The design of responsive and green networks necessarily entails the optimisation of multiple conflicting objectives with strategic, tactical, or operational decisions. This paper addresses a bi-objective green p-hub centre routing problem with hub location-allocation decisions and vehicle routing decisions. In their respective routes, vehicles may only travel using one selected speed between each node pair. The objectives are the minimisation of the worst service time and the environmental costs incurred during the transportation of all necessary demand flows, respectively. Since the studied problem is NP-hard, a meta-heuristic approach based on the non-dominated sorting genetic algorithm-II meta-heuristic is proposed. Additionally, min-max location and sequential allocation-routing method is developed to generate initial solutions. Furthermore, problemspecific crossover and mutation operators are implemented to efficiently explore the search space. Whereas, a novel rankbased speed selection procedure is devised to determine the appropriate travel speeds for generated off-springs based on their relative ranks in current population. Computational experiments are performed on the Australian Post (AP) dataset, and results indicate that our proposed heuristic approach provides good solutions in competitive CPU times. Finally, a discussion on the obtained Pareto frontier approximations is offered, and analysis is conducted on the effects of key decision parameters such as the number of located hub nodes, and the number of vehicles available at open hubs.

This study investigates the influence of different driving behaviors, namely eco-driving and aggressive driving, on energy consumption and the accuracy of the prediction of state of charge (SOC) in electric vehicles (EV). This contribution presents a co-simulation framework that integrates a physics-based vehicle model, implemented in Simulink, with a machine learning-based SOC prediction algorithm. We applied the framework to a case study assessing how different driving styles impact SOC prediction accuracy, energy efficiency, and overall energy consumption across two standard driving cycles. The findings reveal that aggressive driving results in significantly higher energy consumption, lower efficiency, and higher SOC prediction errors compared to eco-driving. These results underscore the importance of developing more robust and adaptive SOC prediction models capable of handling aggressive driving patterns.

Distributed Denial of Service (DDoS) attacks pose a persistent threat to network security, requiring timely and scalable mitigation strategies. In this paper, we propose a novel collaborative architecture that integrates a P4-programmable data plane with an SDN control plane to enable real-time DDoS detection and response. At the core of our approach is a split early-exit neural network that performs partial inference in the data plane using a quantized Convolutional Neural Network (CNN), while deferring uncertain cases to a Gated Recurrent Unit (GRU) module in the control plane. This design enables high-speed classification at line rate with the ability to escalate more complex flows for deeper analysis. Experimental evaluation using real-world DDoS datasets demonstrates that our approach achieves high detection accuracy with significantly reduced inference latency and control plane overhead. These results highlight the potential of tightly coupled ML-P4-SDN systems for efficient, adaptive, and low-latency DDoS defense.

We delve into the issue of node classification within graphs, specifically reevaluating the concept of neighborhood aggregation, which is a fundamental component in graph neural networks (GNNs). Our analysis reveals conceptual flaws within certain benchmark GNN models when operating under the assumption of edge-independent node labels, a condition commonly observed in benchmark graphs employed for node classification. Approaching neighborhood aggregation from a statistical signal processing perspective, our investigation provides novel insights which may be used to design more efficient GNN models.

Deep learning (DL) techniques are increasingly used for diagnosing Parkinsons Disease (PD) due to their non-invasive nature and accessibility. This study introduces PD-VOST (Parkinsons Disease Voice Spectrogram Transformer), an innovative DL model that employs the Transformer architecture for PD diagnosis using voice data collected via smartphones. Unlike conventional transfer learning approaches that typically use image-pretrained models for audio tasks, PD-VOST leverages a model pre-trained specifically on audio data. This specialization allows for more effective fine-tuning on voice recordings from individuals with PD and healthy controls. Our model achieved an average AUC of 95.89% and an average AUPRC of 87.11%, outperforming state-of-the-art methods. These results underscore the potential of employing audio-specific pre-trained models in advancing the early detection and management of PD using the voice as a biomarker.

International audience

Mobile tourism applications have gained massive popularity, offering various services such as booking accommodations, navigating unfamiliar locations, and providing travel recommendations. These applications have revolutionized travel planning, making it easier and more convenient for tourists in smart cities. This work aims to enhance the tourist appeal of Morocco’s historical regions by developing a mobile application called E-Tourism, which leverages computer vision technology for historical site recognition in smart cities. The proposed application provides detailed descriptions and historical context for various heritage sites. Moreover, it utilizes a pre-trained lightweight neural network model to accurately recognize and provide information about historical landmarks, even when GPS fails to pinpoint users’ locations precisely. Users can scan landmarks with their device’s camera, and the application will display relevant information, enriching their visit with an educational experience.

The blockchain oracle problem is a central challenge in the integration of blockchain in decentralized systems. Ensuring that off-chain data fed into smart contracts is reliable is a problem, and relying on a single oracle introduces a single point of failure. To address this, several decentralized oracle designs have been proposed, including those based on threshold signature schemes. In such systems, a data feed is accepted only if a minimum number of oracles sign it. While this improves robustness, it introduces coordination issues: signing incurs a cost, and individual oracles may prefer to free-ride, expecting others to sign. In this work, we model oracle participation as a discrete public goods game and analyze the conditions under which signing is a rational strategy in equilibrium. We characterize the set of pure and symmetric mixed-strategy Nash equilibria and study how key system parameters, such as the number of oracles, the cost-to-reward ratio, and the signature threshold, affect participation incentives. Our results show that system parameters can give rise to multiple symmetric mixedstrategy equilibria, but that such equilibria disappear when the signing cost reaches as little as 27.5% of the reward.

Integrating blockchain technology into the Internet of Things (IoT) has revolutionized industries, enabling decentralized and reliable management of systems, while improving both efficiency and security. However, a key challenge for blockchain-based IoT solutions is ensuring the accuracy of data fed into the blockchain, known as the “blockchain oracle problem.” This work addresses this challenge by proposing the BIVO system (blockchain information verification oracles), a blockchain-based decentralized oracle for IoT networks. The system utilizes a reputation and voting mechanism suitable for both crowdsourced and semi-controlled environments. We also model the weighted voting mechanism as a stochastic game and conduct stress tests to analyze the system’s expected accuracy and cumulative payoffs under various conditions. Our findings indicate that the system achieves higher accuracy compared to nonweighted voting approaches. In semi-controlled environments, the system demonstrates resilience against up to 64% of adversarial nodes. However, under the worst conditions, malicious nodes need to control no more than 36% of the network to benefit from malicious behavior. Additionally, we implemented a prototype of the BIVO system and deployed it on both a local blockchain simulator and the public Ethereum testnet Sepolia to evaluate the cost and feasibility of blockchain integration.

