Distributed training of artificial intelligence models, such as Large Language Models (LLMs), generates highly structured and intense traffic patterns between GPUs, with synchronous and repetitive flows that can easily cause congestion and bottlenecks in data center networks. In this context, currently adopted protocols, such as RoCEv2, show significant limitations in the presence of bursty traffic and low entropy, compromising overall system efficiency. Segment Routing over IPv6 (SRv6) offers a programmable mechanism to steer AI workload traffic along explicitly chosen paths, enabling precise and congestion-aware routing under dynamic conditions. Lightweight monitoring modules can detect congestion conditions in real time and report them to the orchestrator or NICs, enabling dynamic rerouting decisions without requiring control-plane signaling or state in the fabric. SRv6 micro-segment (uSID) encoding allows the NIC to steer traffic along alternate, congestion-free paths simply by updating the IPv6 destination address, preserving RoCEv2 semantics while ensuring rapid adaptability. This work provides a practical implementation and experimental validation of the recent IETF Internet-Draft “SRv6 for Deterministic Path Placement in AI Backends”, demonstrating its feasibility and performance benefits in RoCEv2-based infrastructures. The results highlight the potential of SRv6 as a practical and vendor-agnostic solution to enhance networking efficiency in modern AI datacenters.

Accurate localization is a key enabler for advanced autonomous applications in UAVs and Internet of Things (IoT) networks, particularly in outdoor scenarios where GPS may be unavailable, unreliable, or insufficiently precise. Ultra-Wideband (UWB) technology offers high-accuracy ranging capabilities and has shown strong performance in indoor environments, but its deployment in large-scale outdoor networks remains underexplored. This work investigates the presence of range-dependent bias in long-distance UWB measurements and evaluates its impact on multilateration-based localization systems for UAVs. We present a field measurement campaign with specifically designed UWB testing boards over distances up to 1.2 km, revealing that all tested modules exhibit a growing positive bias, up to 20 cm at the maximum range. To assess how such bias affects positioning, we implement a simulation framework modeling a UWB localization network. We analyze the effect of varying anchor spacing and compare ideal measurements to biased ones using the measured trend. Our results show that geometric anchor deployment can mitigate some of the bias effects, and that awareness of systematic errors is crucial for the design of scalable, accurate outdoor localization networks.

Federated testbeds enable collaborative research by providing access to diverse resources, including computing power, storage, and specialized hardware like GPUs, programmable switches and smart Network Interface Cards (NICs). Efficiently sharing these resources across federated institutions is challenging, particularly when resources are scarce and costly. GPUs are crucial for AI and machine learning research, but their high demand and expense make efficient management essential. Similarly, advanced experimentation on programmable data plane requires very expensive programmable switches (e.g., based on P4) and smart NICs. This paper introduces SHARY (SHaring Any Resource made easY), a dynamic reservation system that simplifies resource booking and management in federated environments. We show that SHARY can be adopted for heterogenous resources, thanks to an adaptation layer tailored for the specific resource considered. Indeed, it can be integrated with FIGO (Federated Infrastructure for GPU Orchestration), which enhances GPU availability through a demand-driven sharing model. By enabling real-time resource sharing and a flexible booking system, FIGO improves access to GPUs, reduces costs, and accelerates research progress. SHARY can be also integrated with SUP4RNET platform to reserve the access of P4 switches.

Spectrum monitoring has become essential to ensure the reliability of wireless communications. Spectrograms provide a visual time-frequency representation of the radio signal, which analyzed with deep learning tools of the visual world, enables the identification of coexistence problems, interference and critical propagation conditions. In this paper, we propose a spectrum analysis system able to detect 5G control signals. Our methodology is based on the acquisition of 16 -bit IQ signals on extended time scales, transformed and analyzed as images. The paper discusses issues related to dataset creation for model training, as well as the possibility of using pre-trained models through transfer learning and few-shot learning to detect signals in real environments. To this end, a proof of concept of the proposed methodology was conducted, based on a dataset created from real-world acquisitions used to train a YOLOv8x model. The results show that the methodology is very promising, but highlight the need for significantly more examples to train the model.

