Hazard detection is an important problem for Intelligent Transportation Systems (ITS), although, developing and deployment of such systems is a complicated task, given human presence in the loop and a large account of non-controlled variables (e.g. traffic levels, weather conditions, etc). In addition, a distributed hazard detection system will require a communication network for a large number of sensors, making this network a crucial element of the system and a potential bottleneck for its performance. Therefore, a suitable platform is needed for the development and validation of vehicular applications that handle both the vehicular and network aspects of the system to seamlessly migrate an application to an urban environment. This paper addresses these gaps by designing a hardware-in-the-loop architecture for a realistic ITS hazard detection system. The platform relies on CARLA simulator to provide a safe, controlled, and repeatable environment to test the application, and a software-defined Open RAN network that allows studying the effect of communications nuisances on the service and the possibility to implement network aware services to enhance performance. In addition, a hazard detection service is devised using the described architecture. Our approach uses a combination of machine learning detections with Kalman (or particle) filters to aggregate data from multiple cameras. We assume detections are asynchronous and data transmission should be minimized to ensure scalability. A data association criterion based on Mahalonabis distance is proposed to automatically associate filters with trajectories. Our experiments provide insights into the throughput and latency of the network and their impact on the ITS service. The results show the service is robust to end-to-end latency, making comparisons among KFs, PFs and unscented KFs.

The growing complexity of Cyber-Physical Systems (CPS) in industrial and manufacturing environments calls for more sophisticated methods to represent heterogeneous assets and processes. In response, hierarchical Digital Twins (DTs)–virtual representations of physical, taxonomy-based processes–offer transparent, layered modeling of diverse data sources. This layered structure fuels renewed interest in intelligent engines capable of extracting meaningful insights and mapping them within the stratified DT ecosystem. While current Intelligent Digital Twin (I-DT) engines based on Deep Learning are computationally demanding, lightweight alternatives like Reservoir Computing (RC) offer efficient solutions with low training costs and fast inference for modeling causal dynamics. This inherent trade-off between performance and practicality underscores the limitations of evaluating I-DTs on accuracy alone. To address this gap, this work introduces a novel metric, Fidelity , designed to provide a comprehensive evaluation. Unlike traditional approaches, Fidelity also accounts for maintainability and deployability, especially in contexts involving time-varying and hierarchical data dynamics. Extensive experiments on two multimodal datasets demonstrate the competitiveness of our RC-based engine and highlight the value of introducing Fidelity for effectively profiling I-DTs. Specifically, our RC-based engine, identified as optimal through a higher Fidelity score, consumes an order of magnitude less energy and achieves up to 39 % higher accuracy (about 10 % increase on average) compared to both canonical and other RC-based alternatives.

The remarkable success of attention-based models in real-world applications has sparked a crucial question for Reservoir Computing (RC): Can its inherent computational efficiency compete with the high-performance, yet energy-intensive, novel deep learning architectures? Can Deep and modular RC neural networks address state-of-the-art challenges in Computer Vision and Natural Language Processing? In the attempt to consolidate RC capabilities towards more complex tasks, this paper delves into the exploration of a comprehensive RC’s offline-online cycle cost analysis. Our investigation highlights hyperparameters (HPs) optimization as a major bottleneck in RC deployment, particularly for those exploring RC capabilities and those who want to maintain user-level knowledge of the solution. To address this, we introduce an adaptive ϵ-Greedy based search exploration mechanism, significantly streamlining the off-line optimization process while maintaining high accuracy. Furthermore, we enhance existing RC frameworks to support online transfer learning and inference, enabling seamless, fast, and energy-efficient adaptation to real-world environments. By analyzing the impact of optimized HPs on performance, we aim to demonstrate the viability of RC as a powerful and efficient alternative for many practical applications, including those on devices with limited resources. Experimental results proved that our solution is able to reduce the time required for offline HPs optimization by 70%, enabling energy savings of up to 88%. Moreover, in the online scenario, it guarantees similar performance in terms of accuracy while reducing memory usage by 66%.