Skill mismatch can be attributed to diverse factors, encompassing but not limited to outdated or misaligned curricula, swift technological advancements, alterations in economic structures, inadequate training programs, and a dearth of comprehensive information about the job market. Prior investigations predominantly employ survey-based methodologies to acquire data and derive insights. However, this approach often proves time and resource-intensive and encounters limitations related to respondents' knowledge constrained within their specific domains. Moreover, the insights generated are circumscribed to the surveyed area of study and lack generalizability. In this paper, we investigate skill mismatches in cybersecurity education and job market requirements, focusing on the alignment of university curricula with industry demands. We conduct a comprehensive analysis of job advertisements and university training programs to identify disparities in skills, particularly in programming languages, certifications, and soft skills. We use data science techniques, offering a more detailed and current view than traditional surveys. Key findings include mismatches in specific skills like C#, C++, Sql Server and certifications, absent in university training, which highlights the need for curriculum evolution to enhance graduate employability and recommends incorporating in-demand skills to meet the cybersecurity job market's dynamic demands.

Artificial Intelligence of Things (AIot) has made Stream Processing (SP) an essential element for data analytics. However, the dynamic nature of AIoT data streams incurs significant challenges in SP systems, especially when real-time data processing is required. While traditional approaches rely on resource scaling to meet the AIoT application requirements, our work explores a complementary approach by optimizing in real-time the configuration of SP systems to balance operational and resource efficiencies with data throughput and latency considerations. At the core of our work is the application of a Q-learning algorithm, enabling dynamic and responsive tuning of the SP system configuration parameters to adapt to the fluctuating demands of real-time data processing. Through empirical testing, our approach’s ability to improve SP efficiency in various data environments is demonstrated, providing insights into its practical application and effectiveness. The research concludes by highlighting the potential of extending this optimization approach to broader real-time data processing scenarios, suggesting avenues for future exploration in SP optimization.

The integration of Multi-Rotor Aerial Vehicles (MRAVs) into 5G and 6G networks enhances coverage, connectivity, and congestion management. This fosters communication-aware robotics, exploring the interplay between robotics and communications, but also makes the MRAVs susceptible to malicious attacks, such as jamming. One traditional approach to counter these attacks is the use of beamforming on the MRAVs to apply physical layer security techniques. In this paper, we explore pose optimization as an alternative approach to countering jamming attacks on MRAVs. This technique is intended for omnidirectional MRAVs, which are drones capable of independently controlling both their position and orientation, as opposed to the more common under-actuated MRAVs whose orientation cannot be controlled independently of their position. In this paper, we consider an omnidirectional MRAV serving as a Base Station (BS) for legitimate ground nodes, under attack by a malicious jammer. We optimize the MRAV pose (i.e., position and orientation) to maximize the minimum Signal-to-Interference-plus-Noise Ratio (SINR) over all legitimate nodes.

Bladder cancer ranks within the top 10 most diagnosed cancers worldwide and is among the most expensive cancers to treat due to the high recurrence rates which require lifetime follow-ups. The primary tool for diagnosis is cystoscopy, which heavily relies on doctors’ expertise and interpretation. Therefore, annually, numerous cases are either undiagnosed or misdiagnosed and treated as urinary infections. To address this, we suggest a deep learning approach for bladder cancer detection and segmentation which combines CNNs with a lightweight positional-encoding-free transformer and dual attention gates that fuse self and spatial attention for feature enhancement. The architecture suggested in this paper is efficient making it suitable for medical scenarios that require real time inference. Experiments have proven that this model addresses the critical need for a balance between computational efficiency and diagnostic accuracy in cystoscopic imaging as despite its small size it rivals large models in performance.

Accurate short-term traffic flow prediction is crucial for effective urban traffic management. However, selecting the most suitable prediction model and relevant features poses a significant challenge. Moreover, predicting traffic flow on a road using only historical data, adjacent roads, or all roads in the study area can compromise the model’s accuracy or execution time. This paper tackles this challenge by proposing an enhanced Vector Auto regression VAR-based prediction method. We suggest selecting relevant roads for the model using spatio-temporal correlation analysis. Subsequently, we conduct a comparative study between our methodology’s results and those obtained from other temporal and spatio-temporal traffic forecasting methods, including historical average, K-nearest neighbors (KNN), support vector machine for regression (SVR), and autoregressive model (AR). Model performance is evaluated by considering both the impact of normal and abnormal traffic conditions, as well as the selected training days: weekdays and weekends. The study utilizes a traffic dataset collected from an area of Xuancheng city in China. The proposed enhanced VAR outperforms the other methods for short-term forecasting horizons ($\approx$ from 5 to 25 minutes), under both normal and abnormal traffic conditions.

Accurate origin-destination (OD) matrices, which represent the travel patterns of individuals or groups between different locations, are vital for traffic planning and logistics. Traditionally, they’re determined through costly surveys, often resulting in outdated data. A newer method involves using traffic sensors, but mapping these counts to OD matrices is challenging, especially when sensor coverage is limited.To tackle this, we propose using traffic simulators like SUMO to create synthetic datasets. Machine learning models are then trained on this data to predict OD matrices from traffic counts. Different machine learning models have been investigated in this paper, we focus on Random Forest models, which consistently outperform existing methods like DFrouter, a SUMO tool for OD matrix estimation, and evaluated on the same test data using the root mean squared error (RMSE) and mean absolute error (MAE) metrics. The proposed Random Forest-based method is shown to consistently outperform DFrouter, achieving average errors three times lower than DFrouter.

Privacy and false fall detection pose a significant challenge within the current camera-based human activity monitoring research. In response, we propose a solution that leverages the inherent relationship between binary fall detection and activity classification through hierarchical learning for low false fall classification. Our solution involves employing edge computing for data preprocessing, with a specific focus on extracting key points of the human body and motion features. Subsequently, the classification process based on multi-stream hierarchical learning takes place at the cloud level. This approach prevents sending raw RGB videos to the cloud to improve data privacy. Moreover, our network incorporates the hierarchical relationship between binary and multi-class classification using stream-to-stream skip connections and multi-level feature fusion. We created our Multiscale Convolutional Fusion Block (MSCFB) for multi-level feature extraction and fusion with an inner block skip connection. Additionally, we added an auxiliary fall detection loss to the first stream to regulate and control the feature extraction process for fall detection. Our network attained state-of-the-art performance in activity classification on the UP-Fall dataset, securing an F1-Score of 97.27%. Notably, there were no misclassifications between regular activities and fall sub-classes, leading to a perfect F1-Score of 100% for binary fall detection on this dataset. Furthermore, our fall detection network achieved state-of-the-art on the PRECIS HAR dataset with an F1-Score of 98.34%.

The importance of ground mobile robots (MRs) and unmanned aerial vehicles (UAVs) within the research community, industry, and society is growing fast. Nowadays, many of these agents are equipped with communication systems that are, in some cases, essential to successfully achieve certain tasks. In this context, we have begun to witness the development of a new interdisciplinary research field at the intersection of robotics and communications. This research field has been boosted by the intention of integrating UAVs within the 5G and 6G communication networks and will undoubtedly lead to many important applications in the near future. Nevertheless, one of the main obstacles to the development of this research area is that most researchers address these problems by oversimplifying either the robotics or the communications aspects. Doing so impedes the ability to reach the full potential of this new interdisciplinary research area. In this tutorial, we present some of the modeling tools necessary to address problems involving both robotics and communication from an interdisciplinary perspective. As an illustrative example of such problems, we focus on the issue of communication-aware trajectory planning in this tutorial.

