Traditional terrestrial computing facilities, i.e., Edge Computing (EC) and Cloud Computing (CC), have limited applicability for remote Internet of Things (IoT) applications due to connectivity challenges. These challenges can be addressed through a Space-Edge Cloud continuum that leverages computing facilities enabled by Low Earth Orbit (LEO) satellites and terrestrial cloud facilities. We aim to minimize the joint latency and energy cost for processing remote IoT data in this continuum by formulating a complex optimization problem. Specifically, our goal is to optimize the complete offloading process by jointly selecting optimal routes, computation nodes, and buffering policies. We develop a Multi-Agent Hierarchical Reinforcement Learning (MA-HRL) solution to solve this problem. The proposed solution is developed in a Python environment and compared with several benchmark methods, demonstrating significant performance gains in terms of reduced latency and energy demands.

With the rapid development of lunar exploration, devices deployed on the lunar surface will encounter emergency events including meteor impacts and lunar dust storms. Additionally, the extreme lunar environment characterized by intense radiation and the high latency of Earth-based cloud computing pose severe challenges to real-time and reliable task processing. Existing collaborative computing and fault-tolerant schemes are not fully efficient in dynamic radiation environments. To address these challenges, a collaborative computing architecture integrating lunar surface devices and lunar orbit satellites is proposed for lunar emergency tasks. Comprehensive network, radiation, communication and computing models are established to support this architecture. The problem is then formulated as a multi-objective optimization problem and solved by the Radiation-aware hiErarchical collAborative Fault-Tolerant Reinforcement Learning (REAFTRL) algorithm, which integrates radiation-aware, hierarchical decision-making, and fault-tolerant execution with feedback mechanisms. Simulations show the proposed collaborative computing scheme outperforms traditional fault-tolerant strategies in task completion time, completion rate, and error rate, providing a reliable solution for future lunar exploration.

Federated Split Learning (FSL) has emerged as a transformative paradigm that synergizes the parallel processing and scalability of Federated Learning (FL) with the computational efficiency and enhanced privacy of Split Learning (SL). This comprehensive survey provides a systematic exploration of FSL, beginning with a detailed taxonomy of Distributed Machine Learning (DML) paradigms, tracing the progression from foundational concepts to advanced frameworks such as Federated Split Transfer Learning (FSTL) and Generalized Federated Split Transfer Learning (GFSTL). It then delves into the core challenges inherent to FSL, including privacy and security risks, system and data heterogeneity, computational and system constraints, communication overhead, and model optimization complexities. The heart of the survey presents a detailed categorization and analysis of FSL's diverse applications across key domains, including the Internet of Things (IoT) and Edge Computing (EC), wireless networks, healthcare, vehicular networks, Large Language Models (LLMs), and Earth Observation (EO). To ground this research, the survey further discusses standardized evaluation methodologies and implementation frameworks, followed by a quantitative visualization of survey data and research trends. The work concludes by synthesizing critical research gaps and outlining promising future directions. By synthesizing insights from over 100 recent research articles and providing a critical analysis of evaluation methodologies, this survey offers an essential roadmap for researchers and practitioners developing scalable, efficient, and privacy-aware distributed intelligence for the 6G and AI era.

Satellite offloading is a critical issue in the Internet of Things (IoT) edge intelligence environment. In this work, we present a novel neurosymbolic framework for computation offloading decisions in satellite-enabled IoT edge intelligence scenarios. By combining the forecasting capabilities of time-series foundation models with the transparency of rule-based reasoning, our approach enables data-efficient and inherently explainable decision making under uncertainty. Specifically, we use TimeGPT to predict future throughput quality and satellite CPU load, which are then processed through a fuzzy logic controller to derive context-aware offloading decisions with transparent rationale. The results show effective forecast accuracy, high decision robustness, and improved explainability when compared to traditional reinforcement learning-based approaches that require task-specific training.

With the growing demand for multimedia services, Dynamic Adaptive Streaming over HTTP (DASH) has become a key solution for delivering high-quality video content. In this work, we consider an Edge-DASH scenario and formulate a joint optimization problem that involves four critical aspects: bitrate allocation, user-to-server assignment, caching, and bandwidth allocation. Due to the complexity of the joint problem, we decompose it into sub-problems and address them separately. To solve the resulting sub-problems, we employ deep reinforcement learning, specifically the Deep Deterministic Policy Gradient (DDPG) method, for three of them, and develop a heuristic solution for the fourth. Simulation results demonstrate that our approach enhances performance across multiple metrics, including improved video delivery, reduced buffer underflow and overflow, and more efficient caching, which collectively enable greater utilization of edge resources for streaming. Moreover, we evaluated inference latency across edge and cloud hardware, confirming sub- to few-millisecond performance suitable for real-time deployment. This showcases the benefits of combining learning-based and heuristic techniques to meet the growing demand for adaptive video streaming in edge computing environments.

Traditional Cloud-based multimedia transcoding approaches struggle with issues like latency and bandwidth limitations. In this work, we experiment with strategies that exploit Edge computing capabilities to get around such constraints. We focus on an on-demand multimedia transcoding service for real-time video streaming on the uplink, leveraging a state-of-the-art Edge Computing service orchestrator to handle its provisioning. We regard this as a use case for the orchestration platform, with the objective of enhancing the experience of the user as well as keeping important metrics in check, including resource utilization and load distribution over the Edge compute nodes. We perform evaluations on a physical testbed to assess the performance of the Edge-based system using real-world video transcoding workloads.

The Internet of Things (IoT) is one of the most important applications in a distributed edge-cloud continuum. In this study, we aim to solve the data offloading and buffering policy selection problem for the remote IoT devices to process the data over the space edge cloud continuum formed by the non-terrestrial Edge Computing (EC) facilities, deployed over Low Earth Orbit (LEO) satellites, and the terrestrial cloud infrastructure. In particular, we have developed a constrained optimization problem to minimize the joint latency and energy costs associated with the data processing operation over LEO satellite nodes and cloud facilities. The problem is then decomposed into a hierarchy of decision-making subproblems and addressed using a Hierarchical Reinforcement Learning (HRL) approach. The proposed HRL solution is then implemented within a Python-based simulator, and its performance is compared with several other benchmark solutions. Performance analysis indicates the gain in terms of latency, energy, and resource utilization with adaptive utilization of space computing resources based on the devices’ demands.

Dynamic Adaptive Streaming over HTTP (DASH) is a promising solution to enhance the Quality of Experience (QoE) of mobile video services. In this paper, we consider an Edge-DASH scenario where two problems of Bitrate Allocation (BrA) and user-to-server allocation (USA) have been jointly formulated. Then, we exploit Deep Reinforcement Learning (DRL) algorithm to solve the USA problem and select the streaming point for users, which can be streaming from the Edge, Macro layer or cloud, and deliver the users the most appropriate bitrate respecting the QoE by solving the BrA problem. In the simulation results, we have demonstrated that our Deep Deterministic Policy Gradient (DDPG) outperforms the traditional solution in terms of bitrate allocation.