As the complexity of communication networks continues to increase, driven by a diverse array of devices, services and applications, the adoption of Machine Learning (ML) has seen a significant rise to address various challenges ranging from management to security. Regarding network security, the application of ML ranges from preventive measures to detection and remediation due to its ability to dynamically learn and adapt to evolving threat landscapes. However, ML requires a significant amount of resources, mainly due to the fact that ML operates on data, and the volumes of data are consistently rising. This review article explores the resource consumption aspect of ML techniques used for network security and provides a comprehensive review of the current state of research. Moreover, we propose a taxonomy that can be used to classify the methods through which the resource consumption can be reduced for different ML-based network security implementations. The focus of the study encompasses several key aspects related to resource consumption, including energy, computing, memory, latency, bandwidth, and human resources. These resources are critical in improving the efficiency and optimizing the reliability and sustainability of network security solutions. Furthermore, based on an extensive literature review, we summarize key points regarding optimizing resource consumption in ML-based network security solutions. Finally, the challenges and future research directions for resource-efficient, ML-based network security solutions are outlined to aid in the advancement of research in this area.

While substantial research has been devoted to optimizing model performance, convergence rates, and communication efficiency, the energy implications of federated learning (FL) within Artificial Intelligence of Things (AIoT) scenarios are often overlooked in the existing literature. This study examines the energy consumed during the FL process, focusing on three main energy-intensive processes: pre-processing, communication, and local learning, all contributing to the overall energy footprint. We rely on the observation that device/client selection is crucial for speeding up the convergence of model training in a distributed AIoT setting and propose two clustering-informed methods. These clustering solutions are designed to group AIoT devices with similar label distributions, resulting in clusters composed of nearly heterogeneous devices. Hence, our methods alleviate the heterogeneity often encountered in real-world distributed learning applications. Throughout extensive numerical experimentation, we demonstrate that our clustering strategies typically achieve high convergence rates while maintaining low energy consumption when compared to other recent approaches available in the literature.

The increasing demand for efficient resource allocation in mobile networks has catalyzed the exploration of innovative solutions that could enhance the task of real-time cellular traffic prediction. Under these circumstances, federated learning (FL) stands out as a distributed and privacy-preserving solution to foster collaboration among different sites, thus enabling responsive near-the-edge solutions. In this paper, we comprehensively study the potential benefits of FL in telecommunications through a case study on federated traffic forecasting using real-world data from base stations (BSs) in Barcelona (Spain). Our study encompasses relevant aspects within the federated experience, including model aggregation techniques, outlier management, the impact of individual clients, personalized learning, and the integration of exogenous sources of data. The performed evaluation is based on both prediction accuracy and sustainability, thus showcasing the environmental impact of employed FL algorithms in various settings. The findings from our study highlight FL as a promising and robust solution for mobile traffic prediction, emphasizing its twin merits as a privacy-conscious and environmentally sustainable approach, while also demonstrating its capability to overcome data heterogeneity and ensure high-quality predictions, marking a significant stride towards its integration in mobile traffic management systems.

Federated learning (FL), thanks in part to the emergence of the edge computing paradigm, is expected to enable collaborative learning-based applications. However, its original dependence on a central server for orchestration raises several concerns in terms of security, privacy, and scalability. To solve some of these worries, blockchain technology is expected to bring decentralization, robustness, and enhanced trust to FL. The empowerment of FL through blockchain (widely known as FLchain), however, has some implications in terms of ledger inconsistencies that lead to forks and staleness, which are naturally inherited from the blockchain’s fully decentralized operation. Such issues stem from the fact that, given the temporary ledger versions in the blockchain, FL devices may use different models for training, and that, given the asynchronicity of the FL operation, stale local updates (computed using outdated models) may be generated. In this paper, we shed light on the implications of the FLchain setting and study how decentralization in blockchain affects the age of information (AoI) and FL accuracy. To that end, we provide a faithful simulation tool that allows capturing the decentralized and asynchronous nature of the FLchain operation.

Emerging vehicle-to-everything (V2X) systems call for a diverse set of novel mechanisms to address vulnerabilities and security breaches. In this context, misbehavior detection approaches aim to detect malicious behavior of rogue V2X entities and possible attacks that may originate from them. In this paper, we introduce a data-driven ensemble framework which jointly leverages clustering and reinforcement learning to detect misbehaviors in unlabeled vehicular data. A rigorous detection assessment using an open-source dataset reveals meaningful performance trends for various attacks. In particular, while the majority of attacks can be effectively detected, detection may be curtailed for certain misbehavior types due to partly inaccurate clustering and erratic activity of the attacker over time. Performance comparison against benchmark detectors reveals the robustness of our approach in the presence of potentially inconsistent or mislabeled training data. The real-time detection capabilities of our framework are also explored in an effort to evaluate its practical feasibility in mission-critical V2X scenarios.