With the rise of deep learning (DL), our world braces for artificial intelligence (AI) in every edge device, creating an urgent need for edge-AI SoCs. This SoC hardware needs to support high throughput, reliable and secure AI processing at ultra-low power (ULP), with a very short time to market. With its strong legacy in edge solutions and open processing platforms, the EU is well-positioned to become a leader in this SoC market. However, this requires AI edge processing to become at least 100 times more energy-efficient, while offering sufficient flexibility and scalability to deal with AI as a fast-moving target. Since the design space of these complex SoCs is huge, advanced tooling is needed to make their design tractable. The CONVOLVE project (currently in Inital stage) addresses these roadblocks. It takes a holistic approach with innovations at all levels of the design hierarchy. Starting with an overview of SOTA DL processing support and our project methodology, this paper presents 8 important design choices largely impacting the energy efficiency and flexibility of DL hardware. Finding good solutions is key to making smart-edge computing a reality.

Recently, Supervised Contrastive Learning (SCL) has been shown to significantly outperform the well-known cross-entropy loss-based learning on most classification tasks. In SCL, a neural network is trained to optimize two objectives: pull an anchor and positive samples together in the embedding space, and push the anchor apart from the negatives. These two different objectives may be conflicting with one another, thus requiring a trade-off between them during optimization. In this work, we formulate the SCL problem as a Multi-Objective Optimization (MOO) problem for the fine-tuning phase of RoBERTa language model. Two methods are utilized to solve the optimization problem: (i) the linear scalarization (LS) method, which minimizes a weighted linear combination of per-task losses; and (ii) the Exact Pareto Optimal (EPO) method which finds the intersection of the Pareto front with a given preference vector. We evaluate our approach on several GLUE benchmark tasks, without using data augmentations, memory banks, or generating adversarial examples. The empirical results show that the proposed learning strategy significantly outperforms a strong competitive contrastive learning baseline.

With the increasing penetration of plug-in electric vehicles (PEV s), the availability of public PEV charging stations (PEVCS) becomes a major concern. Indeed, in a PEVCS with a limited number of chargers, the waiting time may be quite large, particularly during peak hours, given that each PEV requires a considerable amount of time to charge. This could lead to discontent with the service and a high customer attrition rate. In this paper, we propose a portable multi-port system (PMS), which is a patent-protected technology, that optimises the investment cost of the charging station while improving the accessibility of the chargers. The system allows multiple vehicles to be connected to the same charger and be automatically charged according to an optimized scheduling scheme. To ensure greater accessibility, a double layer laxity-based approach (DLBA) has been designed to assign the most appropriate charging spot for each vehicle according to their charging urgency. The proposed solution is shown to outperform the single layer laxity-based allocation method in terms of the required number of chargers meeting PEVs charging needs.

The e-mobility infrastructure faces several challenges that hinder the general adoption of electric vehicles (EV). Indeed, the management system requires multiple actors to jointly act on different interdependent processes which makes it complex, time-consuming and inefficient. Moreover, the current state of the infrastructure raises several security and privacy concerns that render it non-compliant with security regulations and privacy laws. In this work, we propose a blockchain-based solution that allows a more efficient, secure and privacy-preserving management of the EV infrastructure. We used hybrid smart contracts and blockchain oracles to feed data to the blockchain in a secure and trusted manner.

There is a need to improve fall detection systems to better discriminate falls from regular daily activities. Multi-sensor systems were proposed for this purpose. However, using wearable sensors may not be practical for elderly people. The use of surveillance cameras and advanced computer vision algorithms is thus an attractive solution. Indeed, raw RGB frames are rich in information including the human body shape and motions during activities. Yet, several disturbing features extracted from the raw frames may prevent distinguishing between normal activities and fall. To address this issue, we here leverage the human skeleton, and the binary history of motion during the activity. The former was analyzed with a Convolution Long Short Memory (ConvLSTM) to include spatial features during sequence learning. The latter was processed with a lightweight network for feature extraction. The outputs from both streams were merged to discriminate between fall and normal activities. Using the UP Fall dataset, our model detected falls with a single camera with a test accuracy of 100%, an improvement of 0.01% over current state-of-the-art methods.

Data breaches have begun to take on new dimensions and their prediction is becoming of great importance to organizations. Prior work has addressed this issue mainly from a technical perspective and neglected other interfering aspects such as the social media dimension. To fill this gap, we propose STRisk which is a predictive system where we expand the scope of the prediction task by bringing into play the social media dimension. We study over 3800 US organizations including both victim and non-victim organizations. For each organization, we design a profile composed of a variety of externally measured technical indicators and social factors. In addition, to account for unreported incidents, we consider the non-victim sample to be noisy and propose a noise correction approach to correct mislabeled organizations. We then build several machine learning models to predict whether an organization is exposed to experience a hacking breach. By exploiting both technical and social features, we achieve a Area Under Curve (AUC) score exceeding 98%, which is 12% higher than the AUC achieved using only technical features. Furthermore, our feature importance analysis reveals that open ports and expired certificates are the best technical predictors, while spreadability and agreeability are the best social predictors.

In this note we investigate the problem of Digital 2D state observers design, also software sensors, of the famous Navier Stokes equation. At a first step we provide an explicit state space representation deduced from a finite difference based discretization approach. After, a 2D software sensor algorithm is constructed under weak hypotheses. Sufficient conditions for asymptotic convergence are established in terms of linear matrix inequality (LMI).

Variations in breath rates can be used as an indicator for important changes in the patient's physiological status. Although the breath rate is an important vital sign, it is often overlooked due to the difficulty of monitoring it. With the rise of the Internet of Things (IoT), connected low-cost sensors can be used as an alternative to traditional breath rate monitoring methods. This paper presents a light-weight, yet efficient, breath rate estimation technique based on continuously monitoring the variations in nasal air temperature using a low-cost temperature sensor. Sophisticated sampling mechanisms are proposed to reduce energy consumption which is an important feature for low-resources IoT devices. Results show that the proposed technique is able to reliably and accurately monitor the breath rate while reducing the processed data volume by up to 94%.

Unmanned aerial vehicles (UAVs) have been proposed as a promising technology to collect data from IoT devices and relay it to the network. In this work, we are interested in scenarios where the data is updated periodically, and the collected updates are time-sensitive. In particular, the data updates may lose their value if they are not collected and analyzed timely. To maximize the data freshness, we optimize a new performance metric, namely the Age-of-Updates (AoU). Our objective is to carefully schedule the UAVs hovering positions and the users' association so that the AoU is minimized. Unlike existing works where the association parameters are considered as binary variables, we assume that devices send their updates according to a probability distribution. As a consequence, instead of optimizing a deterministic objective function, the objective function is replaced by an expectation over the probability distribution. The expected AoU is therefore optimized under quality of service and energy constraints. The original problem being non-convex, we propose an equivalent convex optimization that we solve using an interior-point method. Our simulation results show the performance of the proposed approach against a binary association.

