Large-scale deep learning models rely on wireless networks for distributed training approaches, which are essential to meet the immense computational and data demands. However, the stochastic nature of wireless environments introduces significant challenges such as variable delays, noise interference, and packet loss, which lead to degraded gradient synchronization and hinder model convergence. In this work, we propose a novel communication-aware distributed training (CADT) framework that integrates real-time channel state information (CSI) feedback into the gradient aggregation process. Unlike conventional methods that assume static or ideal communication conditions, CADT dynamically reweights gradients from each node based on instantaneous channel quality, enabling robust aggregation under adverse wireless conditions. By dynamically adjusting the contribution of each node based on instantaneous channel conditions, CADT effectively compensates for wireless impairments, thereby ensuring more reliable gradient aggregation and significantly improving both convergence speed and final model accuracy. Extensive experiments on CIFAR-10, CIFAR-100, ImageNet, and SVHN using Vision Transformer and ResNet-50 demonstrate that CADT outperforms baseline methods in terms of convergence, accuracy, and communication efficiency. In addition, we provide a rigorous theoretical analysis that establishes convergence guarantees under realistic wireless conditions, thereby advancing the theoretical foundation of distributed optimization in non-ideal communication environments.Our framework offers a practical solution for real-world scenarios such as edge computing, where communication constraints and environmental variability are dominant factors.

Efficiently running services in the worldwide scattered spectrum bands is increasingly difficult as those bands get ever more congested. Unlicensed spectrum offers a promising solution but effective coexistence among access technologies is required to mitigate interference and ensure the always more stringent Quality of Service required by new services. Besides, the increasing number and complexity of security threats further challenge system reliability. To address these issues, this paper proposes a mechanism to enhance security and reliability , and a blockchain-based framework for spectrum sensing and sharing in Wi-Fi and Long Term Evolution-Unlicensed (LTE-U) coexistence networks. Performance under different channel fading models is analyzed, and obtained results show the effectiveness of the proposed scheme, improved sensing accuracy, and resistance to malicious behavior in both low- and high-density scenarios.

The rapid advancement of Large Language Models (LLMs) has opened new possibilities for intelligent multi-agent systems capable of autonomously performing complex tasks. To build such systems, LLMs can be leveraged for task-solving, tool interaction, and code generation but at the same time their costs and unpredictability have to be properly managed. To do so this paper introduces COPMA, a model-based approach to enabling continuous human-LLM co-programming of multi-agent LLM applications. COPMA uses feature-block models to track application features and their implementations as agents and code blocks. Supported by co-programming patterns, de-velopers are guided in constructing, refining, and refactoring feature implementations via trial-and-errors with LLM agents, leveraging their feedback, suggestions, and code examples. The patterns guide the shift of feature implementations between agents and code to balance flexibility, predictability, and cost. Our experience in developing LLM agents for collecting and reviewing medical research papers demonstrates that human-LLM co-programming can reduce development effort to enable rapid prototyping of multi-agent LLM applications.

In light of the evolving landscape of future converged networks, heightened demands for scalability and performance have become paramount, particularly in the industrial context where enhanced flexibility, adaptability, time synchronization and deterministic low latency are key features. Time-Sensitive Networking (TSN) has been introduced to provide converged networks with determinism. To mitigate the complexities associated with cabling requirements, Wireless Time-Sensitive Networking (WTSN) emerges as a more suited solution in industrial applications that need mobility. This study presents an application in smart manufacturing that incorporates WTSN technology. Specifically, it focuses on a scenario where an Unmanned Ground Vehicle (UGV), under the control of an edge controller connected through a wireless link, operates within a critical environment subject to constraints such as to avoid obstacles and to build a real-time map of the surrounding area. The mapping outcome performed by the edge controller demonstrates the effectiveness of the WTSN capabilities.