Nasal polyposis and chronic nasal obstruction significantly impact patients' quality of life and require continuous clinical monitoring. Traditional follow-up methods, based on frequent in-person visits, are often inefficient and burdensome for both patients and healthcare systems. This study presents a telemonitoring solution enabling remote assessment of nasal airflow using a customized version of the Spirobank Smart device. Mechanical and software adaptations were implemented to measure Peak Nasal Inspiratory Flow (PNIF) and transmit data in real time to clinicians via a secure digital infrastructure. The system was validated on 30 healthy volunteers by comparing measurements with those from a standard PNIF Meter. Statistical analysis showed no significant difference between the two devices. The proposed solution offers a reliable, accessible, and costeffective tool for managing chronic nasal conditions remotely, with potential to improve long-term care and patient autonomy.

The monitoring of wildlife through advanced tracking technologies is crucial for ecological research, conservation efforts, and the study of animal behavior. However, most existing tracking devices are primarily designed for positional data collection, with limited integration of biologically relevant sensors. This work presents the design, development, and evaluation of a low-power, multisensor tracking device tailored for long-term wildlife monitoring. Our device integrates a comprehensive set of sensors and novel methodologies to acquire additional information about animals, including monitoring heart rate and respiration in free-ranging individuals. We optimize energy consumption through an advanced power management system, leveraging energy harvesting to ensure long-term operational sustainability. We validate the system with extensive laboratory tests on energy efficiency and data accuracy. This research paves the way for next-generation wildlife tracking devices capable of supporting more comprehensive studies in conservation biology and environmental monitoring.

Recent advances in network switch designs have enabled machine learning inference directly within the switch at line speed. However, hardware constraints limit switches capabilities of tracking stateful features essential for accurate inference, as the demand for these features grows rapidly with line rates. To address this, we propose DIDA, a distributed in-network machine learning approach. In DIDA, feature extraction occurs at the host, features are transmitted via in-band telemetry, and inference is performed on the switches. In this paper, we evaluate the effectiveness and efficiency of this architecture. We examine its impact on network bandwidth, CPU and memory usage at the host, and its robustness across different feature sets and deep neural network classifications.

With the rise of the Network Softwarization era, eBPF has become a hot technology for efficient packet processing on commodity hardware. However the development of custom eBPF solutions is a challenging process that requires highly qualified human resources. Indeed, in eBPF, it is difficult to devise truly modular applications since the development model does not favour the use of pre-compiled functions and libraries. In addition, for safety purposes, each eBFF program must pass a binary code verifier of the Linux kernel, which may increase the difficulty of the development process. To overcome such difficulties and enable a new development model, in this paper we propose the eCLAT framework with the goal to lower the learning curve of engineers by re-using eBPF code in a programmable way. eCLAT offers a high level programming abstraction to eBPF based network programmability, allowing a developer to create custom application logic with no need of understanding the complex details of regular eBPF programming. A developer can write eCLAT scripts in a python-like language to compose eBPF programs. To support such abstraction at the eBPF level, we created an eBPF framework called HIKe which brings code reuse and modularity in eBPF. The eCLAT/HIKe solution does not require any kernel modification. The new development model is tested through two concrete examples and compared with other proposed frameworks in the eBPF world.

With the rise of the Network Softwarization era, eBPF has become a hot technology for efficient packet processing on commodity hardware. However the development of custom eBPF solutions is a challenging process that requires highly qualified human resources. In this paper we propose the eCLAT framework with the goal to lower the learning curve of engineers by re-using eBPF code in a programmable way. eCLAT offers a high level programming abstraction to eBPF based network programmability, allowing a developer to create custom application logic in eBPF with no need of understanding the complex details of regular eBPF programming. To support such modularity at the eBPF level, we created an eBPF library that implements a virtual machine, called HIKe VM. The HIKe VM library extends the conventional eBPF programs so that they can be integrated in eCLAT. The eCLAT/HIKe solution does not require any kernel modification.