Low Earth Orbit (LEO) satellite constellations have seen significant growth and functional enhancement in recent years, which integrates various capabilities like communication, navigation, and remote sensing. However, the heterogeneity of data collected by different satellites and the problems of efficient inter-satellite collaborative computation pose significant obstacles to realizing the potential of these constellations. Existing approaches struggle with data heterogeneity, varing image resolutions, and the need for efficient on-orbit model training. To address these challenges, we propose a novel decentralized PFL framework, namely,ANovel DecentraLized PersonAlized Federated Learning for HeterogeNeous LEO SatellIte CoNstEllation (ALANINE). ALANINE incorporates decentralized FL (DFL) for satellite image Super Resolution (SR), which enhances input data quality. Then it utilizes PFL to implement a personalized approach that accounts for unique characteristics of satellite data. In addition, the framework employs advanced model pruning to optimize model complexity and transmission efficiency. The framework enables efficient data acquisition and processing while improving the accuracy of PFL image processing models. Simulation results demonstrate that ALANINE exhibits superior performance in on-orbit training of SR and PFL image processing models compared to traditional centralized approaches. This novel method shows significant improvements in data acquisition efficiency, process accuracy, and model adaptability to local satellite conditions.

Distributed Machine Learning (DML) methods are expected to play a crucial role in the forthcoming 6G era, with the goal of enabling ubiquitous connected intelligence. Distributed intelligence enabled through distributed computing environments, 6G technology, and big data can be extremely supportive of achieving the goals of emerging intelligent IoT applications in the proximity of end users. With this in mind, we propose an advanced Federated Learning (FL) approach for efficiently enabling intelligent applications over latency-critical networks in Non-Terrestrial environments. In the proposed solution, the client and server nodes reduce idle time using a parallel processing approach with the help of a replica of the training model. Next, the proposed FL framework is tested in a Python environment to show its effectiveness with respect to the traditional FL approach.

With the proliferation of devices connected to the Internet of Things (IoT), the complexity of network management has increased. To intelligently manage large-scale networks, we propose a Knowledge-Defined Edge Computing Networks (KDECN) architecture. Edge Nodes (ENs) deployed in the KDECN architecture are responsible for collecting and preprocessing the relevant information uploaded by User Devices (UDs), and provide computation resources for UDs. Futhermore, since multiple UDs share system computation resources, one computing decision will affect the subsequent decision-making of other UDs. Thus, accurately predicting the demands for UD task requests is a key challenge to maximize long-term execution utility. To this end, we deploy the LSTM-based Task Request Demand Prediction (TRDP) method on the management plane of KDECN architecture to predict the task request quantity of UDs in each future time slot. In order to maximize long-term execution utility of the system, we propose a Deep Reinforcement Learning (DRL)-based Long-term Computation Offloading and computation Resource Allocation (L-CORA) algorithm. Specifically, the proposed L-CORA algorithm makes computing decisions based on the prediction of the offloading task quantity and the personalized demands of UDs to ensure the long-term quality of computing service. Extensive experiments with Shanghai real-world datasets to prove that the KDECN-based L-CORA algorithm effectively improves the average utility of the system.

With recent advancements in Internet of Things (IoT), Machine Learning (ML), and wireless communication networks, e.g., 6G, there has been an increasing interest in Intelligent Vehicular Networks (IVNs), where efficient traffic flow management is one of the most important requirements. In recent times, several new distributed ML methods have been introduced aiming at solving complex networking problems due to their added advantages in terms of learning efficiency and privacy over distributed wireless scenarios. With a focus on the upcoming 6G enabled Intelligent Transportation Systems, real-time traffic flow management is essential for providing the adequate Quality of Service to the end users. Distributed Learning methods can be crucial for solving the traffic management problem in highly dynamic vehicular systems. With this motivation, in this work, we have considered a distributed learning method, belonging to the class of the collaborative Federated Learning (FL) approaches, named Networked FL (NFL) for estimating the dynamic traffic flow over time and space. We have exploited the added advantage of NFL in terms of multi-task FL capabilities for predicting traffic patterns over different times and locations in a service area. The simulation results compared with the traditional centralized FL and independent area-based learning show improvements in terms of learning efficiency, accuracy, and the cost required.

Internet of Vehicles (IoV) is a recently introduced paradigm aiming at extending the Internet of Things (IoT) toward the vehicular scenario in order to cope with its specific requirements. Nowadays, there are several types of vehicles, with different characteristics, requested services, and delivered data types. In order to efficiently manage such heterogeneity, Edge Computing facilities are often deployed in the urban environment, usually co-located with the roadside units (RSUs), for creating what is referenced as vehicular edge computing (VEC). In this article, we consider a joint network selection and computation offloading optimization problem in multiservice VEC environments, aiming at minimizing the overall latency and the consumed energy in an IoV scenario. Two novel collaborative$Q$-learning-based approaches are proposed, where vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication paradigms are exploited, respectively. In the first approach, we define a collaborative$Q$-learning method in which, through V2I communications, several vehicles participate in the training process of a centralized$Q$-agent. In the second approach, by exploiting the V2V communications, each vehicle is made aware of the surrounding environment and the potential offloading neighbors, leading to better decisions in terms of network selection and offloading. In addition to the tabular method, an advanced deep learning-based approach is also used for the action value estimation, allowing to handle more complex vehicular scenarios. Simulation results show that the proposed approaches improve the network performance in terms of latency and consumed energy with respect to some benchmark solutions.

Supported by some of the major revolutionary technologies, such as Internet of Vehicles (IoVs), Edge Computing, and Machine Learning (ML), the traditional Vehicular Networks (VNs) are changing drastically and converging rapidly into one of the most complex, highly intelligent, and advanced networking systems, mostly known as Intelligent Transportation System (ITS). Recently, distributed ML techniques, such as Federated Learning (FL) have gained huge popularity mainly for their advantages in terms of intelligence sharing and privacy concerns. VNs are a natural contender for exploiting FL for solving challenging problems; however, their limited resources, dynamic nature, high speed, and reduced latency requirements often become the bottleneck. V2X communication technologies allow vehicular terminals (VTs) to share their valuable local environment parameters and become aware of their surroundings. Such information can be utilized to build a more sustainable and affordable FL platform for serving VTs. Gaining from recently introduced 3D architectures, integrating terrestrial and aerial edge computing layers, we present here a distributed FL platform able to distribute the FL process on a 3D fashion while reducing the overall communication cost for providing vehicular services. The framework is defined as a constrained optimization problem for reducing the overall FL process cost through a proper network selection between various nodes. We have modeled the FL network selection problem as a sequential decision-making process through a Markov Decision Process (MDP) with time-dependent state transition probabilities. A computation-efficient value iteration algorithm is adapted for solving the MDP. Comparison with various benchmark methods shows the overall improvement in terms of latency, energy, and FL performance.

Abstract The uncapacitated facility location problem (UFLP) is a popular NP‐hard optimization problem that has been traditionally applied to logistics and supply networks, where decisions are difficult to reverse. However, over the years, many new application domains have emerged, in which real‐time optimization is needed, such as Internet of Vehicles (IoV), virtual network functions placement, and network controller placement. IoV scenarios take into account the presence of multiple roadside units (RSUs) that should be frequently assigned to operating vehicles. To ensure the desired quality of service level, the allocation process needs to be carried out frequently and efficiently, as vehicles' demands change. In this dynamic environment, the mapping of vehicles to RSUs needs to be reoptimized periodically over time. Thus, this article proposes an agile optimization algorithm, which is tested using existing benchmark instances. The experiments show that it can efficiently generate high‐quality and real‐time results in dynamic IoV scenarios.