Motivated by the heterogeneous nature of devices participating in large-scale federated learning (FL) optimization, we focus on an asynchronous server-less FL solution empowered by blockchain technology. In contrast to mostly adopted FL approaches, which assume synchronous operation, we advocate an asynchronous method whereby model aggregation is done as clients submit their local updates. The asynchronous setting fits well with the federated optimization idea in practical large-scale settings with heterogeneous clients. Thus, it potentially leads to higher efficiency in terms of communication overhead and idle periods. To evaluate the learning completion delay of BC-enabled FL, namely FLchain, we provide an analytical model based on batch service queue theory. Furthermore, we provide simulation results to assess the performance of both synchronous and asynchronous mechanisms. Important aspects involved in the BC-enabled FL optimization, such as the network size, link capacity, or user requirements, are put together and analyzed. As our results show, the synchronous setting leads to higher prediction accuracy than the asynchronous case. Nevertheless, asynchronous federated optimization provides much lower latency in many cases, thus becoming an appealing solution for FL when dealing with large datasets, tough timing constraints (e.g., near-real-time applications), or highly varying training data.

The massive deployment of small cells in 5G networks represents an alternative to meet the ever increasing mobile data traffic and to provide very-high throughout by bringing the users closer to the Base Stations (BSs). This large increase in the number of network elements demands a significant increase in the energy consumption and carbon footprint followed by complex interference management. In order to address these challenges, we consider multi-level Sleep Mode (SM) where BS components with similar activation/deactivation times can be put to sleep. The deeper and higher energy efficient the SM is, the longer it will take the BS to activate, which might impose degradation in the Quality of Service (QoS). While this adds operational flexibility to the BS, it brings complex management to the operator. In this paper, we consider a heterogeneous network architecture where small cells can switch to different SM levels to save energy and reduce dropping rate. We propose a reinforcement learning algorithm for small cells that adapts their activities subject to service delay constraint. In this regard, the algorithm intelligently learns from the environment based on the co-channel interference, the cell buffer size and the expected cell throughput in order to decide the best SM policy. Numerical values show that important energy savings can be obtained with an acceptable dropping rate. Moreover, we show that while offloading users to the macro cell can significantly reduce their delay, dropping rate and the cluster energy consumption, it comes at a cost of decreasing the network energy efficiency up to 5 times compared with the case of no offload.

In this paper we advocate the use of mobile networks as sensing platforms to monitor metropolitan areas. In particular, we are interested in detecting urban anomalies (e.g., crowd gathering) by processing the control information exchanged among the base stations and the mobile users. For this, we design an anomaly detection framework based on semi-supervised learning, which enables the automatic identification of different types of anomalous events without any a-priori information. The proposed approach uses unsupervised learning techniques to gain confidence in real mobile traffic demand patterns from the city of Madrid in Spain and build an ad-hoc ground truth. A recurrent neural network is then trained to detect contextual anomalies and identify different types of urban events. Simulation results confirm the better performance of the semi-supervised method compared to pure unsupervised anomaly detection frameworks.

The high penetration of mobile services provides an ample set of data generated by users and network elements. The analysis of such data yields insights on the behaviour of users and their experienced quality, and can be used by mobile operators to improve their mobile networks. In this paper, we design methods to infer user mobility patterns, estimate their channel quality and cluster them based on their Modulation and Coding Scheme (MCS) time evolution. In detail, we propose: i) a mapping between MCS and SNR, useful to assess the quality of the transmissions, ii) a method for deriving users' approximate velocities and categorising user mobility, and iii) a hierarchical clustering algorithm using Dynamic Time Warping, able to generate meaningful user clusters according to communication length and quality. We apply those proposed methods to real traces collected from more than one week of observations of three operative base stations in Spain. We observe that our solutions successfully provide relevant information about users' mobility and their channel quality, making them suitable for improvements in the understanding and planning of LTE resources by mobile network operators.