Besides providing data sharing, commercial cloud-based storage services (e.g., Dropbox) also enforce access control, i.e. permit users to decide who can access which data. In this paper we advocate the separation between the sharing of data and the access control function. We specifically promote an overlay approach which provides end-to-end encryption and empowers the end users with the possibility to enforce access control policies without involving the cloud provider itself. To this end, our proposal, named ABEBox, relies on the careful combination of i) attribute-based encryption for custom policy definition and management, with ii) proxy re-encryption to provide scalable re-keying and protection to key-scraping attacks, with a novel revocation procedure. Moreover, iii) we concretely embed our protection mechanisms inside a public domain virtual file system module to provide an overlay and trivial-to-use transparent service which can be deployed on top of any arbitrary cloud storage provider.

Segment Routing over IPv6 (SRv6 in short) is a networking architecture suitable for both IP backbones and datacenters. The research, standardization and implementation of this architecture are actively progressing and SRv6 is already adopted in a number of large scale deployments. Effective solutions for Performance Monitoring (PM) of SRv6 networks are strongly needed and there is a lot of activity in this area. A full blown Performance Monitoring solution needs to include: i) Data Plane (as needed to measure metrics such as packet loss and delay); ii) Control Plane (to send commands to the monitoring entities in the nodes); iii) Management Plane (e.g., to collect the measured metrics). Moreover, Big-Data tools and solutions can be applied inside or above the traditional Management Plane boundaries to store and analyze the collected data. In this article we describe SRv6-PM, a solution for Performance Monitoring of SRv6 networks that deals with all the aspects discussed above. SRv6-PM features a cloud-native architecture that supports: i) the ingestion, processing, storage and visualization of PM data using Big-Data tools; ii) the SDN-based control of network routers to drive the performance monitoring operations. In particular, we focus on Loss Monitoring and consider a solution capable of tracking single packet loss events operating in near-real time (e.g., with a time granularity in the order of 10-20 seconds). SRv6-PM is released as open source. We offer a re-usable and extensible platform that can be automatically deployed in different environments, from a single host to multiple servers on private/public clouds and includes a set of Big-Data tools and the SDN control plane. We also provide a reproducible Data Plane environment for PM experiments in SRv6 networks based on the Mininet emulator.

Segment Routing over IPv6 (SRv6 in short) is a networking solution for IP backbones and datacenters, which has been recently adopted in several of large scale network deployments. The SRv6 research, standardization and implementation activities are going on at a remarkable pace. In particular, a number of Internet Drafts have been submitted related to the Performance Monitoring (PM) of flows in an SRv6 network. In this paper we discuss the proposed PM approaches, considering both data plane and control plane aspects and focusing on loss monitoring. Then we describe the implementation of a per-flow packet loss measurement (PF-PLM) solution based on the “alternate marking” method. Our implementation is based on Linux kernel networking and it is open source. We describe a platform that can be used to validate the standardization proposals from a functional perspective and the implemented solution from the performance point of view. We analyze two different design choices for the implementation of PF-PLM and evaluate their impact on the maximum forwarding throughput of a software based (Linux) router.

Cryptography key management system plays a very central role in the cloud data security. Nonetheless, a great part of the current commercial solutions rely on cloud providers that hold both the encrypted data and the related private master key of their served customers in their secure key vaults, having a de-facto total control on their customer digital assets. Conversely, entrusting customer companies for key holding can be dangerous as witnessed by many cases of key loss or theft. In this work we present CoProtect, a novel architecture to protect the cryptography keys in cloud systems that leverage on the cooperation between the cloud provider and the customer company. With such trust model, we present the proposed data management strategy, the key generation and the crypto procedures, and a proof of concept.

