The evolution toward 6G networks envisions a heterogeneous Radio Access Network (RAN) comprising diverse access technologies, such as private 5G, public 4G/5G, and Wi-Fi, managed by multiple stakeholders. While considerable research effort has been devoted to O-RAN-based frameworks enabling rApp and xApp implementation and validation, few works provide integrated support for cross-technology RAN orchestration, end-to-end multi-tenancy, and a unified subset of SMO functionalities, including Non-RT RIC components. This paper introduces COSMO, a novel RAN Service Management and Orchestration platform designed to support heterogeneous 3GPP (5G NR, LTE) and non-3GPP (Wi-Fi) access networks. COSMO enables cross-technology multi-tenancy, defined as the capability to allow multiple tenants to dynamically share heterogeneous RAN resources with explicit resource allocation guarantees based on Service Level Agreements (SLAs). This is achieved through management primitives that support flexible and on-demand resource allocation. Additionally, the platform includes a cross-technology Non-Real-Time RAN Intelligent Controller (Non-RT RIC) that enables the development of intelligent rApps for closed-loop control and network orchestration. Beyond its architectural design, COSMO improves resource utilization and operational flexibility through unified orchestration of heterogeneous multi-tenant RAN resources. Through prototyping and benchmarking, we demonstrate the effectiveness of COSMO in resource allocation, SLA enforcement, and scalability. In our prototype, the SLA-based rApp reduces SLA violation from approximately 21% to below 10% under dynamic traffic conditions in a heterogeneous RAN deployment including 5G, 4G, and Wi-Fi access networks. Our results confirm that COSMO offers an efficient solution for managing and orchestrating future multi-tenant cross-technology RAN environments.

Energy efficiency is a critical concern in the deployment and operation of 5G networks, particularly due to the low utilization of 4G and 5G carriers during off-peak hours. While considerable research has focused on designing energy-efficient cell on/off switching strategies that avoid disrupting user connectivity, the integration of operator-specific policies to guarantee particular Quality of Service (QoS) levels has received limited attention. This paper presents a machine learning (ML)-based energy saving strategy, trained using a real-world dataset from a European mobile operator, that enforces operator-defined policies that jointly consider strong throughput requirements and maximum outage tolerance constraints. By tuning the model’s class ratios during training, the proposed solution enables operators to manage the trade-off between energy savings and QoS policy compliance prior to deployment in live networks. Evaluation results show that the method provides substantial energy savings while maintaining policy-compliant service levels under realistic 5G operating conditions.

The integration of IEEE 802.11 Wi-Fi networks in dense deployments faces significant challenges related to resource management, particularly with the introduction of advanced multilink capabilities in Wi-Fi 7. Effective coordination Access Points (APs) is essential for optimizing communication reliability and maximizing overall spectrum efficiency. This research introduces a Graph-Driven Min-Max Link Scheduling (GMMLS) approach, which models traffic load distribution among Multilink-Enabled Devices (MLDs) and facilitates optimal link allocation through enhanced AP coordination. Our extensive evaluations using the NS-3 simulator demonstrate that managing the new multilink operation feature with GMMLS enhances Service Level Agreement (SLA) compliance by up to 88% compared to existing scheduling techniques, making it a highly promising solution for denser environments.

Industrial metrology is used to check dimensional conformance of manufactured parts for quality control of production processes. Today, metrology is implemented using Coordinate Measurement Machines (CMMs) that are deployed in the factory floor and require manual operation from an expert operator. Enabling remote operation of CMM devices across public 5G networks would disrupt the metrology sector, enabling new service models in Industry 5.0. To support remote operation though, 5G networks need to provide a guaranteed service to the traffic flowing between the CMM and the remote operator. In this paper we investigate the use of 5GNR Bandwidth Parts (BWP) as a mechanism to provide these guarantees. To this end, we characterize the traffic generated by remotely operated CMMs, we deploy a BWP-enabled multi-cell factory testbed that resembles a public 5G network, and characterize the impact of the BWP configuration on the performance of the metrology service. Our results demonstrate the feasibility of enabling remote operation of metrology services over public 5G networks.