With the rapid advancement of intelligent transportation systems (ITS) and vehicular communications, vehicular edge computing (VEC) is emerging as a promising technology to support low-latency ITS applications and services. In this paper, we consider the computation offloading problem from mobile vehicles/users in a heterogeneous VEC scenario, and focus on the network- and base station selection problems, where different networks have different traffic loads. In a fast-varying vehicular environment, computation offloading experience of users is strongly affected by the latency due to the congestion at the edge computing servers co-located with the base stations. However, as a result of the non-stationary property of such an environment and also information shortage, predicting this congestion is an involved task. To address this challenge, we propose an on-line learning algorithm and an off-policy learning algorithm based on multi-armed bandit theory. To dynamically select the least congested network in a piece-wise stationary environment, these algorithms predict the latency that the offloaded tasks experience using the offloading history. In addition, to minimize the task loss due to the mobility of the vehicles, we develop a method for base station selection. Moreover, we propose a relaying mechanism for the selected network, which operates based on the sojourn time of the vehicles. Through intensive numerical analysis, we demonstrate that the proposed learning-based solutions adapt to the traffic changes of the network by selecting the least congested network, thereby reducing the latency of offloaded tasks. Moreover, we demonstrate that the proposed joint base station selection and the relaying mechanism minimize the task loss in a vehicular environment.

Fog Computing can facilitate the adoption of the Everything-as-a-Service paradigm in infrastructure segments that are located closer to the end user, or to the data source, compared to typical Cloud solutions. This enables combining the advantages of flexible service deployment models with the need to cope with the strict requirements–especially in terms of latency–of emerging applications in softwarized networks. Along comes the need to consider aspects of service orchestration specific to the Fog environment and its intrinsically dynamic nature. In this paper we propose an architecture for flexible Fog Computing service orchestration, with a particular focus on the awareness of service deployment models. We discuss the design choices and describe the components and operations of the proposed orchestration system. We then present a complete working implementation of such architecture, including insights on its ability to handle critical orchestration functions such as service discovery and resource monitoring. We also report on the experimental validation of the system and the performance evaluation on real-world equipment, proving the feasibility and the effectiveness of the approach on a dynamic Fog infrastructure. We complement the work by presenting the results of a combinatorial analysis, validated by simulation, of the service model-aware resource selection process. As a result of our investigation, we show that Fog services can be effectively deployed in a matter of a few seconds, or even in less than one second when suitable Fog nodes are available, taking advantage of the awareness of the available service models.

In a mobile edge computing (MEC) network, mobile devices, also called edge clients, offload their computations to multiple edge servers that provide additional computing resources. Since the edge servers are placed at the network edge, e.g., cell-phone towers, transmission delays between edge servers and edge clients are shorter compared to those of cloud computing. In addition, edge clients can offload their tasks to other nearby edge clients with available computing resources by exploiting the Fog Computing (FC) paradigm. A major challenge in MEC and FC networks is to assign the tasks from edge clients to edge servers, as well as to other edge clients, in such a way that their tasks are completed with minimum energy consumption and minimum processing delay. In this paper, we model task offloading in MEC as a constrained multi-objective optimization problem (CMOP) that minimizes both the energy consumption and task processing delay of the mobile devices. To solve the CMOP, we design an evolutionary algorithm that can efficiently find a representative sample of the best trade-offs between energy consumption and task processing delay, i.e., the Pareto-optimal front. Compared to existing approaches for task offloading in MEC, we see that our approach finds offloading decisions with lower energy consumption and task processing delay.

Computation sharing is one of the most promising services in fog computing allowing the Fog Nodes (FNs) to share among themselves data and tasks to be computed. In case of battery powered-FNs, energy consumption becomes an issue. Simultaneous Wireless Information and Power Transfer (SWIPT) is a recently introduced technology enabling data and power transfer through microwave links among different nodes. In this work, we have considered the presence of a battery powered FN able to simultaneously share data and harvest energy from a Fog Access Point (F-AP), supposed to be plugged to the electrical network. The aim of this work is to define two suitable bounds able to drive the offloading decision to be taken by the battery powered FN, based on the estimated packet generation time, with the aim of having a stable energy system. We have further studied the impact of bandwidth and packet size on the two bounds. Simulation results demonstrate the impact of SWIPT-based offloading decision algorithm on network in terms of latency and network lifetime.

The increasing number of devices and applications requesting external processing and storage facilities with reduced access latency has led to the introduction of edge computing solutions. Among others, Fog Computing can be considered as an edge computing solution enabling the edge devices to offer general-purpose processing and storage capabilities. Despite a huge research effort for proposing efficient Fog Computing solutions, their implementability is still under study. By resorting to the Cloud Computing service models, we propose here an Android-based proof-of-concept solution allowing to implement different Fog services in an edge scenario by using off-the-shelf end-user devices.

Edge Computing refers to a recently introduced approach aiming to bring the storage and computational capabilities of the cloud to the proximity of the edge devices. Edge Computing is one of the main techniques enabling Fog Computing and Networking. Among several application scenarios, the urban scenario seems one of the most attractive for exploiting edge computing approaches. However, in an urban scenario, mobility becomes a challenge to be addressed, affecting the edge computing. By gaining from the the presence of two types of devices, Fog Nodes (FNs) and Fog-Access Points (F-APs), the idea in this paper is that of exploiting Device to Device (D2D) communications between FNs for assisting computation offloading requests between FNs and F-APs by exchanging status information related to the F-APs. With this knowledge, this paper proposes a partial offloading approach where the optimal tasks amount to be offloaded is estimated for minimizing the outage probability due to the mobility of the devices. In order to reduce the outage probability we have further considered a relaying approach among F-APs. Moreover, the impact of the number of tasks that each F-AP can manage is shown in terms of task processing delay. Numerical results show that the proposed approaches allow to achieve performance closer to the lower bound, by reducing the outage probability and the task processing delay.

Fog Computing offers storage and computational capabilities to the edge devices by reducing the traffic at the fronthaul. A fog environment can be seen as composed by two main classes of devices, Fog Nodes (FNs) and Fog-Access Points (F-APs). At the same time, one of the major advances in 5G systems is decoupling the control and the data planes. With this in mind we are here proposing an optimization technique for a mobile environment where the Device to Device (D2D) communications between FNs act as a control plane for aiding the computational offloading traffic operating on the data plane composed by the FN - F-AP links. Interactions in the FNs layer are used for exchanging the information about the status of the F-AP to be exploited for offloading the computation. With this knowledge, we have considered the mobility of FNs and the F-APs' coverage areas to propose a partial offloading approach where the amount of tasks to be offloaded is estimated while the FNs are still within the coverage of their F-APs. Numerical results show that the proposed approaches allow to achieve performance closer to the ideal case, by reducing the data loss and the delay.