Selfish mining is an attack on blockchain networks, where a minority mining pool deviates from the original mining protocol and keeps some blocks private. The goal of the attacking pool is to waste the computational power of the other miners and increase their revenue. In this paper, we use a new approach to analyze the profitability of such attacks. Using game theory, we model the interactions between pools to derive the utility of mining strategies. We simulate the game for a Bitcoin blockchain and analyze the profitability of an attack, in terms of the monetary award instead of the relative revenue. We express the utility to include the cost of a strategy and revisit existing selfish mining strategies to discuss possible outcomes of the game. Depending on the game parameterization, we highlight scenarios where the system could be compromised. To the best of our knowledge, this is the first work that models the selfish mining attack as a stochastic game.

Network reconnaissance is the first step preceding a cyber-attack. Hence, monitoring the probing activities is imperative to help security practitioners enhancing their awareness about Internet’s large-scale events or peculiar events targeting their network. In this paper, we present a framework for an improved and efficient monitoring of the probing activities targeting network telescopes. Particularly, we model the probing rates which are a good indicator for measuring the cyber-security risk targeting network services. The approach consists of first inferring groups of network ports sharing similar probing characteristics through a new affinity metric capturing both temporal and semantic similarities between ports. Then, sequences of probing rates targeting similar ports are used as inputs to stacked Long Short-Term Memory (LSTM) neural networks to predict probing rates 1 hour and 1 day in advance. Finally, we describe two monitoring indicators that use the prediction models to infer anomalous probing traffic and to raise early threat warnings. We show that LSTM networks can accurately predict probing rates, outperforming the non-stationary autoregressive model, and we demonstrate that the monitoring indicators are efficient in assessing the cyber-security risk related to vulnerability disclosure.

Sampling is a core process in IoT systems. It deter-mines the data volume circulating within the network as well as the energy consumption on the IoT devices. Adaptive sampling aims to control the volume of generated data to reduce energy and bandwidth consumption without undermining data quality. Within this context, we propose two new adaptive sampling techniques: a light-weight adaptive sampling algorithm and an optimized uniform sampling method. We tested our methods using various real data-sets and compared their performances against state-of-the-art adaptive sampling algorithms in terms of data quality and data volume. The results show that the proposed methods are consistently among the best with a noticeable reduction in computational load.

With the increasing use of wireless communication technologies, it is important to monitor electromagnetic exposure, ideally with high temporal and spatial resolutions. This paper presents our low-cost electro-smog measurement process, covering hardware selection, RF power measurement, and RF power correction. Then, a time series analysis is performed on the electromagnetic exposure data collected in the city of Sala Al Jadida - Morocco for seven days. The results show that the electro-smog exposure has a strong predictable pattern and a preliminary time series model is derived.

Air pollution continues to draw global attention, and still causes adverse environmental issues and health effects. Questions about how air pollution evolves spatially are also still unsolved. Many air quality monitoring stations are deployed in several countries to give insight about air quality. However, it is quite frequent for these stations to go out of order during their long life time. These incidents may lead to a significant loss of pollution data. To mitigate this issue, we propose to leverage spatial correlation between pollution monitoring stations to predict the lost data. To reduce the complexity of the prediction model, for each monitoring station, we identify the best set of other stations to use in the prediction model. Correlation-based station selection is shown to outperform distance-based station selection and provides an R2above 0.8 when applied to the Airparif datasets.

Understanding the properties exhibited by Spatial-temporal evolution of cyber attacks improve cyber threat intelligence. In addition, better understanding on threats patterns is a key feature for cyber threats prevention, detection, and management and for enhancing defenses. In this work, we study different aspects of emerging threats in the wild shared by 160,000 global participants form all industries. First, we perform an exploratory data analysis of the collected cyber threats. We investigate the most targeted countries, most common malwares and the distribution of attacks frequency by localisation. Second, we extract attacks’ spreading patterns at country level. We model these behaviors using transition graphs decorated with probabilities of switching from a country to another. Finally, we analyse the extent to which cyber threats have been affected by the COVID-19 outbreak and sanitary measures imposed by governments to prevent the virus from spreading.

We consider the issue of designing closed 3D UAV trajectories that allow for an energy efficient collection of data with a UAV-aided wireless sensor network. We consider a 3D wireless channel model and a realistic dynamical model for the UAV. The proposed trajectory is largely derived analytically, thus making its online calculation computationally tractable. We also show the importance of using realistic dynamical and energy models for the UAV in designing efficient trajectories. This is done mainly by showing that minimising the flying time of the UAV is not equivalent to minimising its energy consumption. Simulation results corroborate these findings.

We propose a deep learning based technique for power quality disturbances (PQD) detection and identification that aims at mimicking the reasoning of human field experts. This technique consists of processing small-size images containing superimposed time and frequency representations of the electric signal. The classification of PQD is performed with a convolutional neural network (CNN) trained with synthetic signals containing various single and multiple PQDs. Simulation results show that our technique is able to detect and identify with a high accuracy, in addition to pure sinusoidal, eight single PQDs and 20 of their combinations (up to four PQDs in the same signal) even in the presence of noise. Features such as lower computational load and simplicity while maintaining high performance sets the proposed technique apart from previous ones.

This paper presents an IoT platform designed for air pollution monitoring. It aiming to facilitate the testing of different data collection strategies, to simplify the air quality monitoring process, to provide the citizens with real-time information about air pollution, to allow citizens to participate in the air quality monitoring process, and to help the authorities identify zones of high air pollution and take the most appropriate measures to improve air quality. The sensor nodes have been developed using low-cost off-the-shelf hardware. Both nomadic and mobile sensor nodes have been developed. A novel sensor management middleware has been designed and developed to have the flexibility to remotely control the operational settings of the sensor nodes and to reduce the volume of transmitted data. Finally, two applications have been developed and implemented for data visualization. The first is a mobile friendly air pollution meter. The second offers a spatial visualization of air pollution levels using a Geographic Information System (GIS). The developed platform has been tested in multiple measurement campaigns. The results of one of these campaigns (conducted in Hay Nahda II, Rabat, Morocco) is presented in this paper to showcase the platform.

Wireless communications is nowadays an important aspect of robotics. There are many applications in which a robot must move to a certain goal point while transmitting information through a wireless channel which depends on the particular trajectory chosen by the robot to reach the goal point. In this context, we develop a method to generate optimum trajectories which allow the robot to reach the goal point using little mechanical energy while transmitting as much data as possible. This is done by optimizing the trajectory (path and velocity profile) so that the robot consumes less energy while also offering good wireless channel conditions. In this article, we consider a realistic wireless channel model as well as a realistic dynamic model for the mobile robot (considered here to be a drone). Simulations results illustrate the merits of the proposed method.

