate the usefulness and applicability of the proposed QoS estimator using the ideal UAV placement algorithm. Simulation results show the QoS estimator has an average prediction error lower than 5%.

The growth of Internet of Things (IoT) technologies has triggered the development of low-cost solutions characterised by low energy consumption and low complexity. To interconnect these devices, some wireless communications technologies including IEEE 802.11 and IEEE 802.15.4 have been used due to their deployment and management simplicity and high scalability. However, in scenarios where the devices are physically distant or there is a massive number of devices in a reduced area, cellular technologies such as 3rd Generation Partnership Project (3GPP) Narrowband-Internet of Things (NB-IoT) are seen as the solution.This paper proposes a network planning theoretical model for NB-IoT, named NB-IoT Deterministic Link Adaptation Model (NB-DLAM), which can be used to estimate Quality of Service (QoS) metrics such as Packet Delivery Ratio (PDR), transmission time, and throughput. NB-DLAM estimations were compared with simulation results, which show the accuracy of the proposed model.

To properly validate wireless networking solutions we depend on experimentation. Simulation very often produces less accurate results due to the use of models that are simplifications of the real phenomena they try to model. Networking experimentation may offer limited repeatability and reproducibility. Being influenced by external random phenomena such as noise, interference, and multipath, real experiments are hardly repeatable. In addition, they are difficult to reproduce due to testbed operational constraints and availability. Without repeatability and reproducibility, the validation of the networking solution under evaluation is questionable. In this paper, we show how the Trace-based Simulation (TS) approach can be used to accurately repeat and reproduce real experiments and, consequently, introduce a paradigm shift when it comes to the evaluation of wireless networking solutions. We present an extensive evaluation of the TS approach using the Fed4FIRE+ w-iLab.2 testbed. The results show that it is possible to repeat and reproduce real experiments using Network Simulator 3 (ns-3) trace-based simulations with more accuracy than in pure simulation, with average accuracy gains above 50%.

Autonomous Underwater Vehicles (AUVs) are widely used as a cost-effective mean to carry out underwater missions. During long-term missions, AUVs may collect large amounts of data that usually needs to be sent to shore. An AUV may have to travel several kilometers before reaching an area of interest near the seafloor, thus surfacing is unpractical for most cases. Long-range underwater communications rely mostly on acoustic communications, which are characterized by very low bitrates, thus making the transfer of large amounts of data too slow. GROW is a novel solution for long-range, high bitrate underwater wireless communications between a survey unit (e.g., deep sea lander, AUV) and a central station at surface. GROW combines AUVs as data mules, short-range high bitrate wireless RF or optical communications, and long-range low bitrate acoustic communications for control.In this paper we present the Underwater Data Muling Protocol (UDMP), a communications protocol that enables the control and the scheduling of the Data Mule Units within the GROW framework. Experimental results obtained using an underwater testbed show that the use of UDMP and data mules can outperform acoustic communications, achieving equivalent throughput up to 150 times higher within the typical range of operation of the latter.

Despite recent advances, always-on broadband Internet connectivity is still not available in Temporary Crowded Events (TCEs). To solve this problem, this paper envisions a novel concept named Traffic-Aware Multi-Tier Flying Network (TMFN). A TMFN consists of a mobile and physically reconfigurable network of Flying Mesh Access Points (FMAPs) and Gateways, which is able to dynamically reconfigure its topology according to the users' traffic demands - characterized by the users' positions and offered traffic. To implement this concept, a novel traffic-aware Network Planning (NetPlan) algorithm is proposed, which dynamically determines the FMAPs' coordinates and Wi-Fi cell ranges according to the users' traffic demands, in order to improve the TMFN's aggregate throughput, without compromising the overall coverage. Simulation results obtained in scenarios typically observed in TCEs demonstrate improved Quality of Service metrics, specifically the mean throughput, thus validating the proposed NetPlan algorithm.

The advent of small and low-cost Unmanned Aerial Vehicles (UAVs) is paving the way to use swarms of UAVs to perform missions such as aerial video monitoring and infrastructure inspection. Within a swarm, UAVs communicate by means of a Flying Multi-hop Network (FMN), which due to its dynamics induces frequent changes of network topology and quality of the links. Recently, UAVs have also been used to provide Internet access and enhance the capacity of existing networks in Temporary Events. This brings up additional routing challenges not yet addressed, in order to provide always-on and high capacity paths able to meet the Quality of Service expected by the users.This paper presents RedeFINE, a centralized routing solution for FMNs that selects high-capacity paths between UAVs and avoids communications disruptions, by defining in advance the forwarding tables and the instants they shall be updated in the UAVs; this represents a major step forward with respect to traditional routing protocols. The performance evaluation of RedeFINE shows promising results, especially regarding Throughput and Packet Delivery Ratio, when compared with state of the art routing solutions.