AI-based quality control of production lines is a key enabler towards Zero Defect Manufacturing (ZDM). The main roadblock to widespread deployment of these technologies is the availability cost-efficient network architectures that provide enough uplink capacity to connect multiple AI-vision enabled cameras deployed across multiple production lines in the factory floor. In this paper we propose a novel wireless network architecture that provides transparent layer 2 services and leverages MPTCP to aggregate Non-public 5G and WiFi 6 to deliver high capacity uplink connectivity in a cost-efficient way. We demonstrate how our architecture enables AI-based quality control in a real production line, while evaluating end-to-end performance in terms of throughput and latency. In addition, we benchmark the performance of a real AI-vision quality control application, where we compare our wireless-based architecture to an Ethernet network, showcasing how our solution is able to serve a quality control application that generates $\mathbf{1 0 0 ~ M B}$ images in less than $\mathbf{1. 5}$ seconds.

As technology advances and factories adopt the industry 4.0 paradigm, the demand for low latency and highly reliable wireless connectivity becomes more crucial than ever. To cater to these challenges multi-link operation (MLO) is a new feature introduced in IEEE 802.11be (Wi-Fi 7) that requires scheduling packets in a station across multiple Wi-Fi links. This paper proposes SLA-MLO, which is a novel MLO scheduler for industrial communications designed to manage per-flow Service Level Agreement (SLA), where an SLA is defined as a maximum allowed percentile latency. We evaluate SLA-MLO by means of NS-3 packet-level simulations and compare it against a state-of-the-art MLO mechanism that schedules packets based on instantaneous per-link congestion measurements. Our results show that SLA-MLO achieves an average reduction in SLA deviation of 50 %.

Next-generation networks have become key enablers for distributed eXtended Reality (XR) services, which pose stringent demands in terms of bandwidth, compute and reliability. In this context, the Network as a Service (NaaS) paradigm, where the network offers APIs tailored to specific vertical domains, is a promising approach to enhance delivery protocols and communications for XR services. In this paper, we present NetXRate which is a new NaaS API tailored to interactive XR services. NetXRate leverages Open RAN (O-RAN) and the network exposure capabilities of the 5GCore, to deliver rate recommendations to XR applications based on the instantaneous capacity available in the cellular network. Using a simulative approach, we demonstrate how NetXRate can be integrated with two state-of-the-art Over-the-Top (OTT) XR rate recommendation strategies, achieving a reduction of up to 72% in outage probability in scenarios with severe network congestion where multiple XR users compete for higher share of bandwidth in the same cell.

Providing 5G seamless and secure services for train passengers is very challenging. In addition to the challenges related to high-speed, many others, such as low signal quality due to 5G coverage holes, tunnels, and complex orography, may compromise the Quality of Service (QoS) provided to train passengers, especially in trains crossing international borders. To overcome these challenges, the EU-funded Horizon 2020 5GMED project has devised a cross-border 5G network architecture that allows train passengers to have the same QoS they have at home in whichever country they are. The network architecture is based on the concept of train neutral host, which operates as a service provider for other mobile network operators. The communication between the train and the ground is facilitated by a heterogeneous network infrastructure comprised of 5G StandAlone (SA), IEEE 802.11ad, and satellite networks to minimize the probability of having low quality signals along the rail track. The seamless switching between these network technologies is performed using Adaptive Communication System-Gateways (ACS-GW) developed in the project. In this paper, we propose the train neutral host architecture, and present the performance evaluation results obtained in a realistic environment over 5G SA networks deployed on the Mediterranean cross-border corridor between Spain and France. Experimental results show satisfying results in terms of service throughput, latency and train inter-cell handover.

Cooperative, Connected and Automated Mobility (CCAM) and Future Railway Mobile Communications Systems (FRMCS) services usually require uninterrupted seamless connectivity. However, in cross-border scenarios, legacy roaming techniques lead to interruption times in the range of one to two minutes, which results unsuitable for the provisioning of demanding CAM and FRMCS services. This paper presents the implementation of state-of-the-art roaming optimization techniques in both the 5G Core and radio access network, including a novel radio optimization handover mechanism for home-routed roaming (HRR) that enables the completion of roaming procedures in 5G Standalone (SA) networks with short interruption times. In addition, a network key performance indicator (KPI) tool is presented to measure the network performance in terms of latency, throughput, and interruption time during roaming. Unlike previous works, static and dynamic performance evaluations are performed in a realistic environment over 5G SA networks deployed on the Mediterranean cross-border corridor between Spain and France. The proposed optimization mechanism yields average interruption time measurements in the range between 135ms and 155ms, enabling seamless service continuity in crossborder scenarios.