Post-traumatic stress disorder (PTSD) is a mental health disorder that is triggered by exposure to traumatic events. PTSD is associated with severe emotional disturbances that impair the person's functional abilities like social interactions and the ability to work. If left untreated, PTSD can even lead to suicide. Even though PTSD is a manageable and treatable disorder, many people are not able to get the appropriate treatment, either because of cost problems, stigma, mobility problems, or other causes. Since mobile phones are devices that are owned by a large number of people, m-health is an aspect that can facilitate access to the needed treatment and care for PTSD in many ways, one being via mobile applications (apps). Using gamification features in apps can be helpful in the management and treatment of PTSD. This paper identifies a list of 24 gamification-based apps for PTSD, 14 Android apps and 10 iOS apps, and assesses their functionalities and characteristics. The results highlighted mainly the need for more consideration of security aspects, incorporation of medical devices and physical indicators, and the apps' availability in multiple languages.

Object detection represents the most important component of Automated Vehicular Surveillance (AVS) systems. Moving vehicle detection based on background subtraction, with fixed morphological parameters, is a popular approach in AVS systems. However, the performance of such an approach deteriorates in the presence of sudden illumination changes in the scene. To address this issue, this paper proposes a method to adjust in real-time the morphological parameters to the illumination changes in the scene. The method is based on machine learning. The features used in the machine learning models are first, second, third and fourth-order statistics of the grayscale images, and the outputs are the appropriate morphological parameters. The resulting background subtraction-based object detection is shown to be robust to illumination changes, and to significantly outperform the conventional approach. Further, artificial neural network (ANN) is shown to provide better performance than Naive Bayes and K-Nearest Neighbours models.

Driver aggressiveness is a major cause of traffic accidents. Aggressive driving detection is an important application in the field of intelligent transportation systems (ITS). Developing systems capable of automatically detecting aggressive driving behavior should help improve traffic safety. In this paper we propose a novel solution to the problem of drivers' behavior classification based on a Long Short Term Memory Fully Convolutional Network (LTSM-FCN) to detect if a driving session involves aggressive behavior. We formulate the problem as a time series classification and test the validity of our approach on the UAH-DriveSet, a public dataset that provides a large amount of naturalistic driving data obtained from smartphones via a driving monitoring application. The proposed solution is compared to other deep learning and classical machine learning models for different processing time window sizes. It is shown that the proposed system outperforms the other methods in terms of the F-measure score, which reaches 95.88% for a 5 minutes window length.

Probing is the first step to gain access to a network. Predicting the rate levels of probing against a network sufficiently ahead of time could be insightful to security analysts and practitioners. Indeed, an accurate prediction would help to understand the potential threats and attacks menacing an organization's network. However, this prediction problem is a challenging task; prior works make predictions over time horizons not exceeding a few hours. In this work, we propose a machine learning approach to predict the next day probing rate levels for a network telescope by leveraging Twitter users' sentiments toward the country hosting the network telescope. First, we investigate the relationship between probing rates and Twitter sentiments. Second, we cluster the probing rates to determine the probing severity levels. Finally, we predict future rate levels using several classifiers. We show that incorporating negative sentiments improves significantly the prediction performance. This demonstrates the importance of incorporating social signals as predictors when predicting future probing rates.

The new era of the Internet of Things (IoT) is prompting the evolution of conventional vehicle ad-hoc networks (VANETs) into the Internet of Intelligent Vehicles (IoIV). Different from VANETs, where a vehicle is essentially considered as a node disseminating messages, the emerging IoIV paradigm is expected to regard each vehicle as a smart object equipped with a powerful multisensor platform, unprecedented communication capability, computing units, and Internet protocol (IP)-based connectivity. As such, the vehicles in IoIV are highly efficient in a broad array of vehicular and transportation applications. As a unique subset of general purpose IoT, IoIV can benefit from the existing research on VANET, which lays the foundation toward a more pervasive and ubiquitous communications and networking core that is essential for IoIV. Nevertheless, research in many aspects of IoIV, especially those that are application-driven and data-oriented ones, is still at its infancy.

In this paper, we present a detailed analysis of the coverage and spectral efficiency of a downlink cellular network. Rather than relying on the first order statistics of received signal-to- interference-ratio (SIR) such as coverage probability, we focus on characterizing its meta- distribution. Our analysis is based on the alpha- beta-gamma (ABG) path-loss model which provides us with the flexibility to analyze urban macro (UMa) and urban micro (UMi) deployments. With the help of an analytical framework, we demonstrate that selection of underlying degrees-of-freedom such as BS height for optimization of first order statistics such as coverage probability is not optimal in the network-wide sense. Consequently, the SIR meta-distribution must be employed to select appropriate operational points which will ensure consistent user experiences across the network. Our design framework reveals that the traditional results which advocate lowering of BS heights or even optimal selection of BS height do not yield consistent service experience across users. By employing the developed framework we also demonstrate how available spectral resources in terms of time slots/channel partitions can be optimized by considering the meta-distribution of the SIR.

The non-referenced web is estimated at five hundred times the size of the surface web. Darkweb represents about 6% of the non-referenced web and includes all kinds of delinquency: drug trafficking, counterfeiting, hacking market etc. Several studies about Darkweb marketplaces have been carried out, focusing mainly on the analysis of drug trafficking or the extraction of products sold in different forums. To the best of our knowledge, by the time of this study, no work has yet been done to analyze hacking trade business.Most hackers rely on malwares and softwares offered in different cyber markets to commit their attacks. The main objective of our work is to present an exploratory analysis of the illegal trade that is developing in this marketplaces in order to have a clear idea of threats that may harm individuals, industries and organizations. Through this work, we have been able to give a clear insight on the hacking market. The main motivation of sellers in this market is profit making, in fact this market generated over 26 million USD during the period studied. Product accessibility is also an alarming factor: 85% of the products offered do not exceed 150 USD, making cyber crime accessible to all. Finally, one cell controls almost the entire market, indicating the presence of a well-organized infrastructure. The results of this analysis are discussed below.

Understanding the properties exhibited by large scale network probing traffic would improve cyber threat intelligence. In addition, the prediction of probing rates is a key feature for security practitioners in their endeavors for making better operational decisions and for enhancing their defense strategy skills. In this work, we study different aspects of the traffic captured by a /20 network telescope. First, we perform an exploratory data analysis of the collected probing activities. The investigation includes probing rates at the port level, services interesting top network probers and the distribution of probing rates by geolocation. Second, we extract the network probers exploration patterns. We model these behaviors using transition graphs decorated with probabilities of switching from a port to another. Finally, we assess the capacity of Non-stationary Autoregressive and Vector Autoregressive models in predicting port probing rates as a first step towards using more robust models for better forecasting performance.