This paper presents, for the first time, a method to extract both range and Doppler information from commercial Wi-Fi Channel State Information (CSI) using a monostatic (single transceiver) setup. Utilizing the CSI phase in Wi-Fi sensing from a Network Interface Card (NIC) not designed for full-duplex operation is challenging due to (1) Hardware asynchronization, which introduces significant phase errors, and (2) Proximity of transmit (Tx) and receive (Rx) antennas, which creates strong coupling that overwhelms the motion signal of interest. We propose a new signal processing approach that addresses both challenges via three key innovations: Time offset cancellation, Phase alignment correction, and Tx/Rx coupling mitigation. Our method achieves cm-level accuracy in range and Doppler estimation for moving targets, validated using a commercial Intel Wi-Fi AX211 NIC. Our results show successful detection and tracking of moving objects in realistic environments, establishing the feasibility of high-precision sensing using standard Wi-Fi packet communications and off-the-shelf hardware without requiring any modification or specialized full-duplex capabilities.

Wi-Fi is a very common means for providing wireless access to the Internet, e.g., using the 2.4GHz Industrial, Scientific, and Medical (ISM) band and more recently also the 6 GHz band via Wi-Fi 6E. Thanks to a chip recently launched by Semtech, in the same 2.4GHz band now can also operate Long Range (LoRa), which is widely used in Internet of Things (IoT) applications due to its low power consumption and wide coverage range. To allow for data interchange among these technologies, multi-radio gateways are needed, which introduce additional costs, complexities, and potential points of failure. To address this challenge, we propose the concept of Wireless to LoRa (WiLo) to make directional communication from Wi-Fi to LoRa. WiLo uses physical-layer (PHY) communication and dedicated input chips in the 2.4 GHz band to transmit information. To overcome the modulation technique differences between Wi-Fi and LoRa, WiLo leverages narrow-band communication, a technique that generates ultra-narrowband signals using single-tone sinusoidal signals by manipulating the payload of Wi-Fi devices. These signals can be detected by LoRa Wide Area Network base stations due to their high receiver sensitivity for long-range communication. Our experiments, which make use of both Universal Software Radio Peripheral (USRP) and commodity devices, demonstrate that WiLo can achieve concurrent wireless communication over a distance of 500 m, from commercial Wi-Fi chips to a LoRaWAN, with more than 96% frame reception rate. These findings show the effectiveness of WiLo in enabling reliable and efficient wireless communication over long distances, making it particularly relevant for applications such as remote monitoring systems, sensor networks, and smart cities.

The demand for precise time synchronization is of great interest in contemporary networked systems, especially in the context of converged networks where seamless communication among devices, actuators, and sensors is imperative. This has led to the development and adoption of Time-Sensitive Networking (TSN) and Wireless TSN (WTSN) technologies, with a particular attention to the IEEE 802.1AS Generalized Precision Time Protocol (gPTP) standard. In this work, we explore the role of time synchronization in a wireless network scenario involving an Unmanned Ground Vehicle (UGV) that has to track a desired time dependent trajectory. The UGV receives trajectory waypoints from a secondary station, emphasizing the importance of precise timing in trajectory tracking. Utilizing the IEEE 802.1AS standard for wireless time synchronization, this study investigates the impact of clock synchronization errors and latency on trajectory tracking. Hence, it demonstrates the capability and benefits of IEEE 802.1AS in managing device clocks and facilitating time correction to ensure precise trajectory tracking despite synchronization errors and latency.

Multi-Link Operation (MLO) in Wi-Fi 7 is expected to tangibly boost throughput while lowering transmission latency at the same time. This is very relevant in industrial scenarios and makes MLO suitable, e.g., to support seamless device mobility. Benefits depend on the ability of multi-link devices to select at run-time the best link, among the available ones, in order to maximize both communication performance and reliability.In this paper an experimental platform is proposed, with the aim of leveraging commercial hardware and open source software, and easing prototyping and evaluation of MLO techniques. The platform has been employed to analyze the transmission quality of two pairs of non-overlapping channels, and in particular to assess whether or not adequate diversity is provided, so that those channels can be exploited to improve reliability. Results point out that correlation between different links is, in most cases, limited, which makes MLO a valuable approach.