3GPP Integrated Access and Backhaul (IAB) allows operators to deploy outdoor mm-wave access networks in a cost-efficient manner, by reusing the same spectrum in access and backhaul. In IAB networks the performance bottleneck is the wireless backhaul segment, where efficient forwarding strategies are needed to effectively use the available capacity. In addition, the performance of the mm-wave IAB backhaul segment is contingent on the availability of line of sight (LoS) conditions in the selected deployment sites. To mitigate LoS dependence, in this paper, we propose to complement the mm-wave backhaul segment of IAB networks with additional Sub6 backhaul links, which contribute to the capacity and robustness of the backhaul network. We refer to IAB networks combining Sub6 and mm-wave links in the backhaul as Sub6 enhanced IAB networks. In this context, the main contribution of this paper is PHaul, a forwarding engine for Sub6 enhanced IAB networks that accomodates different traffic engineering criteria, and combines an offline path selection heuristic with an online Deep Reinforcement Learning (DRL) agent based on Proximal Policy Optimization (PPO). By leveraging a network digital twin of the IAB wireless backhaul, PHaul periodically samples the input traffic of the backhaul network and updates flow to path mappings, with execution times below 10 seconds in realistic backhaul topologies. We present an exhaustive performance evaluation, where we demonstrate that PHaul can achieve gains of up to 36% in throughput efficiency and of up to 20% in fairness, when compared against two alternative heuristics in a wide range of network configurations. We also demonstrate that PHaul is robust to differences between the network topologies considered in the training and inference phases, which can occur in practice due to link failures.

One of the applications where fifth generation (5G) networks are expected to have a greatest impact is vehicular-to-everything (V2X) communications. The exchange of data among different vehicles on the network will make roads a safer environment. However, the massive usage of V2X communications leads to an increase of data traffic and resources consumption. Moreover, the mobility associated to vehicles may drain the available resources in particular cells, compromising the required quality of service (QoS) of connected users. To avoid these situations, the introduction of machine learning algorithms to perform mobility load balancing between neighboring cells arises as a promising tool to ensure to all connected vehicles their demanded resources. In this paper, we address the cell overload problem by proposing an O-RAN compliant Q-learning algorithm that dynamically adapts the handover offset between two neighboring cells to mitigate a network overload situation. The algorithm performance is assessed using realistic vehicular traces. Results show that the network overload appearing during rush hour can be successfully mitigated and the load is fairly distributed between cells.

Vehicular communications are called upon to become one of the services that will benefit the most from 5G networks. The low latency and unprecedented high data rates 5G is expected to deliver are a key enabler for new vehicular services. A booster for this to happen is network slicing, which enables to tailor radio resources to satisfy the different services demand. However, frequent traffic jams in urban scenarios can lead to network overloading, compromising the required Quality of Service by running out of available radio resources. In this paper, we firstly conduct a microscopic analysis of vehicles mobility using realistic vehicular traces to detect traffic jams. Secondly, the network overload introduced by this congestion is studied considering a network slicing environment. Finally, a load balancing approach to mitigate the radio resources demand is proposed. Results show a significant mitigation of the overload situation.

Fifth Generation (5G) networks will support countless new applications and new business models. One of the 5G paradigms is network slicing, which enables the integration of multiple logical networks each one tailored to the requirements of the different services that can be provided by both network operators and vertical industries. One of the services where 5G is expected to have a greatest impact is vehicular-to-everything (V2X) communications, which will have their stringent latency requirements now met. However, the mobility associated to vehicles can lead to cell overload compromising the required quality of service (QoS). To address this problem, in this paper we propose and evaluate the performance of three network overload alleviation techniques to control network congestion provoked by traffic jams using realistic vehicular traces in a network slicing environment. Firstly, we describe the architecture supporting V2X communications. Secondly, the network congestion control approaches are explained. Finally, after providing a complete description of the considered scenario, results will be detailed, showing that the network overload appearing during rush hour can be significantly reduced.