Direct device-to-device (D2D) communication presents as an effective technique to reduce the load at the base station (BS) while ensuring reliable localized communication. In this paper, we propose a large-scale M2M data Aggregation and Trunking (MAT) scheme, whereby the user equipments (UEs) aggregate M2M data from the nearby MTDs and trunk this data along with their own data to the BS in the cellular uplink. We develop a comprehensive stochastic geometry framework by considering a Poisson hard sphere model for UE coverage. The main motivation of this model is to capture the fact that a UE can gather data from short range, low-power MTDs located only in its close proximity while ensuring that an MTD is associated to at most one UE. We explore the inherent trade-off between the time reserved for aggregation and successful trunking of data to the BS and compare our results with the baseline case where no aggregation mechanism is used. We show that while the baseline case of connecting a bulk of MTDs directly with the BS is prohibitive, MAT scheme can efficiently gather data from selected MTDs in a distributed manner.

IEEE 802.11ah has recently emerged as a promising standard for enabling massive machine-to-machine (M2M) communication. In order to support uplink data transmission from dense machine type clients (such as smart meters, IoT end nodes etc.), 802.11ah relies upon the restricted access window (RAW) based Medium Access Control (MAC) protocol. The underlying motivation behind this protocol is to reduce the contention for spectrum access among a large number of devices. The nodes contend with each other in their assigned RAW slot using Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA). In each RAW slot, the throughput depends upon the number of nodes. Current studies have suggested that the duration of each RAW slot should be the same in the entire RAW frame. However in this paper, we argue that the duration of each RAW slot should be chosen according to the size of the group. We present a model where a RAW frame is divided into two sub-frames and the duration of RAW slots in each sub-frame is chosen according to the size of the group. With the help of an analytical framework, we demonstrate that the throughput under our proposed scheme can be significantly enhanced when compared to a conventional implementation.

In this article, we investigate how the performance of Massive MIMO cellular systems can be enhanced by introducing D2D communication. We consider a scenario where the base station (BS) is equipped with large, but finite number of antennas and the total number of UEs is kept fixed. The key design question is that what fraction of users should be offloaded to D2D mode in order to maximize the aggregate cell level throughput. We demonstrate that there exists an optimal user offload fraction, which maximizes the overall capacity. This fraction is strongly coupled with the network parameters such as the number of antennas at the BS, D2D link distance and the transmit SNR at both the UE and the BS and careful tuning of the offload fraction can provide up to 5× capacity gains.1

Mobile robots (MRs) using wireless communications often experience small-scale fading so that the wireless channel gain can be low. If the channel gain is poor (due to fading), the robot can move (a small distance) to another location to improve the channel gain and so compensate for fading. Techniques using this principle are called mobility diversity algorithms (MDAs). MDAs intelligently explore a number of points to find a location with high channel gain while using little mechanical energy during the exploration. Until now, the location of these points has been predetermined. In this paper, we show how we can adapt their positions by using channel predictors. Our results show that MDAs, which adapt the location of those points, can in fact outperform (in terms of the channel gain obtained and mechanical energy used) the MDAs that use predetermined locations for those points. These results will significantly improve the performance of the MDAs and consequently allow MRs to mitigate poor wireless channel conditions in an energy-efficient manner.

While stochastic geometric models based on Poisson point processes (PPPs) provide a tractable approach for the analysis of uniform two-tier network deployments, the performance evaluation of a non-uniform deployment remains an open issue, which we address in this paper. This is due to the fact that smaller cells can be more efficiently deployed in areas where the QoS of traditional macro base stations is poor. Therefore, in this paper, we introduce Stienen's model, which allows us to analyse such non-uniform deployment. In contrast to traditional PPP-based analysis, performance characterization under the Stienen model is more challenging due to location and density dependencies. However, we demonstrate that the performance can be approximated in a tractable manner. The developed statistical framework is employed to characterize the gains in terms of energy efficiency (EE) for non-uniform deployments. Results show an achievable 19% to 124% improvement in the macrocell coverage as compared to a uniform deployment, while the femtocell coverage and system EE are of the same order of magnitude for both deployments. These results are complemented with the fact that OPEX and CAPEX are reduced due to a lesser number of FAPs deployed.

In cognitive radio networks, secondary users (SUs) face two conflicting objectives. Each SU seeks to minimize the sensing duration while maximizing the detection probability of primary users (PU) to avoid interfering with their transmissions. Both objectives have a substantial effect on energy efficiency. This paper investigates a noncooperative setting for selecting the sensing duration when multiple SUs operate in the same network. Here, each SU has a certain throughput requirement. The interaction among SUs is captured via a satisfaction strategic game with explicitly stated throughput demands. We prove that depending on the throughput requirements, either zero, one or two Satisfaction Equilibria (SE) exist. We then provide a fully distributed learning algorithm (SELA) to discover them. Extensive simulation results show the validity of the proposed SELA and illustrate the relationship between the throughput demand and the sensing duration.

In this paper, we develop a comprehensive analytical framework for cellular networks that are enhanced with coordinated device-to-device (D2D) communication, where the D2D devices are equipped with content caching capabilities. The base station (BS) coordinates the D2D communication by establishing a D2D link between the requesting user and the nearest D2D helper within the same cell if the latter contains the requested content, otherwise, the BS serves the user itself. The motivation behind restricting D2D pairs within a macro cell is to make coordinated D2D communication realizable as the BS can keep track of the content of the devices without the increased overhead of inter-BS coordination. This approach is similar to LTE direct, where D2D pairing is managed by the BS. We model the locations of BS and D2D helpers using a homogeneous Poisson point process (HPPP). The distribution of the distance between the tagged user and its neighboring D2D helper within the cell is derived using disk approximation for the Voronoi cell, which is shown to be reasonably accurate. We fully characterize the cellular and D2D coverage and the link spectral efficiency of such a network. Our results reveal that cache enabled D2D communication becomes more effective as the requesting user moves away from the BS and high performance gains can be achieved compared to conventional cellular networks, especially when the popularity distribution is skewed and most popular files are requested.

In this paper we introduce Stienen's model for analysing the performance of a non-uniform two-tier networks. The topology of the network consists of a set of macro base stations (MBSs) uniformly deployed, and a set of femtocell access points (FAPs) deployed only outside exclusion areas (discs) surrounding the MBSs. The MBSs serve users within the innermost areas of each macrocell, while the femtocells are restricted to serve users located in the outermost areas towards the edge of the macrocells. Results show that the edge user performance in terms of coverage is highly increased by the addition of femtocells. Moreover, the coverage in the macrocell tier can be also increased in comparison with a macrocell-only network if the number of femtocells deployed is judiciously selected. Furthermore, a well balanced network can be achieved, where the same performance is expected throughout the entire area.