This paper is motivated by the concept that the successful, effective, and sustainable implementation of the smart city paradigm requires a close cooperation among researchers with different, complementary interests and, in most cases, a multidisciplinary approach. It first briefly discusses how such a multidisciplinary methodology, transversal to various disciplines such as architecture, computer science, civil engineering, electrical, electronic and telecommunication engineering, social science and behavioral science, etc., can be successfully employed for the development of suitable modeling tools and real solutions of such sociotechnical systems. Then, the paper presents some pilot projects accomplished by the authors within the framework of some major European Union (EU) and national research programs, also involving the Bologna municipality and some of the key players of the smart city industry. Each project, characterized by different and complementary approaches/modeling tools, is illustrated along with the relevant contextualization and the advancements with respect to the state of the art.

Fog computing is a fascinating paradigm which has drawn attention recently by bringing the cloud capabilities closer to the users. A fog computing infrastructure can be seen as composed by two layers: one including Fog Nodes (FNs) and another the Fog Access Points (F-APs). While FNs are usually battery operated, the F-APs are instead connected to the electrical networks having unlimited energy. Moreover, F-APs facilitate the computation of tasks due to their higher storage and computational capabilities compared to the FNs. Considering FN energy consumption and task processing delay, we propose a suboptimal partial offloading technique aiming at exploiting jointly both FNs and F-APs. The simulation results demonstrate how partial offloading has a profound impact on the network lifetime and reduces energy consumption and task processing delay by comparing the single and two layer architectures.

In this paper, we propose two novel schemes to solve the problem of finding a quasi-optimal number of coded packets to multicast to a set of independent wireless receivers suffering different channel conditions. In particular, we propose two network channel virtualization schemes that allow for representing the set of intended receivers in a multicast group to be virtualized as one receiver. Such approach allows for a transmission scheme not only adapted to per-receiver channel variation over time, but to the network- virtualized channel representing all receivers in the multicast group. The first scheme capitalizes on a maximum erasure criterion introduced via the creation of a virtual worst per receiver per slot reference channel of the network. The second scheme capitalizes on a maximum completion time criterion by the use of the worst performing receiver channel as a virtual reference to the network. We apply such schemes to a GEO satellite scenario. We demonstrate the benefits of the proposed schemes comparing them to a per-receiver point-to-point adaptive strategy.

The need for increased data rates has led towards the development of advanced broadband satellite systems able to achieve higher capacity for providing Internet and backbone services in remote areas. Aside of the more conventional geostationary orbit (GEO), Medium Earth Orbit (MEO) satellite constellations are envisaged due to their advantages in terms of shorter link path and low latency; MEO satellite constellations are currently operating at Ka-band and are already showing signs of spectrum saturation in high demand areas in their coverage. In search of additional spectrum, in this paper, we employ the Q-band by investigating the performance of a multi-satellite MEO constellation network in terms of system capacity considering sophisticated propagation and physical layer tools. In particular, within the proposed Q-band solution, a higher throughput can be achieved with respect to a corresponding Ka-band system, by reaching up to 100% gain in terms of system capacity in a clear sky conditions.

Wireless communication systems are predicted to appear massively in the future human life. In case of 5G communications, high expectations in terms of Quality of Service and reliability are foreseen. However, the development of future systems has to cope with a better exploitation of the available resources. As a matter of fact, system coexistence and frequency sharing are two of the most promising approaches to achieve spectrum efficiency. Cognitive Radio (CR) is a key enabler of these approaches, which are also arising in Satellite Communications (SatComs). In this paper, a carrier allocation based scheduling algorithm inspired by genetic algorithms (GA) is proposed by taking into account different environment conditions and service requirements of the users of the cognitive satellite system.

The evolutionary trends of the Information Society have lead to the definition of the so-called Smart City, an environment characterized by the interaction of several heterogeneous technologies aiming at providing unprecedented services and efficiency. The Smart City is one of the most challenging scenarios for the Internet of Things (IoT) applicability. Among several things composing a Smart City, the computing infrastructures are responsible for giving a distributed elaboration intelligence to the environment. This paper takes into consideration the presence of the Mobile Cloud Computing (MCC), where different types of Cloud Computing Infrastructures (CCIs) are available through different wireless connections. MCC can be exploited by Smart Mobile Devices (SMDs) for offloading applications towards powerful remote servers and other SMDs pooled together, shortening execution time and extending battery life. Aim of this paper is to propose a cluster based computation offloading technique able to work in a distributed environment, in order to better satisfy the Quality of Service (QoS) requirements of the SMD users, by minimizing a cost function taking into account a tradeoff between SMD's energy consumption and execution time.

A warm welcome to the first edition of the IEEE Workshop on Fifth Generation Wireless: From Bits to Packets (5GB2P), 2016.

The increasing request of communication capacity for the introduction of modern multimedia services has collided with the problem of the spectrum shortage. One of the most known approach is represented by cognitive radios, which are supposed to exploit already deployed frequency bands in use by an incumbent system. Among other cognitive radio features, detection and estimation of the incumbent user are essential for implementing the cognitive radio system avoiding any interference. This is particularly important in SatCom cognitive scenarios due to transmission power impairments. In this paper, we focus on a joint interference and noise estimation algorithm aiming at detecting and estimating incumbent interference, for allowing the coexistence of the two systems. The behavior of the algorithm is analytically derived, and numerical results obtained through computer simulations confirm the effectiveness of the proposed approach.

The aim of this letter is to address the statistical modeling of the spectrum sensing energy consumption in cognitive radio networks. A Poisson point process has been shown to yield tractable and accurate results for the modeling of the interference in cognitive radio networks. We adopt this homogeneous stochastic process to develop an unified framework for deriving the energy consumption of the spectrum sensing in clustered cognitive radio networks. Furthermore, we extend the framework to multi-hop networks. The letter demonstrates that the spectrum sensing energy can be modeled as a Gamma-truncated distribution, as a function of the number of secondary users, their spatial density, and the number of hops of the cognitive radio network.

The most recent trend in the Information and Communication Technology world is toward an ever growing demand of mobile heterogeneous services that imply the management of different quality of service requirements and priorities among different type of users. The long-term evolution (LTE)/LTE-advanced standards have been introduced aiming to cope with this challenge. In particular, the resource allocation problem in downlink needs to be carefully considered. Herein, a solution is proposed by resorting to a modified multidimensional multiple-choice knapsack problem modeling, leading to an efficient solution. The proposed algorithm is able to manage different traffic flows taking into account users priority, queues delay, and channel conditions achieving quasi-optimal performance results with a lower complexity. The numerical results show the effectiveness of the proposed solution with respect to other alternatives.

The increasing request of bandwidth for multimedia advanced services is one of the major issues of modern wireless systems. The spectrum shortage has been faced in several ways; among others the cognitive radio approach, aiming to exploit the unused spectrum resources already assigned to incumbent users, is maybe the most known. However, even if its application has been extensively proposed for wireless terrestrial communications, it remains a still unexplored area concerning Satellite Communications. The aim of this paper is to propose an Energy Detector based Radio Environment Mapping for the spectrum awareness functionality of a hybrid terrestrial/satellite scenario where the satellite components aim at exploiting the resources unused by terrestrial communications. The proposed approach allows to take advantage of cooperation between multiple sensing nodes evaluating spatial detection and false alarm probabilities besides their relationship with device detection and false alarm probabilities.