As part of 3GPP releases 15 and 16 Wi-Fi networks have been integrated with the 5G Core, which is a key feature in private network scenarios. Another important feature for private networks is multi-tenancy, whereby an infrastructure provider shares a common radio access network among several tenants subject to service level agreements (SLAs). 3GPP has defined network slicing on the radio access segment supporting multi-tenancy for 5GNR, but a similar feature is lacking in Wi-Fi networks. In this paper we present G-ADRR, which, to be best of our knowledge, is the first global slicing policy for Wi-Fi that delivers per-tenant radio level SLAs over a given geographical area. We extensively evaluate the performance of G-ADRR by means of an experimental prototype and packet level simulations, and demonstrate its advantages as compared to two alternative global scheduling strategies and a static slice configuration policy commonly used in the state-of-the-art.

Despite recent progress in orchestration of Virtual Network Functions (VNFs) and in multi-technology SDN connectivity, the automated provisioning of end-to-end network services composed of virtual functions deployed across distributed compute locations remains an open challenge. This problem is especially relevant to support the deployment of future 5G networks, comprising virtual access and core network functions connected through a potentially multi-domain transport network. In this paper we present and demonstrate the 5GOS, a lightweight end-to-end orchestration framework that enables the automated provisioning of virtual radio access network services. Using an experimental multi-domain testbed we demonstrate that the 5GOS can provision multi-domain virtual Wi-Fi and LTE services in less than three minutes.

Software Defined Wireless Networks offer an opportunity to enhance the performance of specific services by applying centralized mechanisms which make use of a global view of the network resources. This paper presents FALCON, a novel solution that jointly optimizes fair airtime allocation and rate recommendations for Server and Network Assisted DASH video streaming, providing proportional fairness among the clients. Since this problem is NP-hard, FALCON introduces a novel heuristic algorithm that is proved to achieve almost optimal results in a practical amount of time. The performance of FALCON is evaluated when used in conjunction with three referent Adaptive Bit Rate strategies (PANDA, BOLA and RobustMPC) in a simulated ultra-dense In-flight Entertainment System scenario. The obtained results show that FALCON provides significant benefits by minimizing instability and buffer underruns, while obtaining a fair video rate and airtime allocation among clients, thus contributing to an enhanced Quality of Experience.

Vehicular communications will foster mobility services and enable mass adoption of future autonomous vehicles, interchanging huge amount of data acquired from vehicles’ sensors. 3GPP Release 14 presents the first standard for supporting V2X in LTE. Several enhancements are introduced, including a new arrangement of the physical resource grid, where subchannels are the minimum resource unit instead of Resource Blocks. The resource grid is defined by several design parameters, some of them with constraints imposed by 3GPP specifications, that affect the maximum message transmission rate and efficiency of the system. Moreover, the optimum choice of these parameters is closely linked to message length, which is another variable parameter. This paper provides an analysis of the relationship between these design parameters (Resource Block per Subchannel, Transport Block Size Index, and coding rate), message size, and the system’s maximum capacity and efficiency. In doing so, we do not consider channel reuse or radio transmission characteristics because the focus of this paper is trying to find the resource grid design parameters that optimize system capacity, which is a very important aspect to consider by V2X operators.

Software-defined networking is at the root of future 5G network design. Among others, it allows for automated network reconfiguration and network slicing support. In this paper we present an implementation of a hierarchical control plane in the transport network architecture envisioned by the 5G-PICTURE project. We analyze the implementation of the slicing mechanism at the network edge and the end-to-end path establishment procedure, which involves interactions within a hierarchy of controllers. We also evaluate the latency performance of our control plane solution, by means of testbed experimentation.