In this paper, the downlink performance of two-tier heterogenous network is investigated. We consider a scenario where the macro-tier is empowered by massive antenna-array thus allowing for Massive multiple-input multiple-output (MIMO) transmission scheduling. The small cellular network complements the macro-tier capacity. We propose a novel channel allocation mechanism which optimally splits the spectral resources to maximize network level throughput and energy efficiency. Our proposed channel allocation mechanism is robust to the topological and channel variations. More specifically, the proposed scheme is designed by capturing the random locations of the users in both tiers by a Poisson Point Process (PPP). The channel uncertainty is captured by considering Rayleigh fading complemented by large scale power law path-loss. Our analysis shows that there exists an optimal split which maximizes the network wide throughput and energy efficiency. We also demonstrate that there exists an optimal transmit power which maximizes the energy efficiency for the network. Under different scenarios, massive MIMO plays a vital role in improving sum rate capacity as compared to single antenna femtocells. Finally, using implementation parameters, we obtain the optimal configurations that improve system capacity and energy efficiency.

In this article we employ the third generation partnership project (3GPP) recommended path loss models for the analysis of cellular networks overlaid with D2D communication and channel inversion power control in the uplink. We characterize the coverage and average network throughput with the help of stochastic geometry. More specifically, we develop tractable expressions for the coverage in cellular and D2D modes. Our theoretical results differ significantly from previous work, which uses simple power law path loss models. The traditional methodology does not account for the presence of line-of-sight (LoS), non-line-of-sight (NLoS) and free space (FS) links. We demonstrate that such classification of links significantly impacts the inference which can be derived from the analysis for the design of overlaid D2D networks. In particular, we show that, contrary to the previous findings, the average throughput of the network does not saturate with the increase in the density of base stations (BS), but there exists an optimal mode selection threshold and BS density which maximizes the average throughput.

A new problem of optimizing a wireless mobile terminal trajectory under a given communication constraint is introduced. The mobile or vehicle has to move from a given starting point to a target point while uploading/downloading a given amount of data; this contrasts with the classical mobile communications paradigm where the communication and motion aspects are assumed to be independent. To reach the two aforementioned objectives, the mobile has to move sufficiently close to the wireless base station, while accounting for the energy cost due to motion. This setup is formalized here and leads us to determining non-trivial trajectories for the mobile. Remarkably, a counterpart of the Snell-Descartes law for the light propagation is exhibited (see Prop. 2) for the optimal trajectory of the mobile when the latter crosses zones in which the available data rates are different.

Software Defined Networking (SDN) has recently emerged to become one of the promising solutions for the future Internet. With the logical centralization of controllers and a global network overview, SDN brings us a chance to strengthen our network security. However, SDN also brings us a dangerous increase in potential threats. In this paper, we apply a deep learning approach for flow-based anomaly detection in an SDN environment. We build a Deep Neural Network (DNN) model for an intrusion detection system and train the model with the NSL-KDD Dataset. In this work, we just use six basic features (that can be easily obtained in an SDN environment) taken from the forty-one features of NSL-KDD Dataset. Through experiments, we confirm that the deep learning approach shows strong potential to be used for flow-based anomaly detection in SDN environments.

In cognitive radio, there might be some information about primary users’ signals available at secondary users’ receivers since communications systems usually employ training signals for channel estimation and synchronization purposes. This training information can be exploited along with data symbols to perform semi‐blind detection of primary users’ signals. In the literature, it is considered that the locally optimal semi‐blind detection metric is the linear combination of the energy detector (ED) and the matched filter, i.e. the hybrid detector. Locally optimum detection (LOD), known to be optimum in the low signal‐to‐noise ratio, is proposed here in the design of a weighted semi‐blind locally optimum detector (WSBLOD) by focusing on linear modulation in presence of an unknown phase shift and additive white Gaussian noise. By using LOD, it is shown that for binary phase shift keying‐modulated signals, the semi‐blind detector test statistic consists not only in combining linearly the matched filter and the ED but also the pseudo‐energy of the received signal. Then, the designed semi‐blind detector is improved by optimising the weights of the matched filter, energy and pseudo‐energy in the test statistic, which maximises the probability of detection. Simulation results show that the proposed WSBLOD outperforms the hybrid detector.