Multi-access Edge Computing represents a key enabling technology for emerging mobile networks. It offers intensive computational resources very close to the end-users, useful for task offloading purposes. Many scientific contributions already proposed approaches for optimally allocating these resources over time. However, most of them fail to take advantage of the prediction of both users’ mobility and service demands over a look-ahead temporal horizon. To bridge this gap, this paper formulates a novel methodology for anticipatorily allocating communication and computational resources at the network edge, based on the prediction of spatio-temporal dynamics of mobile users. The conceived architecture exploits a Software-Defined Networking approach to monitor users’ mobility, a Convolutional Long Short-Term Memory to predict over different look-ahead horizons the number of users within a given number of cells and their related service demands, and Dynamic Programming to optimally allocate users’ requests among available Multi-access Edge Computing servers. Computer simulations investigate the effectiveness of the proposed approach in a realistic autonomous driving use case and compare its behavior against a baseline solution. Obtained results demonstrate its unique ability to dynamically and fairly distribute users’ requests among the resources available at the network edge, while ensuring the targeted quality of service level.

The automatic classification of applications and services is an invaluable feature for new generation mobile networks. Here, we propose and validate algorithms to perform this task, atruntime, from theraw physical control channelof anoperative mobile network, without having to decode and/or decrypt the transmitted flows. Towards this, we decode Downlink Control Information (DCI) messages carried within the LTE Physical Downlink Control CHannel (PDCCH). DCI messages are sent by the radio cell in clear text and, in this article, are utilized to classify the applications and services executed at the connected mobile terminals. Two datasets are collected through a large measurement campaign: one labeled, used to train the classification algorithms, and one unlabeled, collected from four radio cells in the metropolitan area of Barcelona, in Spain. Among other approaches, our Convolutional Neural Network (CNN) classifier provides the highest classification accuracy of 98%. The CNN classifier is then augmented with the capability of rejecting sessions whose patterns do not conform to those learned during the training phase, and is subsequently utilized to attain a fine grained decomposition of the traffic for the four monitored radio cells, in anonlineandunsupervisedfashion.

To meet the growing quest for enhanced network capacity, mobile network operators (MNOs) are deploying dense infrastructures of small cells. This, in turn, increases the power consumption of mobile networks, thus impacting the environment. As a result, we have seen a recent trend of powering mobile networks with harvested ambient energy to achieve both environmental and cost benefits. In this paper, we consider a network of virtualized small cells (vSCs) powered by energy harvesters and equipped with rechargeable batteries, which can opportunistically offload baseband (BB) functions to a grid-connected edge server depending on their energy availability. We formulate the corresponding grid energy and traffic drop rate minimization problem, and propose a distributed deep reinforcement learning (DDRL) solution. Coordination among vSCs is enabled via the exchange of battery state information. The evaluation of the network performance in terms of grid energy consumption and traffic drop rate confirms that enabling coordination among the vSCs via knowledge exchange achieves a performance close to the optimal. Numerical results also confirm that the proposed DDRL solution provides higher network performance, better adaptation to the changing environment, and higher cost savings with respect to a tabular multi-agent reinforcement learning (MRL) solution used as a benchmark.

In this paper, we focus on the design of energy self-sustainable mobile networks by enabling intelligent energy management that allows the base stations to mostly operate off-grid by using renewable energy. We propose a centralized control algorithm based on Deep Reinforcement Learning. The single agent is able to learn how to efficiently balance the energy inflow and spending among base stations observing the environment and interacting with it. In particular, we provide a study on the performance achieved by this approach when considering different representations of the environment. Numerical results demonstrate that using a good level of abstraction in the choice of the representation variables may enable a proper mapping of the environment into actions to take, so as to maximize the numerical reward.

Recently, wake-up scheme has been proposed to enhance the energy-efficiency of 5G mobile devices and prolong its battery lifetime while reducing the buffering delay. The existing wake-up optimization mechanisms use off-line methods and are tied to specific traffic models. In this paper, a novel concept of wake-up scheduling is introduced to further improve the energy-efficiency of mobile devices and to deal with realistic traffic. The main idea is to use a fixed configuration of the wake-up scheme and adjust the scheduling of the wake-up signals dynamically. For this, a proactive wake-up scheduler is proposed to take online decisions based on traffic prediction. Towards this end, a framework to predict packet arrivals based on recurrent neural networks is developed. Numerical results show that for given delay requirements of video, audio streaming, and mixed traffic flow, the proactive wake-up scheduler reduces the power consumption of the baseline wake-up scheme without scheduler by up to 36%, 28% and 9%, respectively.