The next generation of Wi-Fi is meant to achieve ultra-high reliability for wireless communication. Several approaches are available to this extent, some of which are being considered for inclusion in standards specifications, including coordination of access points to reduce interference.In this paper, we propose a centralized architecture based on digital twins, called WiTwin, with the aim of supporting wireless stations in selecting the optimal association according to a set of parameters. Unlike prior works, we assume that Wi-Fi 7 features like multi-link operation (MLO) are available. Moreover, one of the main goals of this architecture is to preserve communication quality in the presence of mobility, by helping stations to perform reassociation at the right time and in the best way.

Managing Wireless networks, particularly in industrial and factory environments, to meet the escalating demands of time critical applications has become more complex and warrants proactive management strategies. This work introduces an innovative approach to wireless network management enabled by a Digital Twin (DT) designed and continuously enhanced by real-time device telemetry and user inputs through an Extended Reality interface. By collecting real telemetry data from network devices, our methodology defines and calibrates a DT representation of the network, enabling accurate prediction of wireless signal properties and network performance based on simulation models. The DT serves as an automation tool to analyze various scenarios, allowing for informed adjustments to user applications, devices and network configurations. The paper describes a real-life DT implementation of a wireless system in a real enterprise network scenario. Experimental results are provided demonstrating improved performance and user experience enabled by the proposed DT-based network management. The proposed methodology addresses the challenges of real-time network optimization and contributes to advance wireless network management based on device and network telemetry.

In the rapidly evolving domain of satellite communications, integrating advanced machine learning techniques, particularly split learning, is crucial for enhancing data processing and model training efficiency across satellites, space stations, and ground stations. Traditional ML approaches often face significant challenges within satellite networks due to constraints such as limited bandwidth and computational resources. To address this gap, we propose a novel framework for more efficient SL in satellite communications. Our approach, Dynamic Topology-Informed Pruning, namely DTIP, combines differential privacy with graph and model pruning to optimize graph neural networks for distributed learning. DTIP strategically applies differential privacy to raw graph data and prunes GNNs, thereby optimizing both model size and communication load across network tiers. Extensive experiments across diverse datasets demonstrate DTIP’s efficacy in enhancing privacy, accuracy, and computational efficiency. Specifically, on Amazon2M dataset, DTIP maintains an accuracy of 0.82 while achieving a 50% reduction in floating-point operations per second. Similarly, on ArXiv dataset, DTIP achieves an accuracy of 0.85 under comparable conditions. Our framework not only significantly improves the operational efficiency of satellite communications but also establishes a new benchmark in privacy-aware distributed learning, potentially revolutionizing data handling in space-based networks.

For the future 6G-enabled massive Internet of Things (mIoT), how to effectively manage spectrum resources to support huge data traffic under the large-scale overlapping caused by the dense deployment of massive devices is the imperative challenge. In this paper, a novel hypergraph interference model is designed, and two reinforcement learning (RL)-based resource management algorithms in the 6G-enabled mIoT are proposed to enhance the network throughput and avoid overlapping interference. Then, based on the hypergraph interference model, the resource management problem of execution network throughput maximization is theoretically formulated under large-scale overlapping interference scenarios. To handle this problem, we convert it into a Markov decision process (MDP) model and then deal with this MDP model through the advantage actor-critic (A2C)-based resource management algorithm and asynchronous advantage actor-critic (A3C)-based resource management algorithm, which aim to maximize network throughput of the spectrum resource allocation among massive devices. The simulation results verify that the proposed algorithms can not only avoid large-scale overlapping interference but also improve the network throughput.