IEEE 802.11-based wireless multimedia sensor networks (WMSN) are a cost-effective and flexible solution for video monitoring scenarios. Yet, they suffer from three major problems: bad performance, throughput unfairness, and energy inefficiency. Several approaches have been considered to tackle these problems but they are too restrictive or complex. In this paper we propose a scheduling approach using FM as a control channel to address the energy inefficiency problem. By taking advantage of the FM radio characteristics - higher coverage and lower energy consumption than Wi-Fi, our proposed approach uses FM as an always-on point-to-multipoint control channel used to turn off the nodes' Wi-Fi radio interfaces when they are not needed to transmit, receive, or relay data. Numerical and simulation analysis shows that our proposed scheduling mechanism significantly reduces energy consumption, while preserving performance and fairness characteristics.

Typical sensor networks are formed by low-end, battery operated devices, which rely on low-energy communication technologies, such as Bluetooth, Zigbee and ANT+, due to their energy efficiency. On the other hand, sensor networks increasingly need to be connected to the Internet, which implies adaptations of the TCP/IP stack to fit such wireless technologies. These adaptations bring additional complexity and imply new hardware, thus deployments are cumbersome and sub-optimal. Conversely, Wi-Fi is ubiquitous, can be seamlessly integrated with TCP/IP, and is energy-efficient with the right configurations; yet, its usage is still uncommon in e-health scenarios. For these reasons, we argue that a TCP/IP over Wi-Fi approach should be followed in e-health sensor networks. We propose a novel cross-layer, context-aware network configuration mechanism, which monitors the user and networking contexts and optimizes the configuration of the TCP/IP protocol stack accordingly. Our approach enables seamless integration between e-health wireless sensor networks and the TCP/IP backbone, while improving energy efficiency and reliability.

Techniques using RSS fingerprinting for localization have been studied over a number of different technologies in many different scenarios. In the case of underground tunnels localization can be quite challenging, yet it is extremely important for safety reasons. In the specific case of the CERN tunnels, accurate and automatized localization methods would additionally allow the workflow of some activities to become substantially faster. In a radiation area this would also have the added benefit of reducing the exposure time of personnel conducting so called radiation surveys which have to be carried out before access can be granted. In this paper Fingerprinting techniques for GSM and Wireless LAN are studied and enhanced to take advantage of both network technologies simultaneously as well as the channels RSS differential and an observed effect in the radiated power in the leaky-feeder cables. Besides the higher accuracy achieved for a single technology, this methodology looks promising for scenarios where several types of wireless networks are available or expected to be installed at a later stage.

This paper quantifies the impact of topological characteristics on the performance of single radio multichannel IEEE 802.11 mesh networks. Topological characteristics are the number of nodes per subnetwork, the hop count, the neighbor node density, the hidden nodes, the number of nodes in the neighborhood of the gateway, and the hidden nodes in the neighborhood of the gateway. Network performance metrics are throughput, fairness and delay. The data mining Support Vector Machine (SVM) model was used to extract the relationships between the network topology metrics and the network performance metrics based on data results obtained through ns-2 simulation of random networks. The results obtained can be used as a basis to design channel assignment algorithms or to aid the deployment and management of single radio wireless mesh networks.

Accurate localization techniques have long been of major importance for safety systems and a lot of research has been conducted in the distributed computing field regarding its functionality and reliability. In the specific scenario of long yet narrow tunnels existing at CERN, localization methods will enable a number of applications and processes to substantially reduce human intervention. In this paper we evaluate the use of Fingerprinting techniques with GSM signal available throughout the LHC tunnel via a radiating cable and compare some methods to estimate the location. The existing GSM infrastructure and tunnel conditions seem to be favorable to the adoption of these Fingerprinting methods. Nevertheless significant variations in the signal have been observed which might be traced back to different operational states of accelerator equipment. These effects and their sources will be analyzed in more detail in order to improve the applied techniques and their accuracy under such challenging conditions.

The growth of available broadband access technologies has brought an enormous challenge for operators wanting to ensure seamless mobility to its customers in heterogeneous environments. In this paper we present an enhanced Mobility Management entity focused on a real heterogeneous environments and based on IEEE802.21 standard. This entity is capable to perform terminal and handover management regardless the access technology used. This entity was deployed in a real heterogeneous environment with Wi-Fi, WiMAX and HSPA using an Android smartphone. In order to provide Layer 3 mobility support it was integrated with a modified version of MIPv6. The results show not only the impact of a centralized entity to manage vertical handovers, but also the performance of IEEE802.21 protocol in a real scenario with a real implementation.

Explicit congestion control (XCC) is emerging as one potential solution for overcoming limitations inherent to the current TCP algorithm, characterized by unstable throughput, high queuing delay, RTT-limited fairness, and a static dynamic range that does not scale well to high bandwidth delay product networks. In XCC, routers provide multibit feedback to sources, which, in turn, adapt throughput more accurately to the path bandwidth with potentially faster convergence times. Such systems, however, require precise knowledge of link capacity for efficient operation. In the presence of variable-capacity media, e.g., 802.11, such information is not entirely obvious or may be difficult to extract. We explore three possible algorithms for XCC which retain efficiency under such conditions by inferring available bandwidth from queue dynamics and test them through simulations with two relevant XCC protocols: XCP and RCP. Additionally, preliminary results from an experimental implementation based on XCP are presented. Finally, we compare our proposals with TCP and show how such algorithms outperform it in terms of efficiency, stability, queuing delay, and flow-rate fairness.