A careful planning of sleeping activities is essential for guaranteeing long operational time to energy constrained IoT devices, given the extremely low energy consumption required during the sleeping mode. Neighbor discovery instead requires nodes to continuously listen to the radio channel to detect potential and unpredictable messages coming from the arrival of new nodes. These two contrasting needs raise a fundamental question: Should a node be active and listen to the channel or sleep and save energy? Hardware manufacturers introduced dedicated low-power circuitry to allow an energy efficient fast duty cycle (Wake-on Radio) or designed to wake up the main processor when special packets are received (Wake-up Radio). In this work, we propose a novel analytic model optimizing the number of discovered peers for a given energy budget. Our theoretical study demonstrates the existence of an optimum duty cycle in a wide range of operating environments. We designed an adaptive algorithm that dynamically tracks the optimum even in non-stationary conditions, only using local device estimations, therefore allowing nodes to optimize their own duty cycle. Nodes globally converge to a system-wide optimum of a minimal network discovery energy cost.

The expected dramatic growth of connected things raises the issue of how to efficiently organize them, in order to monitor and manage functions and interactions. Information centric networking (ICN) is a communication paradigm that provides content-oriented functionality in the network and at the network level, including content routing, caching, multicast, mobility, data-centric security, and a flexible namespace. Thus, it is a viable solution for supporting Internet of Things (IoT) services without requiring any centralized entity. In this paper, we introduce the lightweight named object solution: a convenient way to represent physical IoT objects in a derived name space, exploiting ICN. We show that this abstraction can: 1) increase the programming simplicity; 2) offer extended functionality, such as augmentation and upgrading, to cope with the “software erosion,” and 3) implement a common interaction logic involving mutual function invocation. We present some proof-of-concept implementations of the proposed abstraction dealing with challenging IoT test cases; we also carry out a performance evaluation in a simulated network scenario.

Many Internet of Things applications require a regular periodic sampling of physical quantities, such as light, CO2, or position. However, for energy harvesting devices, this can be in sharp contrast with the unreliable and time-varying amount of energy gathered opportunistically from the environment, and the severe energy storage limitations in constrained devices further exacerbate such issue. This article proposes an approach devised to jointly optimize frequency and stability of the sampling rate in energy harvesting sensors. Unlike heuristic approaches, StableSENS builds upon a solid theoretical foundation, namely, Lyapunov optimization, which-to the best of our knowledge-is applied here for the first time to the sensing scenario. One of StableSENS' main assets is its very broad applicability: we remark that neither assumptions nor predictions on future energy availability patterns are required. Numerical results obtained in realistic scenarios show that StableSENS yields superior performance with respect to previous heuristic approaches as well as reinforcement-learning-based approaches.

This paper explores methodologies, advantages and challenges related to the use of the Information Centric Network technology for developing NoSQL distributed databases, which are expected to play a central role in the forthcoming IoT and BigData era. ICN services make possible to simplify the development of the database software, improve performance, and provide data-level access control. We use our findings to design a NoSQL spatio-temporal database, named OpenGeoBase, and evaluate its performance by using a real data set relevant to Intelligent Transport System applications.

Neighbor discovery is a primary enabling ability for many emerging mobile applications. Due to its significant impact on the energy budget of battery equipped devices, energy preserving solutions have been investigated for a longtime, often introducing duty cycling. Ultimately, these solutions settle for trade-offs between energy and performance, leaving the final decision on how much energy to allocate to neighbor discovery in the hands of application developers or system engineers. In other words, someone must decide if an improvement in the quality of the discovery (e.g. better discovery latency) is worth an increase in energy consumption. In this paper, we devise a different approach in order to answer the following basic question: how many contacts can a smartphone discover using its battery energy budget? The answer clearly depends on the adopted discovery algorithm, on the mobility conditions, and on the stochastic characteristics of the encounters. However, we demonstrate that there is a natural optimum duty cycle that maximizes the number of discovered contacts in almost every real application scenario. This optimum is natural in the sense that it does not depend on any system level parameter or performance requirements. It depends uniquely on the stochastic characteristics of the meeting process between the nodes. We present an analytic analysis and devise a practical algorithm that dynamically adapts the duty cycle length to the time-varying context, without the need to make assumptions on (or predict) the distribution of the contact duration. The findings presented in the paper are validated against data coming from real human mobility traces and implemented on a mobile application.