The concurrent availability of power-line communication, cellular and other wireless technologies, and the rapid development of concepts like multi-mode power internet of things (PIoT) and digital twin (DT) have made intelligent energy control a reality. However, information timeliness guarantee which determines DT consistency and energy control degradation is still an unsolved issue, as recent studies mainly focus on time-averaged guarantee and ignore the occurrence of extreme events. In this paper, we propose a novel metric named ultra-low age of information (ULAoI), which imposes more stringent constraints on extreme event occurrence probability and high-order statistics characteristics of excess AoI value. Moreover, we minimize energy control performance degradation by optimizing communication control, which acts as the bridge connecting information collection and utilization. A joint device scheduling and multi-mode channel allocation algorithm based on ULAoI-prioritized learning is proposed to achieve communication and control integration. Simulation results verify that the proposed algorithm can significantly reduce energy control performance degradation and achieve ULAoI guarantee.

Industrial Internet of Things (IIoT) use cases have stringent reliability and latency requirements to enable real-time wireless control systems, which are supported by the 5G ultra-reliable low-latency communications (URLLC). However, extremely high quality-of-service (QoS) requirements in 5G URLLC causes huge radio resource consumption and low spectral efficiency, thus limiting network capacity in terms of the number of supported devices. Industrial control applications typically incorporate redundancy in their design and may not always require extreme QoS to achieve the expected control performance. Therefore, we propose both communication-control co-design and dynamic QoS to address the capacity issue for robotic manipulation use cases in 5G-based IIoT. We have developed an advanced co-simulation framework that includes a network simulator, physics simulator, and compute emulator, for realistic performance evaluation of the proposed methods. Through simulations, we show significant improvements in network capacity (i.e., the number of supported URLLC devices), and about 2x gain for the robotic manipulation use case.

This paper provides an extensive analysis of Reliable and Available Wireless (RAW) enhancements and solutions needed to manage industrial environments more effectively. Starting from the description of a representative industrial use case, an analysis of gaps and promising new extensions is performed. Namely, the need to (i) support multi-domain operation, at both technology and administrative levels; (ii) integrate RAW with edge architectures; and, (iii) increase the mobility support in RAW networks. The identified gaps are indeed not yet tackled by the relevant standardization development organizations, mainly the Internet Engineering Task Force (IETF), and are thus object of our future work.

Following the Industry 4.0 vision of a full digitization of the industry, time-critical services and applications, allowing network infrastructures to deliver information with determinism and reliability, are becoming more and more relevant for a set of vertical sectors. As a consequence, deterministic network solutions are progressively emerging, albeit they are still bounded to specific technological domains. Even considering the existence of interconnected deterministic networks, the provision of an end-to-end (E2E) deterministic service over them must rely on a specific control plane architecture, capable of seamlessly integrate and control the underlying multi-technology data plane. In this work, we envision such a control plane solution, extending previous works and exploiting several innovations and novel architectural concepts. The proposed control architecture is service-centric, in order to provide the necessary flexibility, scalability, and modularity to deal with a heterogenous data plane. The architecture is hierarchical and encompasses a set of management platforms to interact with specific network technologies overarched by an E2E platform for the management, monitoring, and control of E2E deterministic services. Furthermore, Artificial Intelligence (AI) and Digital Twinning are used to enable network predictability and automation, as well as smart resource allocation, to ensure service reliability in dynamic scenarios where existing services may terminate and new ones may need to be deployed.

By removing wire harness, Wi-Fi is becoming increasingly pervasive in every aspect of our lives, in both the consumer and industrial worlds. Besides flexibility, the recent high efficiency and extremely high throughput versions managed to close the performance gap with Ethernet. However, it still lags behind Ethernet for what concerns dependability. To this aim, the ultra high reliability study group has been recently formed. This paper reports on some preliminary ideas and proposals about the ways seamless redundancy can be exploited to make Wi-Fi more reliable, yet retaining a good degree of backward compatibility with existing network infrastructures.