Abstract Ubiquitous Internet access is becoming a major requirement for end‐users due to the increasing number of services and applications supported over the Internet. Extending the coverage of current Wi‐Fi infrastructures installed in companies, universities and cities, has been considered a solution to help in fulfilling this requirement, namely when it comes to wireless and nomadic Internet access. This paper describes and analyses a new and simple solution, called Wi‐Fi network Infrastructure eXtension (WiFIX), aimed at extending current Wi‐Fi infrastructures. WiFIX is based on standard IEEE 802.1D bridges and a single‐message protocol that is able to self‐organize the network, and it only requires software changes in IEEE 802.11 access points (APs); no changes to IEEE 802.11 stations are needed. Overhead analysis and experimental results show both the higher efficiency of the solution compared to the IEEE 802.11s draft standard and its good performance as far as data throughput, delay and packet loss are concerned. Copyright © 2010 John Wiley & Sons, Ltd.

Wireless Mesh Networks (WMNs), which feature infrastructureless broadband network configurations, are attracting attention as an elemental technology when it comes to the extension of current WLAN infrastructures. State of the art solutions addressing WMNs usually assume the use of omnidirectional antennas. In this paper we evaluate the performance improvements obtained when using directional antennas. By using simulations, we analyze the gains in terms of throughput, delay and fairness, considering a grid network topology used to extend an infrastructure network. Simulation results show that by changing the type of antenna in use from omnidirectional to directional, the average throughput of a WMN can increase about 56% and the average network delay can be reduced by approximately 40%, without compromising fairness.

The changes in the communication paradigm envisioned for future networks, with peer-to-peer/symmetric attachments gaining momentum and two IP (Internet protocol) versions coexisting, will pose new challenges to mobile communication networks. Traditional IP auto-configuration mechanisms will not work properly, since they were designed mostly having in mind a client-server/asymmetric attachment model, they assume a single IP version paradigm, and they target the auto-configuration of devices only. The 1ST ambient networks project has introduced a new concept - the ambient network - that enables handling every communication entity, either a single device or an entire network, as an ambient network (AN). This paper describes a new efficient mechanism, named basic connectivity (BC) mechanism, for auto-configuring IP connectivity between attaching ANs. A proof-of-concept prototype, experimental results, and theoretical analysis show that BC suites the future networking paradigm and represents a solution more efficient than the current trial-and-error mechanism for auto-configuring IP connectivity.

In the Next Generation Networks (NGNs) users will carry multiple devices forming cooperative networks known as Personal Area Networks (PANs). Some existing technologies enable this type of networks, such as Bluetooth or IEEE 802.15.4, but a unified framework capable of self-organizing them dynamically in a full heterogeneous environment populated by these and other technologies still has to be defined. Also, these networks are envisioned to be connecting dynamically to the Internet, and may use two IP versions and their autoconfiguration mechanisms.In this paper we propose a new framework, the Autoconfiguration and Self-management of Personal Area Networks (ASPAN), which enables the automatic and dynamic deployment of PANs in the heterogeneous environments envisioned for NGNs and handles the automatic and dynamic connection of a PAN to the global Internet.

Real-time multimedia applications, such as VoIP, video conferencing or on-line gaming are key applications to the success of 4G. In today's Internet these applications are not subject to congestion control, therefore the growth of popularity of these applications may endanger the stability of the Internet. On the other hand, 4G networks will be much more dynamic due to mobility and multi-homing, and the network conditions may vary abruptly requiring a fast response from the control mechanism. In this article we compare the feedback-based to the reservation-based congestion control approach and focus on the first one, by evaluating some mechanisms with respect to media friendliness, scalability and dynamic behaviour. We also present a set of requirements for the ideal congestion control mechanism of real-time flows in 4G networks

Next generation communication networks will be characterized by the coexistence of multiple technologies and user devices in an integrated fashion. The increasing number of devices owned by a single user will lead to a new communication paradigm: users owning multiple devices that form cooperative networks, and networks of different users that communicate with each other, e.g., acquiring Internet access through each other. In this communication scenario no user intervention should be required and technology should seamlessly adapt to the user's context, preferences, and needs. In this paper we address one of those scenarios, interworking between personal area networks, using legacy technologies and the Ambient Network and network composition concepts, herein explained. We argue that new functionalities should be introduced to enable effortless use of legacy technologies in such dynamic and heterogeneous environments.

The 4th generation of mobile communication networks uses heterogeneous wireless technologies. Voice and low quality video flows consist of very small IP packets, for which the standard RTP/UDP/IP headers constitute a significant overhead. Considering that the radio resources are scarce, this overhead may be unacceptable and the adoption of header compression mechanisms is desirable. This paper presents a solution for including RoHC header compression mechanisms within 4G networks; the solution is combined with the mechanisms required to provide QoS to the real time flows

The paper presents an end-to-end quality of service (QoS) architecture suitable for IP communications scenarios that include UMTS access networks. The rationale for the architecture is justified and its main features are described, notably the QoS management functions on the terminal equipment, the mapping between IP and UMTS QoS parameters and the negotiation of these parameters.