To deliver on the vision of network slicing, future 5G networks will have to provide virtualization and isolation for a variety of Radio Access Network (RAN) technologies. In this paper we study how to implement RAN slicing in Wi-Fi networks. In particular, we present a scheduling algorithm that allocates airtime through a set of virtual Wi-Fi interfaces executing on the same or different physical radios, in order to fulfill each tenant's Service Level Agreement. We demonstrate a practical implementation of our scheduler on Linux systems based on the mac80211 driver that contrary to other existing solutions is independent from underlying hardware drivers. Finally, we experimentally evaluate the performance of our scheme through a set of realistic experiments.

Dense deployments of small cells (SCs) are key to fulfill the capacity requirements of future 5G networks. However, two roadblocks to the adoption of SCs are: 1) the limited availability and the cost of sites with wired backhaul resources and 2) the complexity to manage a dense deployment of wireless backhaul nodes. Towards these challenges we propose SODALITE, a novel system that applies software-defined networking (SDN) to a wireless backhaul network. We present, how SODALITE can be integrated to 3GPP's 4G and 5G architectures, and show the feasibility of SODALITE through LTE network testbed experiments. We substantiate the scalability of SODALITE through stochastic studies using real-life traffic traces from an LTE network and discuss the effects of cell densification and 5G system architecture on these studies. Further, a reliable backhauling solution for wireless links is introduced in SODALITE through SDN-enabled mechanisms that are capable of reconfiguring the data plane upon a link failure detection. Its reliability is shown through experiments on an LTE network testbed, and studied thoroughly via rigorous simulations and network emulator evaluations. As a result, we claim that SODALITE is a promising carrier-grade system to manage a wireless SC backhaul.

In this paper we present SWAM, a system that builds on commodity Wi-Fi routers with multiple wireless interfaces to provide a wireless access infrastructure supporting multi-tenancy, mobility, and integrated wireless access and backhaul. An infrastructure provider can deploy inexpensive SWAM nodes to cover a given geographical area providing on-demand connectivity for Mobile Network Operators. Our main contribution is the design of the SWAM datapath and control plane, which are inspired by the overlay techniques used to enable multi-tenancy in data-center networks. We prototype SWAM in an office wireless testbed and validate experimentally its functionality.

The Education Roaming (eduroam) is a widely spread federation of Wi-Fi networks that allows users to freely roam between providers. The key to eduroam's massive adoption has been the fact that a single global network identifier is used across all the federated Wi-Fi networks. However, when multiple eduroam networks are deployed in an overlapping geographical area, the current design may cause client devices to inadvertently roam between providers, hence potentially disrupting applications and hindering network planning. In this paper we study how virtual Access Points (vAPs) can be used to address the inter-domain mobility problem in eduroam. Our main contribution is a novel mechanism to steer clients across vAPs, which mitigates unintended handovers. The proposed extensions have been validated experimentally considering a wide range of commercial Wi-Fi clients available in the market.

Dense Small Cell networks are considered the most effective way to cope with the exponential increase in mobile traffic demand expected for the upcoming years and are one of the foundations of the future 5G. However, novel architectures are required to enable cost-efficient deployments of very dense outdoor Small Cell networks, complementing the coverage layer provided by macro-cells. In this regard, two important challenges need to be solved to make this vision a reality: i) increased traffic dynamics, which are translated into more frequent handovers, and ii) cost-efficient deployment of large number of Small Cells. In this paper we propose and evaluate SENSEFUL, an novel architecture addressing the two problems highlighted above: Software-Defined Networking (SDN) as the key technology to promote adaptability to a varying environment and provide efficient mobility solutions in the dense access layer, and novel wireless backhauling technologies where traditional wired connectivity does not meet cost/efficiency restrictions.

The paper elaborates on the technological and architectural innovations researched and developed by 5G-PPP Phase 1 projects and covering innovation areas such as 5G system design and evaluation, novel air interfaces, network management and security as well as virtualization and service deployment aspects.