Mobile and autonomous robots are among the most critical technology applications requiring wireless connectivity with deterministic performance, including bounded latency with high reliability, even under congested network conditions. Emerging Wireless Time-Sensitive Networking (WTSN) capabilities over Wi-Fi and 5G can enable time synchronization and bounded low latency through time-aware scheduling mechanisms. Such Wireless TSN capabilities have been demonstrated in several industrial/robotic use cases, but under static conditions. Mobility introduces new challenges due to the roaming events and associated network outages owing to signaling between client devices and network infrastructure during these events. In this paper, we show how we can take advantage of the TSN redundancy capability (as defined in the IEEE 802.1CB standard) to eliminate outages or delays due to events like roaming and interference in a mobile robot use case enabled by Wi-Fi 6 TSN. We demonstrate the roaming performance with no delay impact on the applications though simulations of a mobile robot in a factory scenario and experimental results with a mobile robot connected via multiple Wi-Fi 6 radios in a warehouse environment.

Age of information (AoI) is an important metric of information timeliness, which determines digital twin (DT) consistency and energy management precision. However, AoI guarantee in the time-averaged sense is unreliable to avoid the occurrence of extreme event. In this paper, we propose a novel information timeliness metric named ultra-low AoI (ULAoI). Compared with AoI, ULAoI further considers the occurrence of extreme event and higher-order statistical characteristics of excess AoI value. Multi-dimensional resources of power internet of things (PIoT) are jointly allocated to achieve ULAoI guarantee from the perspective of sensing-communication-control integration. ULAoI-DT-Prioritized deep Q network (DQN) is proposed to achieve coordinated resource allocation by approximating unobservable information with the assistance of ULAoI-DT, and preventing DQN training from using samples with large AoI based on ULAoI-induced priority. Simulation results demonstrate the superior performance of the proposed algorithm in global loss function, ULAoI guarantee, and energy management optimality.

This paper proposes a novel approach for privacy preserving face recognition aimed to formally define a trade-off optimization criterion between data privacy and algorithm accuracy. In our methodology, real world face images are anonymized with Gaussian blurring for privacy preservation. The anonymized images are processed for face detection, face alignment, face representation, and face verification. The proposed methodology has been validated with a set of experiments on a well known dataset and three face recognition classifiers. The results demonstrate the effectiveness of our approach to correctly verify face images with different levels of privacy and results accuracy, and to maximize privacy with the least negative impact on face detection and face verification accuracy.

Network virtualization allows the coexistence of multiple network slices over a shared physical infrastructure, each delivering a service with own requirements in terms of quality of service, coverage, and time span. Spectrum is an expensive commodity, so it must be managed among these slices in the most efficient way. However, current spectrum sharing techniques are too rigid to address all combinations of service requirements and properly exploit the new air interface flexibility and network deployment options introduced by the Fifth Generation (5G) mobile networks. In this paper, we extend the state of the art first by proposing a novel spectrum sharing scheme that supports an unlimited number of radio networks. Second, we propose a 5G compliant novel service-based network management architecture to integrate Machine Learning (ML) algorithms for Radio Resource Management (RRM), including spectrum sharing. Finally, we propose a new three-stage ML framework that exploits forecasting, clustering and reinforcement learning algorithms to implement the proposed spectrum sharing scheme. Our proposed solution can allow an arbitrary number of 5G network slices to share spectrum more effectively and more dynamically either with each other or with other radio networks.

In-Network Computing is a promising field that can be explored to leverage programmable network devices to offload computing towards the edge of the network. This has created great interest in supporting a wide range of network functionality in the data plane. Considering a networked robotics domain, this brings new opportunities to tackle the communication latency challenges. However, this approach opens a room for hardware-level exploits, with the possibility to add a malicious code to the network device in a hidden fashion, compromising the entire communication in the robotic facilities. In this work, we expose vulnerabilities that are exploitable in the most widely used flexible framework for writing robot software, Robot Operating System (ROS). We focus on ROS protocol crossing a programmable SmartNIC as a use case for In-Network Hijacking and In-Network Replay attacks, that can be easily implemented using the P4 language, exposing security vulnerabilities for hackers to take control of the robots or simply breaking the entire system.