Adjacent channel interference (ACI) between OFDM signals severely impacts system performance and spectrum efficiency. To eliminate ACI, Active Interference Cancelation (AIC) techniques using additional modulated OFDM sub-carriers can be adopted. This paper describes an AIC technique based on multi-step calculation of clipped canceling symbols. The algorithm has simple implementation and allows to easily control the extra power required for canceling symbols. To avoid bandwidth expansion, non-orthogonal canceling sub-carriers (E-AIC) can be easily incorporated in the procedure. The achievable reduction of the OFDM spectrum tail power has been evaluated analytically and by simulation.

Algorithms and protocols for opportunistic, delay tolerant and wireless ad-hoc networks are designed and validated by simulating the people interactions induced by the nodes mobility. There are cases in which we are interested in simulating just the first interaction between a pair of nodes, for instance to assess the performance of a discovery or epidemic routing protocol. In this cases nodes rapidly extinguish their utility hence it is not convenient to simulate these scenarios using a fixed amount of nodes. Thus we present a novel simulation methodology that introduces the "open environment" where nodes enter, can interact through meeting with other nodes and then exit, keeping the focus on the environment rather than on what happen before and after the nodes stay in the interesting area. The proposed approach uses the statistical distribution extracted from the real traces to reproduce directly the human interaction pattern without going through the traditional random way point approach. Meetings are simulated by a time-varying graph that holds the state of the interactions in the environment, while adapting to the statistics of single node to its history. We show that even in a simple scenario, the epidemic infection, Markov memory-less models have been fairly far from the interaction scenarios that the method reproduces.

With the rate of mobile traffic growth eventually outpacing 4G technology upgrades, it appears crucial to explore new means to offload cellular networks. Exploitation of device-to-device opportunistic short range connectivity for offloading purposes is an intriguing and widely investigated direction, but might ultimately play a negligible practical role: in front of the huge overall content universe a user may select to access, it is indeed unlikely that a specific user request is found in a possibly small number of neighboring mobile devices. Focusing on a concrete use-case scenario, namely Apps download from Google's Android market, we discuss a preliminary design and implementation of CarpeDroid, a local coordination approach devised to proactively augment the network response to a user lookup (in our use-case, coming from the App Market server) with download opportunities for content locally stored in neighboring devices and likely to fall within the users' interest. Our approach aims at acting as a simple recommending system, locally operated on the end user device, and not requiring any interaction with centralized servers. Efficiency and “better than nothing” privacy protection is achieved through the exchange of Bloom filters devised to summarize content opportunistically available in neighboring devices.

Thanks to its in-network drop-based adaptation capabilities, H.264 Scalable Video Coding is perceived as an effective approach for delivering video over networks characterized by sudden large bandwidth fluctuations, such as Wireless LANs. Performance may be boosted by the adoption of application-aware/cross-layer schedulers devised to intelligently drop video data units (NALUs), so that i) decoding dependencies are preserved, and ii) the quality perceived by the end users is maximized. In this paper, we provide a theoretical formulation of a QoE utility-optimal cross-layer scheduling problem for H.264 SVC downlink delivery over WLANs. We show that, because of the unique characteristics of the WLAN MAC operation, this problem significantly differs from related approaches proposed for scheduled wireless technologies, especially when the WLAN carries background traffic in the uplink direction. From these theoretical insights, we derive, design, implement and experimentally assess a simple practical scheduling algorithm, whose performance is very close to the optimal solution.