The popularity and wider acceptance of IEEE 802.11 based WLANs has resulted in their dense deployments in diverse environments. While this massive deployment can potentially increase capacity and coverage, the current physical carrier sensing of IEEE 802.11 cannot limit the overall interference induced and also cannot insure high concurrency among transmissions. Recently, the IEEE 802.11 working group has continued efforts on developing WLAN technology through the creation of the TGax, which aims to improve efficiency of densely deployed IEEE 802.11 networks. In this paper, we propose a Dynamic Sensitivity Control for Access Point (DSC-AP) algorithm for IEEE 802.11ax. This algorithm dynamically adjusts the Carrier Sensing Threshold (CST) of an AP based on received signal strength from its associated stations and interfering APs. We show that the aggregate throughput of a dense network (under asymmetric traffic conditions) utilizing DSC (both at the stations and AP) is considerably improved (i.e. up to 32%) when compared with legacy IEEE 802.11.

In this paper we introduce a novel reliable asynchronous protocol that allows to establish a VLC link between a LED luminary and an off-the- shelf smartphone. Our protocol and decoding algorithms benefit from the access to advanced camera settings available in the latest generations of mobile devices to outperform previous VLC designs in the state of the art. In particular, in the paper we provide an experimental evaluation using a commercial Nexus 5 device where it is shown how our designed prototype can achieve transmission speeds of up to 700 bps and operating distances of up to 3 meters. The previous performance figures have the potential to spark a wide set of novel applications.

Small Cells are recognized as one of the key enablers to address the forecasted exponential traffic increase in future mobile networks. However, several major technical hurdles need to be overcome to fulfill the promise of Small Cells. In this paper we introduce a novel architecture that enables the application of Software Defined Networking (SDN) techniques on the wireless Small Cell backhaul. We demonstrate our architecture in a testbed prototype, where we analyze the costs incurred in the additional processing and the centralization of state imposed by SDN, while motivating the benefits of our architecture in terms of improved network management and control.

The explosive growth in the usage of IEEE 802.11 network has resulted in dense deployments in diverse environments. Most recently, the IEEE working group has triggered the IEEE 802.11ax project, which aims to amend the current IEEE 802.11 standard to improve efficiency of dense WLANs. In this paper, we evaluate the Dynamic Sensitivity Control (DSC) Algorithm proposed for IEEE 802.11ax. This algorithm dynamically adjusts the Carrier Sense Threshold (CST) based on the average received signal strength. We show that the aggregate throughput of a dense network utilizing DSC is considerably improved (i.e. up to 20%) when compared with the IEEE 802.11 legacy network.

Current IEEE 802.11 WLANs suffer from the well-known rate anomaly problem, which can drastically reduce network performance. Opportunistic relaying can address this problem, but three major considerations, typically considered separately by prior work, need to be taken into account for an efficient deployment in real-world systems: 1) relaying could imply increased power consumption, and nodes might be heterogeneous, both in power source (e.g., battery-powered versus socket-powered) and power consumption profile; 2) similarly, nodes in the network are expected to have heterogeneous throughput needs and preferences in terms of the throughput versus energy consumption tradeoff; and 3) any proposed solution should be backwards-compatible, given the large number of legacy 802.11 devices already present in existing networks. In this paper, we propose a novel framework, Self-Optimizing, Legacy-Compatible Opportunistic Relaying (SOLOR), which jointly takes into account the above considerations and greatly improves network performance even in systems comprised mostly of vanilla nodes and legacy access points. SOLOR jointly optimizes the topology of the network, i.e., which are the nodes associated to each relay-capable node; and the relay schedules, i.e., how the relays split time between the downstream nodes they relay for and the upstream flow to access points. Our results, obtained for a large variety of scenarios and different node preferences, illustrate the significant gains achieved by our approach. Specifically, SOLOR greatly improves network throughput performance (more than doubling it) and power consumption (up to 75% reduction) even in systems comprised mostly of vanilla nodes and legacy access points. Its feasibility is demonstrated through testbed experimentation in a realistic deployment.

Wi-Fi has enjoyed since its conception a striking success as a technology to access the Internet. Thus, Wi-Fi is today embedded in most of our portable devices like smartphones and tablets. Leveraging the ubiquity and mobility of Wi-Fi devices, in this paper we study how to expand current Wi-Fi implementations in order to design a mechanism that allows mobile devices to advertise and discover small chunks of information in an energy efficient way, even in crowded environments. We refer to this mechanism as Energy Efficient Discovery (E2D) Wi-Fi. Our contribution in this paper is twofold. First, we present the design of E2D Wi-Fi as a set of driver level extensions to current Wi-Fi implementations, which can de deployed with just a firmware upgrade, and that focus on discovery, synchronization and channel access. Second, we present a thorough performance evaluation based on packet level simulations that demonstrates the performance to be expected from E2D Wi-Fi in realistic scenarios.