In the Internet of Health Things (IoHT)-based e-Health paradigm, a large number of computational-intensive tasks have to be offloaded from resource-limited IoHT devices to proximal powerful edge servers to reduce latency and improve energy efficiency. However, the lack of global state information (GSI), the adversarial competition among multiple IoHT devices, and the ultra reliable and low latency communication (URLLC) constraints have imposed new challenges for task offloading optimization. In this article, we formulate the task offloading problem as an adversarial multi-armed bandit (MAB) problem. In addition to the average-based performance metrics, bound violation probability, occurrence probability of extreme events, and statistical properties of excess values are employed to characterize URLLC constraints. Then, we propose a URLLC-aware Task Offloading scheme based on the exponential-weight algorithm for exploration and exploitation (EXP3) named UTO-EXP3. URLLC awareness is achieved by dynamically balancing the URLLC constraint deficits and energy consumption through online learning. We provide a rigorous theoretical analysis to show that guaranteed performance with a bounded deviation can be achieved by UTO-EXP3 based on only local information. Finally, the effectiveness and reliability of UTO-EXP3 are validated through simulation results.

Following the tremendous success of 2G and 3G mobile networks and the fast growth of 4G, the next generation mobile networks (5G) was proposed aiming to provide infinite networking capability to mobile users. Differentiated from 4G, benefits offered by 5G is much more than the increased maximum throughput. It aims to involve and benefit from many current technical advances, including Internet of Things (IoT). As the IoT integrates many heterogeneous networks, such as wireless sensor networks, wireless local area networks, mobile communication networks (3G/4G/LTE/5G), wireless mesh networks, and wearable health care systems, it is critical to design self-organizing and smart protocols for heterogeneous ad hoc networks in various IoT applications, such as cyber-physical systems, cloud computing for heterogeneous ad hoc networks, large-scale sensor networks, data acquisition from distributed smart devices, green communication and applications, environmental monitoring and control, etc. Moreover, based on the survey conducted by the World Health Organization, the world will lack 12.9 million health care workers by 2035. Hence, it is important to develop wearable health care systems to perform self-health monitoring. In general, wearable health care systems demands low power consumption and high measurement accuracy. Smart technologies including green electronics, green radios, fuzzy neural approaches, and intelligent signal processing techniques play important roles for the developments of the wearable health care systems. This Special Issue aims at providing a forum to discuss the recent advances on 5G and beyond mobile technologies and applications for IoT.

Multi-Access Edge Computing (MEC) is an emerging technology that leverages computing, storage and network resources deployed at the proximity of users to offload terminal from computational- and delay-sensitive tasks. Various existing facilities including mobile devices with idle resources, vehicles, and MEC servers deployed at base stations or road side units, could act as edges in the network. Since offloading tasks incurs extra transmission energy consumption and transmission latency, two key questions to be addressed in MEC deployments are: (i) offload the workload to the edge or compute it in terminals? (ii) which edge, among the available ones, should the task be offloaded to? Hence, we propose a matching theory based task assignment mechanism which takes into account the devices' and MEC servers' computation capabilities, wireless channel conditions, and delay constraints. The main goal of our task assignment mechanism is to reduce overall energy consumption, while satisfying task owners' heterogeneous delay requirements and supporting good scalability. Simulations are conducted to evaluate the efficiency of our proposed mechanism.

Wireless networks have become in the last years a key enabling technology for cloud-enabled robots. Among those, the usage of WiFi is a first choice due to its almost ubiquitous use nowadays. However, WiFi suffers from crucial issues like spectrum interference, connectivity losses, long delay for client association and high latency handover. This work proposes a novel architectural split of the WiFi functionalities based on an enhanced software-defined wireless architecture. Cloud-enabled robots scenarios are addressed to derive results showing that the proposed architecture allows uninterrupted communication during handovers, and a quicker failover management.