In this paper, we discuss a handover management scheme for Next Generation Self-Organized Networks. We propose to extract experience from full protocol stack data, to make smart handover decisions in a multi-cell scenario, where users move and are challenged by deep zones of an outage. Traditional handover schemes have the drawback of taking into account only the signal strength from the serving, and the target cell, before the handover. However, we believe that the expected Quality of Experience (QoE) resulting from the decision of target cell to handover to, should be the driving principle of the handover decision. In particular, we propose two models based on multi-layer many-to-one LSTM architecture, and a multi-layer LSTM AutoEncoder (AE) in conjunction with a MultiLayer Perceptron (MLP) neural network. We show that using experience extracted from data, we can improve the number of users finalizing the download by 18 %, and we can reduce the time to download, with respect to a standard event-based handover benchmark scheme. Moreover, for the sake of generalization, we test the LSTM Autoencoder in a different scenario, where it maintains its performance improvements with a slight degradation, compared to the original scenario.

Recently, the concept of wake-up radio based access has been considered as an effective power saving mechanism for 5G mobile devices. In this article, the average power consumption of a wake-up radio enabled mobile device is analyzed and modeled by using a semi-Markov process. Building on this, a delay-constrained optimization problem is then formulated, to maximize the device energy-efficiency under given latency requirements, allowing the optimal parameters of the wake-up scheme to be obtained in closed form. The provided numerical results show that, for a given delay requirement, the proposed solution is able to reduce the power consumption by up to 40% compared with an optimized discontinuous reception (DRX) based reference scheme.

Recently, discontinuous reception mechanisms (DRX) and wake-up schemes (WuS) have been proposed to enhance the energy efficiency of 5G mobile devices and prolong the battery lifetime. The existing DRX and WuS use commonly pre-configured parameters that cannot be adjusted dynamically. In this paper, a novel wake-up scheduling (WuSched) concept is introduced to further improve the energy efficiency of WuS-enabled mobile devices while controlling the buffering delay in a dynamic manner. The main idea of WuSched is to use a fixed configuration of the wake-up scheme and adjust the scheduling of the wake-up signals dynamically based on actual traffic arrivals. For this purpose, two different optimization approaches of the wake-up scheduling concept are proposed, analyzed, and compared, namely offline and online wake-up schedulers (WuSched-Offline and WuSched-Online). First, the WuSched-Offline is analyzed analytically for Poisson traffic arrivals and optimized (offline) to balance the average delay and power consumption. Second, the WuSched-Online is proposed to take online decisions based on traffic prediction, which is able to deal with general and more complex traffic models. Towards this end, we develop a framework for the prediction of packet arrivals based on recurrent neural networks. Numerical results show that both wake-up schedulers outperform the ordinary WuS-based system where wake-up scheduler is not deployed. In particular, for predefined delay requirements of video streaming, audio streaming, and mixed traffic flow, the WuSched-Online reduces the power consumption of the baseline WuS by up to 36%, 28% and 9%, respectively. Results also show that the WuSched-Offline has slightly better energy efficiency than the WuSched-Online in the case of Poisson packet arrivals, as it is optimized for that, while its power consumption is slightly higher than that of the WuSched-Online scheduler for realistic traffic scenarios.

The limitations of state-of-the-art cellular modems prevent achieving low-power and low-latency Machine Type Communications (MTC) based on current power saving mechanisms alone. Recently, the concept of wake-up scheme has been proposed to enhance battery lifetime of 5G devices, while reducing the buffering delay. The existing wake-up algorithms use static operational parameters that are determined by the radio access network at the start of the user's session. In this paper, the average power consumption of the wake-up enabled MTC TIE is modeled by using a semi-Markov process and then optimized through a delay-constrained optimization problem, by which the optimal wake-up cycle is obtained in closed form. Numerical results show that the proposed solution reduces the power consumption of an optimized Discontinuous Reception (DRX) scheme by up to 40% for a given delay requirement.

Detecting urban anomalies is of upmost importance for public order management, since they can pose serious risks to public safety if not timely handled. However, monitoring large metropolitan areas requires complex systems that can potentially lead to elevated costs. In this paper, we discuss the opportunity of exploiting the mobile network as a supplementary sensing platform for detecting urban anomalies. To favour the reliable and low latency anomaly recognition, we rely on a Multi-access Edge Computing (MEC) architecture, which enables a deep and detailed mobile traffic characterization almost in real-time and allows for a performance-responsive service, that is crucial in our problem. We focus on urban anomaly detection, by monitoring known events that gather a high concentration of people. The mobile network information is collected from LTE Physical Downlink Control Channel (PDCCH), which contains the radio scheduling information and has the benefit of being unencrypted and fine-grained, since the messages are exchanged every LTE subframe of 1 ms. To this purpose, we design an anomaly detection system based on Long Short-Term Memory (LSTM) Neural Networks, to deal with sequential and recurrent inputs. We demonstrate that a stacked LSTM architecture is able to identify traffic anomalies provoked by a rapid growth in the number of users, when a crowded event takes place nearby the monitored area. The numerical results show that the proposed algorithm reaches an F-score = 1 and overcomes the performance of other state-of -the-art benchmarks.