The Smart cities applications are gaining an increasing interest among administrations, citizens and technologists for their suitability in managing the everyday life. One of the major challenges is regarding the possibility of managing in an efficient way the presence of multiple applications in a Wireless Heterogeneous Network (HetNet) environment, alongside the presence of a Mobile Cloud Computing (MCC) infrastructure. In this context we propose a utility function model derived from the economic world aiming to measure the Quality of Service (QoS), in order to choose the best access point in a HetNet to offload part of an application on the MCC, aiming to save energy for the Smart Mobile Devices (SMDs) and to reduce computational time. We distinguish three different types of application, considering different offloading percentage of computation and analyzing how the cell association algorithm allows energy saving and shortens computation time. The results show that when the network is overloaded, the proposed utility function allows to respect the target values by achieving higher throughput values, and reducing the energy consumption and the computational time.

The modern approach in wireless multimedia communications is toward the adaptation of transmission parameters subject to internal and external requirements and constraints, represented by, e.g., the user QoS, the propagation environment, the traffic behavior, the external interferences. One of the most challenging issues that arises during the parameters adaptation is the temporal behavior due to the continuous change of the external context; in that sense it is important to focus the attention on the timing constraints that impact on the system capability of adapting the transmission parameters in a reliable way. The satellite communications (SatCom) are especially affected by the delay problems, due to the high propagation Round Trip Time (RTT). Herein we analyze the temporal behavior of the Land Mobile Satellite (LMS) environment, aiming to define a reliability index for different operational speeds and scenarios.

In the last years we are seeing the increasing presence of multimedia applications together with a wide variety of terminals, from the PCs to the notebooks, from the smartphones to the tablets. In order to exploit such advanced services, ubiquitous and broadband connections are required. While urban or suburban areas can be covered by using terrestrial wireless broadband networks, there are several rural or low populated areas with narrowband access. To this aim, satellite community has developed in the last years two communications standards specifically suited for mobile communications, i.e., DVB-SH and DVB-RCS NG. Among others they consider adaptive waveforms techniques for increasing the system reliability and throughput aiming to exploit the variable channel behavior. Adaptive coding and modulation (ACM) belongs to this family of algorithms, by adapting the modulation and coding scheme based on the channel behavior. In this paper we focus on a state adaptive algorithm suited for mobile applications.

The radio resource shortage is one of the most important issues to be taken into account when deploying modern wireless communication systems. A novel communication paradigm named cognitive radio, has been introduced in the last years for a more efficient exploitation of the limited available spectrum and cope with the inefficiency in the spectrum usage. Its main aim is to allow the co-existence of different wireless systems on the same spectral resources by limiting the mutual interference. The aim of this paper is to design a cognitive networking environment where the primary network is based on the OFDMA principle. The proposed Medium Access Control (MAC) technique for the secondary network, named Data Aided Cognitive Technique (DACT), aims to exploit the framing information broadcast by the primary network in order to setup transparently an independent network with a particular focus on Machine to Machine (M2M) communications.

In the last years the increasing presence of multimedia applications and devices is even requiring higher throughput links with a higher availability. In order to exploit such advanced services, ubiquitous and broadband connections are required. Even if much of populated areas are nowadays covered by terrestrial wireless broadband networks, there is an increasing request of satellite communications able to cover low density populated areas and to fill the coverage gaps. In the last years two novel communication standards specifically suited for mobile satellite communications, i.e., DVB-SH and DVB-RCS NG, have been introduced. Among others, they consider adaptive waveforms techniques for increasing the system reliability and throughput aiming to exploit the variable channel behavior. Adaptive coding and modulation (ACM) belongs to this family of algorithms, by adapting the modulation and coding scheme based on the channel behavior. In this paper we focus on a state adaptive algorithm suited for mobile applications by considering the effect of the mobile speed.

The radio resource shortage is one of the most important problems to be considered when deploying modern wireless telecommunication systems. A novel communication paradigm named cognitive radio, has been introduced in the last years for a more efficient exploitation of the limited available spectrum. Its main aim is to allow the co-existence of different wireless systems on the same spectral resources by limiting the mutual interference. In this paper we consider a cognitive approach environment where the primary system is an OFDMA network. In this paper a MAC protocol based on the Wireless Token Ring approach is proposed for a secondary network aiming to exploit the OFDMA framing structure of the primary network.

ABSTRACT The last years have been characterized by an increasing interest in the grid and cloud computing that allow the implementation of high performance computing structures in a distributed way by exploiting multiple processing resources. The presence of mobile terminals has extended the paradigm to the so called pervasive grid networks, where multiple heterogeneous devices are interconnected to form a distributed computing resource. In such a scenario, there is the need of efficient techniques for providing reliable wireless connections among network nodes. This paper deals with the proposal of a suitable resource management scheme relying on a routing algorithm able to perform jointly the resource discovery and task scheduling for implementing an efficient pervasive grid infrastructure in a wireless ad hoc scenario. The proposed solutions have been considered within two different parallelization processing schemes, and their effectiveness has been verified by resorting to computer simulations. Copyright © 2011 John Wiley & Sons, Ltd.

Long Term Evolution (LTE) is considered one of the main candidate to provide wireless broadband access to mobile users. Among main LTE characteristics, flexibility and efficiency can be guaranteed by resorting to suitable resource allocation schemes, in particular by adopting adaptive OFDM schemes. This paper proposes a novel solution to the sub-carrier allocation problem for the LTE downlink that takes into account the queues length, the QoS constraints and the channel conditions. Each user has different queues, one for each QoS class, and can transmit with a different data rate depending on the propagation conditions. The proposed algorithm defines a value of each possible sub-carrier assignment as a linear combination of all the inputs following a cross-layer approach. The problem is formulated as a Multidimensional Multiple-choice Knapsack Problem (MMKP) whose optimal solution is not feasible for our purposes due to the too long computing time required to find it. Hence, a novel efficient heuristic has been proposed to solve the problem. Results shows good performance of the proposed resource allocation scheme both in terms of throughput and delay while guarantees fairness among the users. Performance has been compared also with fixed allocation scheme and round robin.

The interest towards real-time computing has lead an even more interest in grid computing. While in the past the implementation of grid computing has been done on high performance computers, in the recent years there is an increasing interest in the pervasive grid scenarios, where multiple devices can be used for a distributed computing. The most challenging idea is to use mobile devices connected among them with wireless connections for setting up pervasive grid environments. In this context, it is a crucial problem the optimization of the routing algorithms among the processing nodes, in order to satisfy the performance requirements of a distributed computing. Aim of this paper is the design of specific routing algorithms for different pervasive grid applications with a particular attention to time sensitive scenarios.