The Data Distribution Service (DDS) middleware is enjoying a rapid adoption in high-performance, mission-critical networks. At the same time, the H.264 Scalable Video Coding (SVC) has been recently standardized and it is deemed to be an effective solution for video streaming over a channel with time-varying bandwidth, like the wireless one. In these conditions, it is critical to adapt the video bit-rate to the actual wireless capacity, and bit-rate adaptation is extremely simple for a H.264 SVC video. In this paper we devise, evaluate and demonstrate a technique for streaming H.264 SVC video over a DDS middleware. The contribution is threefold: i) we design a structure of the DDS data-unit able to carry H.264 SVC video-units; ii) we devise a receiver-driven rate-control mechanism based on our DDS data-unit and exploiting specific DDS functionality; iii) we implement and show the effectiveness of our mechanism in an 802.11 wireless scenario, comparing our proposal with other solutions.

Abstract The WiMAX system carries a wide range of services in urban and rural environments supporting quality of service. A key element of the QoS framework is the scheduling algorithm adopted by the Base Station (BS). In this paper, we analyze the saturation throughput perceived by Mobile Stations in the cases of two BS scheduling algorithms: Deficit Round Robin (DRR) and time‐based DRR. We demonstrate that the WiFi issue of ‘Performance Anomaly’ also occurs in WiMAX networks: when the BS uses scheduling approaches aimed at achieving throughput‐fairness, like DRR. Performance Anomaly means that when some Mobile Stations (MSs) use a very low bit rate, the throughput of MSs with a high bit rate is dramatically degraded. We propose time‐based DRR as a viable solution to remove the Performance Anomaly. Time‐based DRR is a simple modification of the DRR algorithm that achieves time‐fairness. Its implementation is feasible in WiMAX. The analysis is carried out by means of analytical models supported by NS2 simulations. Two scenarios are considered: the first is suitably set up to highlight and understand the phenomenon of Performance Anomaly; the second examines the impact of Performance Anomaly on a system level focusing on a rural environment. Copyright © 2008 John Wiley & Sons, Ltd.

This paper describes the H.264 scalable video coding streaming evaluation framework (SVEF). This is the first open-source framework for experimental assessment of H.264 scalable video coding (SVC) delivery over real networks. Effectively adapting of the transport of an H.264 SVC stream to time-varying, bandwidth constrained, and loss prone networks is an important research area. However, very little experimental work has been performed due to the unavailability of real-time H.264 SVC players, the limitations of existing decoding software libraries when challenged with network-imparied received SVC streams (e.g., affected by random loss of Network Abstraction Layer Units - NALUs), and the lack of solutions for SVC streaming support. SVEF overcomes these issues by developing missing components and by integrating them in a hybrid online/offline experimental framework. We believe SVEF will be of significant help to the research community interested in experimentally benchmarking their own proposed SVC adaptation approaches and delivery mechanisms. As a proof-of-concept of SVEF, we provide the experimental performance evaluation of an SVC cross-layer in-network scheduler in a wireless LAN hot spot scenario.

Femto-cell can be considered as a home base station (BS) that can be installed to improve bandwidth availability and coverage for enabling new applications in the home. Actual 3G-based femto BSs are seen as an alternative to Wi-Fi hotspots and/or as a complementary technology. In the very near future 3G and 4G cellular networks will converge on OFDM/OFDMA wireless access interface. Thus the planning and the performance evaluation of OFDM/OFDMA cellular system including femto cells is of interest.

Planning procedures for multi-carrier wireless systems based on the SINR evaluated over one sub-carrier do not allow to account for multi-carrier transmissions features where coded bits in the same block are spread over different sub-carriers. Due to channel frequency selectivity, sub-carriers can be subjected to different propagation conditions. The decoder in the receiver can compensate for errors even when some sub-carriers are received below the SINR target level. This fact should be accounted for during cell planning so to avoid wrong dimensioning of network parameters. In order to extend the classical planning approach to the multi-carrier systems we adopt the effective exponential SINR parameter for planning. The proposed procedure is general and it is based on the simulated statistics of the effective SINR. For evaluation purposes the two cell interference scenario is considered and the variability of the reuse distance with respect to the number of transmitted sub-carriers per block is investigated.