This paper focuses on analyzing some key business aspects that arise during the deployment of key novel enabling technologies for 5G systems. Results are taken out of two EU-funded ongoing research projects, namely 5G-MiEdge and Superfluidity, which largely exploit mmWave communications and softwarization concepts for 5G networks. We initially provide a stakeholder analysis of the 5G ecosystem, as well as a Strengths Weaknesses Opportunities Threats (SWOT) analysis of a couple of key and most promising 5G use cases. For one use case also a preliminary business model is provided. Then we detail an economic 5G cost model, which is able to provide indications on the profitability of 5G networks. Finally, we highlight the planned future works.

This paper proposes a new system architecture for Licensed Shared Access (LSA) wireless networks, as well as novel band management techniques for fair and ranking-based spectrum allocation. The proposed architecture builds upon recently standardized and regulatory-accepted LSA systems and stems from the work done in the EU-funded project ADEL. Two new resource allocation algorithms are introduced and their behaviour is validated via system-level simulations.

Millimeter-wave (mmWave) communication is envisioned to be a promising technology to cater for the continuous growth of wireless communication capacity demand. To compensate severe path loss for mmWave signals, mmWave system typically employs beamforming technique to achieve essential antenna gain. To provide services to multiple user equipment (UE), mmWave system needs to schedule UEs in both frequency and beam domain to achieve optimal target utility function. To this end, this paper proposes an efficient joint beam-frequency scheduling algorithm to achieve target scheduling metric while possessing reasonable complexity. As the results of the scheduling algorithm, multiple UEs are optimally multiplexed in both frequency and beam domains in terms of target scheduling metric.

mmMAGIC (Millimetre-Wave Based Mobile Radio Access Network for Fifth Generation Integrated Communications) is an EU funded 5G-PPP project, whose overall objective is to design and pre-develop a mobile radio access technology (RAT) operating in the 6–100 GHz range, capable of impacting standards and other relevant fora. The focus of the project is on extreme Mobile Broadband, which is expected to drive the 5G requirements for massive increase in capacity and data-rates. This paper elaborates on some 5G key research areas such as: identification of the most compelling use-cases and Key Performance Indicators (KPIs) for future 5G systems, advantages and challenges of millimeter-wave (mmWave) technologies, channel measurements and channel modeling, network architecture; and the design of a new mobile radio interface including multi-node and multi-antenna transceiver architecture.

This paper focuses on one of the key enabling technology that will compose future 5G network, the Direct‐LTE communication underlying a cellular infrastructure, also commonly known as Device‐to‐Device (D2D). Energy efficiency algorithms are proposed for the communication between D2D users and cellular users (CUs) and, following the Lagrangian duality theory, an optimal power and rate control solution is given for D2D users, while satisfying the interference limits related to CUs. Finally, the new algorithm is used to achieve proportional fairness between D2D users and CUs and to show with numerical results that the interference to CUs can be limited to always be under a predefined threshold.

The introduction of infrastructural heterogeneity is generally perceived as a convenient solution to extend the coverage and the capacity of current wireless networks. An accurate assessment of the advantages and costs of such introduction, however, depends on the availability of adequate simulative models to characterize the network control strategies that must support it. This translates in the need for models that can faithfully portray the coupling between the infrastructure and the environment within which this is deployed. The contribution focuses on the analysis of the requirements that a pedestrian mobility model must satisfy, in order to qualify as a convenient solution for simulative analyses of the type of techniques and networks under study, and shows how to modify a form of the random way-point mobility model, operating over space graphs, to fulfil such requirements.

Next-generation mobile networks will offer wireless coverage through heterogeneous radio accesses. The possibility of realistically simulating advanced mobility scenarios in such networks, with the users roaming across different radio technologies, depends on the availability of light models capable of predicting the users' mobility and the wireless coverage offered by the heterogeneous infrastructure. The article proposes an analysis of the criteria to identify proper wireless coverage models and shows how to use the uniform geometrical theory of diffraction and a ray-tracing approach to build up a low complexity model capable of fulfilling such criteria.