During last years modern telecommunication systems have achieved important results in terms of reliability, coverage and data rate, allowing a high variety of applications services. Among others, TETRA (Terrestrial Trunked Radio) is one of the most important in the professional market; in particular, the DMO (Direct Mode Operation) mode allows half-duplex direct communications among users without the need of an infrastructure. However, TETRA-DMO cannot perform multi-hop communications, limiting both the coverage and the scalability of the whole network. The idea behind our proposal is an integration between TETRA-DMO and another more flexible wireless system (e.g., IEEE 802.11), where the TETRA-DMO is the access technology, thus allowing the use of standard TETRA-DMO terminals, while the IEEE 802.11 technology can be used as a flexible multi-hop meshed backbone technology for interconnecting remote TETRA-DMO areas.

In the last years an even more attention has been focused on emergency and crisis management systems. Both communication and computing aspects are of primary importance for a fast and reliable response in these particular scenarios. Differently from other approaches, in this paper we consider an integrated communication and computing solution, by proposing a joint approach, where a distributed computing platform and a heterogeneous meshed communication system interoperate in order to enhance the system reliability and readiness.

Abstract Wireless communications have achieved a great attention during the last decades due to the easy implementation, possibility of delivering multimedia services to rural communities, suitability for public safety and emergency communications. In particular, a wireless network designed for an emergency scenario seems to be the best solution to manage all the phases of an emergency situation, from the prevention to the post‐emergency management. This paper discusses the main features of a such wireless network aiming to interconnect several heterogeneous systems and providing multimedia access to groups of people involved in emergency operations as foreseen by the In.Sy.Eme. (Integrated System for Emergency) project. In particular, the focus here will be on a communication infrastructure able to efficiently support a pervasive high performance distributed elaboration structure based on the Next Generation Grid principle. Main scope of the proposed system is that of allowing functional integration of new technologies with actual or off‐the‐shelf technologies to provide fast responses to any emergency situations and efficient use of all available resources. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract In the last few years the wireless metropolitan area networks (WMANs) have increased their popularity and attracted the interest of important research groups all over the world; as a consequence, several standards have been proposed. Among them, the IEEE 802.16 (WiMAX) is one of the most promising standard to carry out a full‐service broadband wireless network in an urban and suburban area. This standard provides high data rate within a wide coverage area with low implementation costs, possibility of multi‐traffic communications, and different network topologies. This paper deals with the analysis and performance comparison of different scheduling techniques for WiMAX networks for allowing quality of service (QoS) differentiation when different types of applications have to be supported and achieving a fair distribution of resource among users. In particular, the focus here is on the resource allocation problem for the case of mobile stations (MSs) active in an urban environment. The proposed scheduling algorithms exploit the orthogonal frequency division multiple access (OFDMA) scheme with adaptive modulation techniques in order to achieve a better network behavior. The performance of the proposed approaches will be derived here by means of theoretical analysis and computer simulations. Copyright © 2009 John Wiley & Sons, Ltd.

The lack of free frequency spectrum is becoming one of the most important problems in implementing novel wireless systems. The limited available spectrum and the inefficiency in the spectrum usage requires for a new communication paradigm to exploit the existing wireless spectrum. The key idea is based on the cognitive radio paradigm. Cognitive radio allows to different, independent networks to share a certain spectrum interval with low mutual interference. In the last years, one of the most important wireless system is the WiMAX, which is based on the OFDMA technique. The aim of this paper is to propose a cognitive networking environment where the primary network is constituted by an IEEE 802.16 network. Two approaches will be discussed for the secondary networks, by considering an ordinated technique and a collision resolution technique for both the network set-up and access control.

Wireless communications have achieved a great attention during the last decades due to the easy implementation, possibility of delivering multimedia services to rural communities, suitability for public safety and emergency communications. In particular, a wireless network designed for an emergency scenario has to be capable of managing all the phases of an emergency situation, from the prevention to the post-emergency management. This paper discusses the main features of a wireless network aiming to interconnect several heterogeneous systems and providing multimedia access to groups of people by introducing the main outcome of the In.Sy.Eme. (Integrated System for Emergency) project. Particular attention will be devoted to the approach of the project, aiming to consider a communication infrastructure strictly connected with an information processing side.

WiMax is one of the most important technologies for providing a broadband wireless access (BWA) in a metropolitan area. The use of OFDM transmissions has been proposed to reduce the effect of multipath fading in wireless communications. Moreover, multiple access is achieved by resorting to the OFDMA scheme. Adaptive subcarrier allocation techniques have been selected to exploit the multiuser diversity, leading to an improvement of performance by assigning subchannels to the users accordingly with their channel conditions. A method to allocate subcarriers is to assign almost an equal bandwidth to all users (fair allocation). However, it is well known that this method limits the bandwidth efficiency of the system. In order to lower this drawback, in this paper, two different adaptive subcarrier allocation algorithms are proposed and analyzed. Their aim is to share the network bandwidth among users on the basis of specific channel conditions without loosing bandwidth efficiency and fairness. Performance comparisons with the static and the fair allocation approaches are presented in terms of bit error rate and throughput to highlight the better behavior of the proposed schemes in particular when users have different distances from the BS.

The demand for high-speed services and multimedia applications anywhere and anytime has led to the rise of wireless communications. In particular, WiMAX technology is nowadays considered one of the most prominent solutions capable to provide a Broadband Wireless Access (BWA) in metropolitan areas with a simpler installation and lower cost than traditional wired alternatives. This paper deals with the proposal of efficient adaptive modulation and coding techniques to be used in WiMAX based wireless networks, that allow to improve network performance in the case of Non Line-of-Sight communications, which are typical in urban environments. Through these techniques it is possible to switch the modulation order and coding rate in order to better match the channel conditions, and, hence, obtaining better performance both in terms of error probability and data throughput.

Abstract In the last few years, the metropolitan area networks (MAN) have increased their popularity and attracted the interest of the most important research groups all over the world. Among several standards, IEEE 802.16 has taken a relevant role providing high data rate in a big covering range with low implementation costs and multi‐traffic communications. The IEEE 802.16 networks can have a pre‐defined structure, with a central base station (BS) covering a cell in which a variable number of subscriber stations (SSs) can work. This paper deals with the proposal of a quality of service (QoS) driven scheduling algorithm to be used in an IEEE 802.16 network where different traffic types coexist. In particular, the paper mainly focuses on best effort data and VoIP communications, by proposing a scheduling technique that allows an efficient resource management of both traffic types by considering their specific QoS flavor. The performance evaluation has been carried out by considering both the phases of contention and packet scheduling, by means of a theoretical approach and computer simulations. Numerical results show the performance of the proposed algorithm by focusing on a scenario where the BS schedules the best effort and VoIP traffics of several SSs. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract One of the most promising trends for next generation networks is to consider an integrated approach to the communication infrastructure and the processing layer. In particular, the introduction of broadband and reliable wireless networks allows the interaction of a huge number of devices all creating a single network. On the other hand, the grid paradigm is considered as one of the most promising approach for pervasive and dynamic applications. Aim of this paper is to present a novel integrated approach between grid paradigm and wireless networks by highlighting the main advantages of their cooperation. In particular, it will be shown here how a wireless heterogeneous network can be exploited for implementing a pervasive and dynamic grid (mobile grid) and, on the other hand, a mobile grid allows the optimization of the communication infrastructure. The integrated approach can be an effective method for solving applications, such as emergency management, where a huge amount of data derived from a wireless infrastructure needs to be processed efficiently and adaptively, and the traffic flow in the wide area wireless networks needs to be coordinated and optimized. Copyright © 2008 John Wiley & Sons, Ltd.