In this paper, we propose an unsupervised method to learn hidden features of the solar energy generation from a PV system that may give a more accurate characterization of the process. In a first step, solar radiation data is converted into instantaneous solar power through a detailed source model. Then, two different approaches, namely PCA and autoencoder, are used to extract meaningful features from the traces of the solar energy generation. We interpret the latent variables characterizing the solar energy generation process by analyzing the similarities of 67 cities in Europe, North-Africa and Middle-East through an agglomerative hierarchical clustering algorithm. This analysis provides also a comparison between the feature extraction capabilities of the PCA and the autoencoder.

Flexible functional split in Cloud Radio Access Network (CRAN) is a promising approach to overcome the capacity and latency challenges in the fronthaul. In such architecture, the baseband processing takes place partially at local base stations and the remaining processes are executed at the central cloud. On the other hand, we have seen a recent trend of powering base stations with ambient energy sources to achieve both environmental sustainability and profit advantages. As the base stations become smaller and deployed in densified manner, it is evident that baseband processing power consumption has a huge share in the total base station power consumption breakdown. Given that such base stations are powered by energy harvesting sources, energy availability conditions the decision on where to place each baseband function in the system. This work focuses on applying reinforcement learning techniques, in particular Q-learning and SARSA, for optimal placement of baseband functional split options in virtualized small cells that are solely powered by energy harvesting sources. In addition, a comparison of such online optimization solution with respect to offline performance bounds is provided.

Predictive analysis on mobile network traffic is becoming of fundamental importance for the next generation cellular network. Proactively knowing the user demands, allows the system for an optimal resource allocation. In this paper, we study the mobile traffic of an LTE base station and we design a system for the traffic prediction using Recurrent Neural Networks. The mobile traffic information is gathered from the Physical Downlink Control CHannel (PDCCH) of the LTE using the passive tool presented in [1]. Using this tool we are able to collect all the control information at 1 ms resolution from the base station. This information comprises the resource blocks, the transport block size and the modulation scheme assigned to each user connected to the eNodeB. The design of the prediction system includes long short term memory units. With respect to a Multilayer Perceptron Network, or other artificial neurons structures, recurrent networks are advantageous for problems with sequential data (e.g. language modeling) [2]. In our case, we state the problem as a supervised multivariate prediction of the mobile traffic, where the objective is to minimize the prediction error given the information extracted from the PDCCH. We evaluate the one-step prediction and the long-term prediction errors of the proposed methodology, considering different numbers for the duration of the observed values, which determines the memory length of the LSTM network and how much information must be stored for a precise traffic prediction.

Dense deployment of small base stations (SBSs) will play a crucial role in 5G cellular networks for satisfying the expected huge traffic demand. Dynamic ON/OFF switching of SBSs and the use of renewable energies have recently attracted increasing attention to limit the energy consumption of such a network. In this paper, we present a Layered Learning solution for the radio resource management of dense cellular networks with SBSs powered solely by renewable energy. In the first layer, reinforcement learning agents locally select switch ON/OFF policies of the SBSs according to the energy income and the traffic demand. The second layer relies on an Artificial Neural Network that estimates the network load conditions to implement a centralized controller enforcing local agent decisions. Simulation results prove that the proposed layered framework outperforms both a greedy and a completely distributed solution both in terms of throughput and energy efficiency.

Flexible functional split in Cloud Radio Access Network (CRAN) greatly overcomes fronthaul capacity and latency challenges. In such architecture, part of the baseband processing is done locally and the remaining is done remotely in the central cloud. On the other hand, Energy Harvesting (EH) technologies are increasingly adopted due to sustainability and economic advantages. Power consumption due to baseband processing has a huge share in the total power consumption breakdown of smaller base stations. Given that such base stations are powered by EH, in addition to QoS constraints, energy availability also conditions the decision on where to place each baseband function in the system. This work focuses on determining the performance bounds of an optimal placement of baseband functional split option in virtualized small cells that are solely powered by EH. The work applies Dynamic Programming (DP), in particular, Shortest Path search is used to determine the optimal functional split option considering traffic requirements and available energy budget.