OFDMA/OFDM multiple access/multiplexing techniques represent the radio interface technologies for the 4-th generation broadband cellular access networks such as the WiMAX operating the point-to-multipoint configuration. In this paper we consider the problem of the frequency reuse in cellular OFDMA/OFDM systems and in particular we calculate the reuse distance which is required to obtain a given link outage probability level. Analysis is carried out using a semi-analytical approach based on a general expression of the SINR accounting for multipath, frequency and time offsets between the reference terminal and the interfering devices. The proposed approach can be easily applied to evaluate performance under different operating modes of the OFDMA/OFDM cellular network e.g. synchronous BSs or not

This paper investigates the effectiveness of the application level multicasting, named overlay multicasting, with respect to the network layer one in mobile ad hoc networks (MANET). With respect to the network layer multicasting in overlay multicasting, only the mobile nodes participating in the multicast group have exploited the multicast routing at the application level while the other nodes of the MANET simply perform unicast IP routing. This constraint in the possibility of using multicast routing in all the nodes that leads to a loss of efficiency in the bandwidth usage are discussed in this paper. The parameter used for comparison is the multicast distribution tree cost, which is built by means of Steiner based algorithms. We measure this parameter by means of an exhaustive simulation campaign by analysing the performance dependence versus the different parameters: device coverage range, number of MANET nodes, multicast group size and mobility model.

Bluetooth is a low-cost, short-range wireless technology capable of providing many communication functionalities, ranging from wire replacement to simple personal area networking. In Bluetooth local networking applications, a critical issue still under study is the evaluation of the network performance when multiple piconets are simultaneously active in the same area, thus, causing mutual interference. We provide a closed-form expression for the packet loss probability in Bluetooth dense piconet area accounting for capture effects due to propagation losses. The effectiveness of the proposed approach is assessed by comparing the analytical results with those obtained from Monte Carlo simulations. By considering different scenarios, we show the dependence of results on the geometry and on the characteristics of the environment. It is observed that packet loss probability can significantly change with the position of the reference receiver in the area, as well as with the extension of the piconets area as compared with the coverage area of the receiver. We use the packet loss probability to evaluate the upper and the lower bounds of the network aggregate throughput and the average packet transmission time. Due to propagation effects, large variations of these parameters with the position of the reference receiver in the area are evidenced.

We provide a closed-form expression for the packet loss probability in Bluetooth networks accounting for capture effects due to propagation losses. The effectiveness of the proposed approach is assessed comparing the analytical results with those obtained from Monte Carlo simulations. By considering different scenarios, we show the dependence of results on the geometry and on the characteristics of the environment. It is observed that packet loss probability can significantly change with the position of the reference receiver in the area, as well as with the extension of the served area as compared with the coverage area of the receiver.

The severe constraints characterising satellite communications and the need to reduce the user terminal requirements impose restrictions on obtaining high on-board performance. If satellite systems aim at providing a large set of multimedia services to a large set of users, as standalone systems or as a real complement to terrestrial networks, considering the scarce bandwidth allocation, pursuing the improvement of payload characteristics we can get the target performance. To reach this goal the use of multibeam antennas for coverage patterns as similar as possible to terrestrial cellular systems is practically mandatory. The increased number of beams allows greater spatial selectivity helping in alleviating interference constraints and in implementing fading countermeasure techniques. The increase of the number of beams has to be traded off among the co-channel interference, complexity, costs and the maximisation of the spectral efficiency. In this paper the problem of interference in multibeam satellite systems is described and the impact of antenna design on interference is quantified for a LEO constellation. The results of simulations carried out using a simulator capable of performing dynamic analysis varying the number of beams are presented.