This paper deals with the problem of finding an optimal subcarrier allocation strategy for uplink and downlink communications in an OFDMA metropolitan wireless system. In particular a two steps approach is considered; at the first step the scheduler establishes the amount of resources to assign to users on the basis of their quality of service (QoS) constraints, while, at the second step, channel conditions are used to allocate subcarriers to users. A simple strategy for choosing the appropriate modulation and coding scheme according to the channel conditions of the assigned subcarriers is also proposed.

The demand for high-speed services and multimedia applications in wider wireless environments leads to the growth of wireless communications, due to the simplicity of installation and the reduction of costs with respect to traditional cabled links. In particular, WiMAX technology is considered one of the most prominent solutions capable to provide a Broadband Wireless Access in metropolitan areas. In this paper two schemes of adaptive modulation and coding are proposed with the aim of improving performances in Non Line-of-Sight communications, typical of urban environments. Through these techniques it is possible to switch the order of the modulation and the coding rate to better match the channel conditions, obtaining comforting results in terms of probability of error and throughput. The system has been modeled with a finite state structure in which every state consists in a possible scheme of transmission (i.e. a specific modulation and coding rate), and the switch among different states happens when multiple thresholds on channel attenuation are reached. The adaptation is realized at the physical level of the transmission for a WiMAX OFDMA structure. The two proposed techniques are suitable for different kind of traffic and, therefore, can be used for respecting different QoS requirements.

WiMAX is one of the most important technologies for providing a Broadband Wireless Access (BWA) in a metropolitan area. The use of OFDM transmission has been proposed to reduce the effect of multipath fading in wireless communications; moreover, multiple access is achieved by resorting to the use of OFDMA. Adaptive subcarrier allocation techniques have been selected to exploit the multiuser diversity, leading to an improvement of performances by assigning subchannels to the users accordingly with their channel responses. In this paper, three adaptive subcarrier allocation algorithms have been proposed; they are based on channel capacity and aim to assign an equal amount of capacity to all the users or to distribute the capacity proportionally to the users' channel conditions. The algorithms performances have been valued and compared in terms of bit error rate and throughput. The proposed strategies show a substantial improvement with respect to a static allocation, turning the multiuser diversity into a significant increase of data rate.

In the last years the wireless metropolitan area networks have increased their popularity and attracted the interest of the most important research groups all over the world. Several standards have been defined, in particular IEEE 802.16 has taken a relevant role in achieving a full-service broadband wireless network all over a urban and suburban area. This paper deals with the resource allocation problem by considering the resource request and data scheduling of the active stations. The considered scenario is foreseen in an urban environment where multiple stations can move around the Base Station. The proposed scheduling algorithms exploit the orthogonal frequency division multiple access scheme with adaptive modulation techniques for maximizing the network performance. The performance results have been obtained by resorting to theoretical analysis and computer simulations.

WiMAX is considered one of the most prominent technologies for providing a broadband wireless access (BWA) in a metropolitan area. Among several strategies, the adaptive modulation techniques has been selected for providing an efficient non line of sight (NLOS) coverage. Adaptive modulation allows the system to adapt the modulation scheme according to the channel conditions in order to enhance the system performance. This paper deals with the proposal of a state model to be used for the performance comparison of two different adaptation algorithms based on the maximization of different functional costs suitable for use in WiMAX system with an OFDMA physical structure. The algorithms' performance has been derived and compared in terms of error rate and throughput. The algorithms show a significant improvement of the system performance compared with the static case, i.e., no adaptive modulation; in particular each algorithm is suited for satisfying different QoS requirements.

WiMAX is considered one of the most prominent technologies for providing a Broadband Wireless Access (BWA) in a metropolitan area. Among several strategies, the adaptive modulation techniques has been selected for providing an efficient non line of sight (NLOS) coverage. Adaptive modulation allows the system to adapt the modulation scheme according to the channel conditions in order to enhance the system performance. This paper deals with the proposal of a state model to be used for the performance comparison of two different adaptation algorithms based on the maximization of different functional costs suitable for use in WiMAX system with an OFDMA physical structure. The performance of the algorithms has been derived and compared in terms of error rate and throughput. The algorithms show a significant improvement in the system performance compared with the static case, i.e., no adaptive modulation; in particular each algorithm is suitable for satisfying different QoS requirements.

In the last years the Metropolitan Area Networks (MAN) have increased their popularity and kept the interest of the most important research groups all over the world. Several standards have been published which represent the first step for developing metropolitan networks: IEEE 802.16 (WiMAX) has taken a relevant role in reaching the goal of realizing a full-service network all over a urban and suburban area. This standard provide high data rate in a big covering range with low implementation costs, multi-traffic communication and the possibility of creating broadcast, multicast and mesh networks. The WiMAX networks can have a pre-defined structure, with a central Base Station (BS) covering a cell in which a variable number of Subscriber Stations (SS) could work, or a mesh distribution with SSs communicating together without BS participation. In this paper a QoS management model is proposed for a centralized structure in which the BS schedules the Best-Effort and VoIP traffics of several SSs.

Among several wireless network scenarios, the in-home environment is one of the most challenging research areas in recent years. In particular, the Wireless Personal Area Networks (WPANs) seem to be one of the most interesting application scenarios, as it works in small area for delivering multimedia traffic. The IEEE 802.15.3 is the emerging standard for WPAN. This standard is designed to provide low complexity, low cost and low power-consumption for personal area networks that manage multimedia traffic, video and audio between different devices in a small area environment. This paper deals with an adaptive management technique at the Medium Access Control (MAC) layer technique for high data-rate WPANs. The proposed system aims to maximize the performance of the WPAN in terms of throughput, considering multimedia traffic, constituted by data and video traffic.

This letter deals with an advanced minimum mean-squared error receiver for applications to uplink transmissions in a multiuser code-division multiple-access system. The receiver is implemented by means of a suitable neural network in order to enhance the receiver convergence speed in the case of fast fading. Performance comparisons with classical approaches highlights a better behavior for the proposed scheme.

Recently, ad-hoc networks have obtained a growing interest due to their advantages in many practical applications. One of the most critical points is the definition of an efficient medium access control (MAC) protocol that allows the transmission of packets generated by a node and routing of packets arriving from other nodes. Moreover the growth of several applications with different requirements in terms of QoS has raised the problem to manage their priority also at the MAC layer. This paper deals with a traffic priority management technique at the MAC layer based on the code division multiple access (CDMA) scheme, where an adaptation of the used spreading factor to the network congestion is foreseen in order to minimize the energy consumption and maximize the network throughput

Precision agriculture (PA) represents a novel paradigm for managing agro-food production, by monitoring the physical parameters of different farming zones. This might be pursued by means of different communication infrastructures, but the most promising technology seems to be the wireless sensor network (WSN). However, merely applying this approach to the PA context, while resulting in a more flexible communication platform, still exhibits a rigid information management architecture. This limitation can be overcome by applying the ambient intelligent (AmI) paradigm to create an environment highly interactive with all the users involved in the process. Our proposal deals with the porting of the AmI concepts on a highly-integrated WSN platform with a special focus on the routing strategies. In particular, we investigate a class of dynamic flooding algorithm which is aware of the status of neighbor nodes in terms of geographical position and residual battery charge. By comparing this approach with the gossiping and the static flooding schemes, we are able to highlight a remarkable improvement in the network life-time with a particular regard to the most solicited nodes, without increasing complexity.