In this paper, we propose an optimal direct load control of renewable powered small base stations based on Dynamic Programming. The optimization is represented using Graph Theory and the problem is stated as a Shortest Path problem. The proposed optimal algorithm is able to adapt to the varying conditions of renewable energy sources and traffic demands. We analyze the optimal ON/OFF policies considering different energy and traffic scenarios. Then, we evaluate network performance in terms of system drop rate and grid energy consumption. The obtained results are compared with a greedy approach. This study allows to elaborate on the behavior and performance bounds of the system and gives a guidance for approximated policy search methods.

The analysis of real mobile traffic traces is helpful to understand usage patterns of cellular networks. In particular, mobile data may be used for network optimization and management in terms of radio resources, network planning, energy saving, for instance. However, real network data from the operators is often difficult to be accessed, due to legal and privacy issues. In this paper, we overcome the lack of network information using a LTE sniffer capable of decoding the unencrypted LTE control channel and we present a temporal and spatial analysis of the recorded traces. Moreover, we present a methodology to derive a stochastic characterization for the daily variation of the LTE traffic. The proposed model is based on a discrete-time Markov chain and is compared with the real traces. Results show that, with a limited number of states, our model presents a high level of accuracy in terms of first and second order statistics.

Future dense mobile networks will imply much higher costs both in access and backhaul. This paper analyzes the effect on wireless mesh backhaul routing performance when energy saving policies are present at the radio access network (RAN). We consider an heterogeneous two-tier network where small cells (SC) with energy harvesting capabilities extend the capacity of the macro base stations (MBS), and can autonomously switch on-off in order to increase the energy efficiency of the network based on a Q-learning (QL) algorithm. Instead of calculating new routes for each SCs activation pattern, we propose to agnostically adapt to the RAN traffic demands using a non-route-based backpressure routing policy for the wireless mesh backhaul to even the network resource usage amongst SCs. We used the ns-3 simulator to integrate the different mobile network segments: RAN, wireless mesh backhaul, and evolved packet core (EPC). Simulation results show an achieved reduction of the %37% of the RAN energy consumption while satisfying traffic demands with an improvement of up to a factor of 10 of delay performance in the backhaul during peak hours.

In this paper we consider the use of inter-packet encoding at the source node to improve the delay performance of data block transmissions in a multipath communication scenario. First we propose a formal derivation to obtain the coding ratio and the per-path packet allocation that minimizes the data block delay. Then we validate this model through an experimental campaign carried out in a testbed in close-to-real conditions. The proposed encoding scheme is also compared with fixed multiplexing criteria previously proposed in the literature for multipath communications scenarios. Our evaluation shows the benefits of the inter-packet encoding specially when packets travelling through the paths experience unbalanced service rates and heavy tail delays.

Nowadays IEEE 802.11 WLANs are widely deployed; in spite of this, the issue of designing an efficient and practical Access Point selection schemes that can provide the best throughput performance in a variety of link conditions is still open. In this paper we present a Cognitive AP selection scheme that allows the mobile station to learn from its past experience how to select the best AP. In our proposal the mobile station collects measurements regarding the past link conditions and throughput performance, and a cognitive engine based on a Neural Network trained on this data drives the AP selection process. Our performance evaluation shows that the proposed scheme has very good performance in a variety of scenarios, as opposed to other algorithms previously proposed in the literature which perform well only in specific cases and cannot address the non-idealities typical under real conditions.

Call Admission Control (CAC) is recognized as one of the key strategies to achieve satisfactory QoS support for VoIP over IEEE 802.11 WLANs. However, most of the prior CAC solutions for VoIP over WLAN are centralized, and the few distributed CAC schemes proposed so far do not account for issues such as the loss of channel time due to medium contention and the coexistence of VoIP traffic with background traffic such as TCP data flows. In this paper, we describe a distributed CAC scheme which aims at addressing these issues by leveraging on channel monitoring techniques, thus being readily implementable in today's consumer devices. The proposed scheme is thoroughly evaluated by means of testbed experiments as well as network simulations in different scenarios, and it is shown to yield a better CAC performance with respect to prior solutions.