ABSTRACT Wireless LAN is becoming a pervasive wireless access technology that can be found in almost any mobile device such as laptops, PDAs, portable game consoles and mobile phones. Each of these groups of devices have a different set of requirements according to their intended use and applications but most of them share two main requirements: QoS support to satisfy applications' demands and power saving functionality to achieve an operating time according to users' expectations. IEEE 802.11e defines two centralized solutions in order to address these problems: Hybrid Coordination Channel Access (HCCA) for QoS and Scheduled Automatic Power Save Delivery (S‐APSD) for power saving. The focus of our work in this paper is the analysis and evaluation of a proposed centralized scheduler that makes use of both aforementioned IEEE 802.11e QoS and power saving solutions. Our contributions are as follows: (i) Design and analytical modeling of a proposed centralized scheduler (DRA) that maximizes the minimum distance between the resource allocations with pseudo‐polynomial complexity, (ii) Extensive performance evaluation of the QoS and power saving benefits of theDistributionproposal (DRA) as compared to a genericGroupingone (GRA), and (iii) Evaluation of the complexity and scalability of the proposal to assess its feasibility in practice. Copyright © 2010 John Wiley & Sons, Ltd.

WiMAX is one of the most promising technologies to provide broadband wireless access in the near future. In this paper we identify a key element for the performance of a WiMAX network, the DL-MAP packing algorithm, which mainly determines the usage efficiency of the available radio resources and investigate potential differences that could appear between WiMAX equipment vendors in the maximum capacity of the system due to the packing approach used. Our results show that the performance of simple DL-MAP packing algorithms might be significantly outperformed by more complex ones resulting in a clear differentiation factor among manufacturers.

The integration of the wireless LAN technology in mobile devices such as cellular phones or PDAs has become a user need due to its popularity for providing high speed wireless Internet access at a low cost. Such devices though should meet users' expectations with regard to QoS, e.g., guarantee a reasonable voice quality when VoIP is used, and power saving efficiency, e.g., standby and calling times should be similar to the ones of cellular phones. IEEE 802.11e defines QoS and power saving enhancements that should allow the wireless LAN technology address users' wishes in such specific devices. Our focus is the study of the distributed power saving mechanism of 802.11e, i.e., U-APSD, as compared to the legacy 802.11 power saving mode in order to assess its suitability for solving the challenges of the upcoming mobile devices requirements. Our contributions are as follows. We provide first an overview of the U-APSD functionality. Then, we describe in detail our proposed implementation of the U-APSD mechanism, Adaptive U-APSD (AU-APSD), a generic solution that requires only information available at the MAC layer. Finally, we quantify the performance improvements that are obtained with our proposed AU-APSD implementation as compared to the legacy 802.11 power save mode.

The popularity of wireless LANs, due to their low cost provision of high speed wireless Internet access, has resulted in a strong trend toward the integration of this technology in the upcoming all-in-one mobile devices that could include, for instance, cellular, wireless LAN and personal digital assistant (PDA) capabilities. Such devices, though, require power saving mechanisms in order to guarantee a reasonable battery duration. The 802.11 standard provides a power save mode that reduces the wireless LAN technology power consumption, however, this mode can result in downlink delays (AP to station) unacceptable for the QoS of some applications, e.g., VoIP. To overcome this problem, we propose an adaptive power save mode algorithm (APSM) that adapts the data frames MAC downlink delay of a certain station according to the downlink frame interarrival time observed at the AP MAC layer. We conducted an evaluation of our proposal with respect to downlink delay, power efficiency and signaling load using the OPNET simulator and compared its performance with the 802.11 standard power save mode and two different static alternatives. The results show the effectiveness of our algorithm in providing a soft upper bound to the MAC downlink delay while significantly decreasing the power consumption and requiring a signaling load similar to that of the standard power save mode.