The paper proposes an adaptive admission control (AC) policy, that reduces call dropping probability, resorting to rate adaptation in accordance with the instantaneous connection configurations. We describe the philosophy of the proposed adaptive scheme and analyze the effective call dropping probability when considering a single cell UMTS scenario with an additive white Gaussian noise (AWGN) channel. Our simulations demonstrate that, by employing the proposed scheme, the network performance can be extremely increased while the quality of service (QoS) requested is respected.

Future wireless systems need higher support in terms of QoS support. In particular, when several connections with different QoS attributes are allowed, a prioritization system has to be foreseen. In UMTS and other systems that exploit the WCDMA approach, the problem of mitigating multiple access interference arises, too. Admission control algorithms have been introduced in the literature in order to prevent any congestion status in the network. An admission control algorithm that works by exploiting the current interference level is presented; moreover, each type of traffic is managed in order to respect its QoS in terms of priority.

Wireless local area networks represent one of the most interesting technological improvements in the last period. Many people are using WLAN commonly just to connect to the Internet or for e-mail downloading. WLAN do not offer at the moment a satisfactory QoS support for multimedia traffic. The aims of the IEEE 802.11e standard is to develop some schemes for supporting multimedia traffic in an in-home environment. On the other hand, in the last few years link adaptation has gained a lot of attention due to its improvement to the channel occupancy and capacity optimization. In this paper a link adaptation algorithm suitable for using in a WLAN, based on the IEEE 802.11e draft, with QoS support is proposed.

Recently, ad-hoc networks have obtained a growing interest due to their advantages in many practical applications. One of the most critical points is the definition of an efficient medium access control (MAC) protocol that allows the transmission of packets generated by a node and routing of packets arriving from other nodes. This paper deals with a MAC technique based on code division multiple access (CDMA) scheme that adapts the used spreading factor to the network congestion in order to minimize the energy consumption and maximize the network throughput.

In modern satellite communication systems, the quality of service (QoS) management has became a crucial topic due to the increasing interest in multimedia traffic. The actual trends consider the satellite networks as an integrated part of the terrestrial data networks. In IP networks, the differentiated service (DiffServ) approach seems to be the best that satisfies the QoS constraints, due to its end-to-end philosophy. Actual trend considers the satellite on-board switching capabilities for managing multibeam inputs and outputs. In particular this paper deals with the proposal of a new cellular neural network (CNN) for the on-board switching problem to reduce the computational complexity; several traffic classes, according to the DiffServ approach, have been considered and the switch takes into account their priority, queue length and time spent inside queues. Numerical results have shown that the performance is similar to the optimal switching solution of the flexible cellular neural network. Simulation results have been driven with a memoryless distribution and heavy-tailed distribution for several input buffer size and switch dimension.

In the last decade, code division multiple access (CDMA) has gained even more importance due to its capabilities of wider band occupancy without any time constraints. A lot of the recent implemented systems for wireless communications, as Universal Mobile Telecommunication System (UMTS) or IEEE 802.11b wireless local area network (WLAN), use the CDMA approach to allow the simultaneous access of multiple users. One of the main drawbacks of CDMA systems is the so called multiple access interference (MAI). In the literature, several multiuser receivers were developed. Among them, receivers that perform mean square error minimization are very attractive for their very simple implementation. On the other hand, neural networks have gained recently an increasing importance due to their capabilities in solving many engineering problems, involving minimization of errors or some other cost functionals. In this paper, an advanced MMSE receiver based on the use of neural networks is proposed, where at every bit time neural network achieves the optimum values for the coefficient set of receiving filter, thus minimizing the error rate.

Next generation wireless systems, as Universal Mobile Telecommunications System (UMTS), aim at revolutionizing the actual wireless communication paradigm offering real time multimedia services now available on fixed terminals whose quality of service (QoS) requirements could be satisfied by network resources adaptation to traffic conditions. Since radio interface is based on CDMA technology, this system suffers form mutual interference among active connections. Approximately, the communication quality decreases at the increasing of active users number. As a consequence, there exists an active user threshold below which an intolerable QoS degradation is produces, especially for those applications named in 3GPP standard as interactive or background. Therefore, proper admission control (AC) algorithm that mitigates mutual interference by keeping active users under a dynamic threshold, is a crucial topic in UMTS system optimization. This paper deals with an advanced AC scheme proposal that estimates the requesting connections signal to noise plus interference ratio (SINR) by means of accurate multiple access interference (MAI) analysis results. Basically, this algorithm verifies the possibility of a new call admission by valuating if an overall power configuration there exists, whose predicted SINR values satisfy each QoS constraints. Whenever a new connection is accepted, a further optimization is performed in order to allow a QoS higher than the requested value under a maximum radiated power constraint. This value is delivered to power control (PC) algorithm jointly with the associated SINR value. Numerical simulations, closely related to an UMTS system, underline a remarkable number of active users increase up to four times traditional AC policies, together with the maximization of QoS requirements.

This paper deals with the proposal and performance evaluation of a fixed microwave communication (FMC) system that shares the same bandwidth as that of an existing UMTS network. The main application of the FMC system is for wireless connections between remote base stations and a core network access point for an UMTS network implementation in a dense urban environment, where a wired connection could be very expensive for service providers. Besides, sharing of the same frequency band could be attractive for the actual cost and lack of frequency spectrum. The mutual interference effects between the FMC and the existing UMTS systems are investigated by focusing on typical application scenarios. The performance for the two systems under consideration has been evaluated in terms of bit error rate by means of computer simulations for proposed receiving scheme where mutual interference are, firstly, detected and then canceled from the other system. The obtained results between the proposed FMC system and an existing UMTS network and the improvement of performance in terms of bit error rate with proposed receiving system.

This paper deals with an advanced admission control (AC) scheme that estimates the requesting connections signal-to-noise plus interference ratio (SINR) by means of accurate multiple access interference (MAI) analysis results. This algorithm verifies the possibility of a new call admission by valuating if a power configuration exists, whose predicted SINR values satisfy each QoS constraints. Numerical simulations, closely related to the UMTS system, underline a remarkable number of active users increase up to four times traditional AC policies, together with the maximisation of QoS requirements.

In order to enhance the bandwidth utilization, new advanced receivers for next generation mobile communications are developed. Adaptive blind multiuser detection has been widely proposed for applications in CDMA (code division multiple access) wireless communication systems for its principal advantage of eliminating training sequence to set-up receiver filter coefficients. Main drawback of this technique is that it reaches the optimum behavior after a certain number of bit times, which precludes its use in typical time-varying environments. A new neural network approach is proposed in order to solve this drawback. In particular, this paper considers the use of a modified Kennedy-Chua (1988) neural network, based on the Hopfield (1984) model. Numerical results are given to demonstrate the effectiveness of the proposed approach in different time-varying application scenarios.