Nowadays, mobile users wish to use their multi-interface mobile devices to access the Internet through network points of attachment (PoA) based on heterogeneous wireless technologies. They also wish to seamlessly change the PoAs during their ongoing sessions to improve service quality and/or reduce monetary cost. If appropriately handled, multihomed mobile nodes offer a potential solution to this issue. In this paper, we present an improvement of SIP mobility (pre-call plus mid-call mobility) to support seamless mobility of multihomed mobile nodes in heterogeneous wireless networks. Pre-call mobility is extended to associate user identifier (i.e. SIP URI) and interface identifiers (i.e. IP addresses). The multiple addresses of a mobile device are weighted by the user to create a priority list in the SIP server so as to guarantee resilient reachability of mobile nodes and to avoid unnecessary signaling through wireless links, thus saving radio resources. Then, three variations of mid-call mobility, called hard, hybrid and soft procedures, are also proposed. Their main aim is to minimize, or even avoid, packet losses during interface switching at the mobile node. The proposed solutions have been implemented in a wireless heterogeneous testbed composed of 802.11 WLAN plus 3.5 cellular network, which are fully controlled and configurable. The testbed has been used to study the performance and the robustness of the three proposed mid-call mobility procedures.

Evolutionary computing in general and distributed online evolutionary computation in particular are hard problems in terms of monitoring, evaluation, generating functionality, and performance. We strive to complement current approaches and develop mechanisms which do not require the ex post effort of controlling the outcome of the computation. Instead, the goal of our research agenda foresees techniques which allow evolutionary and distributed computing to solve the problems above a priori. To support such an intrinsic system we make use of the powerful tool of algebra. Thus, this paper sheds some light on algebraic theories which allow the establishment of strong connections between biological concepts, automata theory, and the algebraic theories associated with them. We compile various contributions from different areas of research of the last few years discussing the algebraisation of biological systems and functions and their relation to automata theory and algebra. We highlight the role of category theory and abstract algebra and outline why these concepts are highly relevant for computational approaches inspired by biological mechanisms.

This paper presents a reconfigurable framework used to analyze the performance of different technologies in WLAN environments. This framework incorporates state-of-art experimentation tools into a single control paradigm that allows an easy reconfiguration and repetition of experimentation trials. The paper describes the main features of the testbed together with some of the technical solutions adopted. Also, an illustrative experimentation case is described, step-by-step, to show the performance of this tool. Specifically this experimentation case studies the performance of VoIP applications across different wireless scenarios. It reveals some patterns of the behaviour of this type of applications that would be difficult to observe in other more specific experimentation platforms.

One of the hot topics in the communication world is to realize a network able to offer a combination of services complying with different requirements in every place of the world, thus achieving the concept of "entertainment everywhere". In such a context a key role is played by aeronautical world that seems to be one of the last remaining islands in which broadband communications are not available. The present paper aims at studying resource management for a CDMA-based return link via satellite to provide passengers with new generation services. We focus on the connection admission control problem for QoS guarantees, analyzing in particular, the effective bandwidth concept over CDMA discussing its applicability to a multi-service packet switched network. Two admission control algorithms are then derived, based on the effective bandwidth concept for the circuit switched-like services and on a moving average window and linear predictor model for packet switched services. Finally, the performance over aeronautical broadband communication system is evaluated

Call admission control (CAC) plays a basic role in multimedia 3G systems, properly accommodating new connection requests while ensuring seamless service provision to the existing connections. CAC is classically based on signal to interference ratio estimates and threshold comparisons. A novel CAC strategy founded on fuzzy logic is proposed in this paper for WCDMA systems and, in particular, for UMTS. Starting from cell parameters like congestion state, available load and total interference a fuzzy set is defined. A fuzzy rule base is then constructed, giving the rules for the admission criterion. An UMTS radio access simulator, fully taking into account both user movement and channel conditions is employed to evaluate the performance of the new CAC algorithm and compare it to classic solutions. Simulation results show the improvements attained by the proposed solution.

In order to provide the growing demand of data services, several wireless standards are evolving to support packet data more efficiently. In particular 3rd generation partnership project (3GPP) is studying a new access technique called high speed downlink packet access (HSDPA) to support UMTS users with high speed data services. The present paper is focused on scheduling problems for video streaming services in the HSDPA. Two classical scheduling algorithms (weighted first queuing and earliest deadline first) are applied to this access technique and a new algorithm, based on an hybrid procedure between the above, is also designed. Furthermore two strategies for radio resource allocation are introduced: frame-by-frame assignment and fast scheduling, respectively. A software simulation using OPNET modeler is performed in order to compare and find out benefits and drawbacks in the application of each above mentioned strategy.