The increasing demand for any-time any-where wireless connectivity has posed a formidable ‘1000 × data challenge’ for service providers. With the envisioned 1000 × explosion in mobile data traffic by the end of year 2020, wireless network architecture needs to rapidly evolve. In particular, the evolution trajectory should be charted such that exponential gains can be realised in network wide resource efficiency. This requires a clean slate design for future 5G wireless networks while provisioning interoperability with the legacy deployment. Both operators and technology providers realise that 5G will not merely be a newer version of 4G simply provisioning faster data transfers. These 5G networks are expected to be more dynamic due to heterogeneity in terms of devices, technologies, spectral bands and deployment models. Heterogeneity is indeed the intrinsic and central feature of the evolving networking paradigm. Now several potential solutions have recently been proposed to meet the aforementioned challenges and all address both network architecture and technologies. On the architectural front, concepts such as (i) cloudification & softwarisation of radio access networks; (ii) split-plane deployment; (iii) licensed shared access; (iv) decoupled uplink and downlink transmissions; and (v) information/content centric networking are all being considered as the enabling candidates. In terms of new technologies: (i) mmWave communications; (ii) massive MIMO; (iii) D2D communications; (iv) small cell deployment; and (v) low power IoT communication technologies (such as Bluetooth Low Energy, 802.11.ah WiFi, LoRA, SIGFOX) are all vital design tools for future 5G HetNets. In addition, the so-called concept of ‘tactile internet’, which has a wide spectrum of requirements ranging from ultra-low latency to ultra-high throughput via deployment of HetNets, cannot be realised without significant advances in signal processing algorithms. Thus, the main objective of this Special Section is to provide a platform for the dissemination of important results in those signal processing techniques necessary for enabling large scale, heterogeneous, 5G wireless networks. The first part of this Special Section presents four contributions. In the first paper, Mumtaz et al. present an energy efficient algorithm for D2D users in the presence of other cellular users (CUs). The authors employ Lagrangian duality theory for optimising both the power and rate of the D2D users while guaranteeing an acceptable quality-of-service (QoS) for the CUs. Finally, the solution of the proposed algorithm is then employed to achieve proportional fairness between the D2D and the CU users. The second paper (Butt et al.) reflects a growing interest in the area of green communication. It has recently been accepted that opportunistic exploitation of ambient energy sources is going to be the cornerstone of future wireless networks. The authors discuss relay selection schemes with the objective of minimising outage probability for a network consisting of a single source, multiple relays and a single destination. The relays are powered by radio frequency (RF) signals from the source and the authors present an optimal relay selection strategy to minimise outage probability for the system. Finally, a numerical solution is developed to determine the optimal number of relays. In the third paper, Gurjar et al. examine the significance of wireless channel estimation error on the performance of an analogue network coding (ANC)-based MIMO two-way relay system employing zero-forcing (ZF) transceivers in a Rayleigh fading environment. An analytical framework has been developed to study the overall outage analysis and some interesting (exact) expressions have been derived for special cases such as when the relay is equipped with less than two antennas. Some of the important contributions of this work are: a) exact expressions for the overall outage probability and the ergodic sum-rate have been derived within the context of channel estimation error; b) the authors have shown that system diversity may reduce to zero in the presence of channel estimation error due to imperfect self-interference cancellation; c) they conclude that a low complexity solution can be further derived by exploiting channel estimation error with ZF transmission/reception for an ANC based MIMO two-way relay system. In the final paper, Li et al. propose a hierarchical precoding approach for multi-cell, multi-user systems with any number of base stations and users, which is suitable for any number of data streams. The key feature of this approach is to align the inter-user interferences within the same cell to the room spanned by the inter-cell interferences, by which both the inter-cell and inter-user interferences are cancelled simultaneously. The effectiveness of this proposed method is demonstrated with an extensive set of simulations. In summary, this Special Section presents some important recent advances in D2D and relay assisted communication networks with a special focus on energy efficiency. Moreover, some of the state-of-the-art methods in multiuser MIMO systems have also been studied. For those interested in future 5G wireless networks, these articles will serve as a good springboard to appreciate further developments in this important topic. Finally, we would like to thank (i) all the submitting authors for considering this Special Section as a potential journal in which to publicise their research work; (ii) the reviewers for their high quality evaluations; and (iii) the Editorial team of the IET Signal Processing journal for their professional support. Syed Ali Raza Zaidi is currently University Academic Fellow (Assistant Professor) at the University of Leeds, UK. Prior to this, he was a Research Fellow in SPCOM Research Group at Leeds. He received his B. Eng. degree in information and communication system engineering from the School of Electronics and Electrical Engineering, NUST, Pakistan in 2008. He was awarded the NUST's most prestigious Rector's gold medal for his final year project. From September 2007 till August 2008, he served as a Research Assistant in Wireless Sensor Network Lab on a collaborative research project between NUST, Pakistan and Ajou University, South Korea. In 2008, he was awarded overseas research student scholarship along with Tetley Lupton and Excellence Scholarships to pursue his PhD at the School of Electronics and Electrical Engineering, the University of Leeds, U.K. He was also awarded with COST IC0902, DAAD and Royal Academy of Engineering grants to promote his research. In 2013, he was conferred with the prestigious F.W. Carter Prize for outstanding Doctoral thesis by the University of Leeds. Dr. Ali was a visiting Research Scientist at Qatar Innovations and Mobility Centre from October to December 2013. He has served as an invited reviewer for IEEE flagship journals and conferences. Dr. Ali is also UK Liaison for the European Association for Signal Processing (EURASIP). He is currently serving as an editor for IEEE Communication Letters and Lead Guest Editor for IET Signal Processing Special Section on 5G Wireless Networks. He is also the general secretary for IEEE Technical Committee on 5G Networks. He has published more than 60 papers in leading IEEE journals and conferences and has chaired several IEEE workshops/conferences. His current research interests are in the area of design and implementation of large scale networks for machine-to-machine communication (including robotics and autonomous systems). Des McLernon received his B.Sc in electronic and electrical engineering and his MSc in electronics, both from the Queen's University of Belfast, N. Ireland. He then worked in industry on radar systems research and development with Ferranti Ltd in Edinburgh, Scotland and later joined Imperial College, University of London, where he took his PhD in signal processing. After first lecturing at South Bank University, London, UK, he moved to the School of Electronic and Electrical Engineering, at the University of Leeds, UK, where he is a Reader in Signal Processing. His research interests are broadly within the domain of signal processing for wireless communications (in which area he has published over 285 journal and conference papers). He has supervised over 35 PhD students, given many invited talks in the UK and abroad and is Associate Editor of the IET Signal Processing journal. He has been a member of various international conference TPC's and conference organisation committees - recent conference organisation includes IEEE SPAWC 2010, European Signal Processing Conference (EUSIPCO) 2013, IET Conference on Intelligent Signal Processing (London, 2013/2015) and IEEE Globecom 2014/2015 (2nd /3rd Workshops on Trusted Communications with Physical Layer Security). His current research projects include distributed sensing, PHY layer security, caching and energy efficiency in heterogeneous networks, energy harvesting, robotic and drone communications, intrusion detection in software defined networks, compressive sensing and time-frequency analysis. Muhammad Ali Imran received his M.Sc. (Distinction) and Ph.D. degrees from Imperial College London, UK, in 2002 and 2007, respectively. He is currently a Reader in Communications in the Institute for Communication Systems (ICS - formerly known as CCSR) at the University of Surrey, UK and an adjunct Associate Professor at the University of Oklahoma, USA. He has lead a number of multimillion-funded international research projects encompassing the areas of energy efficiency, fundamental performance limits, sensor networks and self-organising cellular networks. He is also leading the new physical layer work area for 5G innovation centre at Surrey. He has a global collaborative research network spanning both academia and key industrial players in the field of wireless communications. He has supervised 21 successful PhD graduates and published over 200 peer-reviewed research papers including more than 20 IEEE Transaction papers. He has been giving a series of expert tutorials on emerging Green 5G technologies and networks at IEEE flagship conferences such as WCNC, PIMRC and ICC. Recently, he has been appointed as an area Chair for IEEE ComSoc Technical Committee on Backhaul/Fronthaul Networking and Communications (TCBNC). He secured first rank in his B.Sc. and a distinction in his M.Sc. degree along with an award of excellence in recognition of his academic achievements conferred by the President of Pakistan. He has been awarded IEEE ComSoc's Fred Ellersick award 2014 and FEPS Learning and Teaching award 2014 and twice nominated for Tony Jean's Inspirational Teaching award. He is a shortlisted finalist for The Wharton-QS Stars Awards 2014 for innovative teaching and VC's learning and teaching award in University of Surrey. He is a senior member of IEEE and a Senior Fellow of Higher Education Academy (SFHEA), UK. Muhammad Zeeshan Shakir is a Senior Research Fellow at Carleton University, Canada. In recent years, he has been involved in several joint R&D initiatives with Telus, DragonWave, University of Surrey, KAUST, and TAMUQ. His research interests include design and deployment of diverse wireless communication systems, including hyper-dense heterogeneous networks and related 5G technologies. He has published more than 75 technical journal and conference papers and has contributed to seven books, all in reputable venues. He is an author of three research monographs including one authored book. He earned his PhD degree in electronic and electrical engineering from University of Strathclyde, Glasgow, UK in 2010. He is an Associate Technical Editor of IEEE Communications Magazine and has served as a Lead Guest Editor for IEEE Communications and IEEE Wireless Communications Magazines. He has been serving as Chair/Co-chair of several workshops/symposia in IEEE flagship conferences, such as ICC and GlobalSIP. He has been giving a series of expert tutorials on emerging Green 5G technologies and networks at IEEE flagship conferences such as Globecom, ICUWB, PIMRC and ICC. Recently, he has been appointed as a Chair to IEEE ComSoc Technical Committee on Backhaul/Fronthaul Networking and Communications (TCBNC). He is an active member of IEEE, IEEE ComSoc and IEEE Standard Association. Mounir Ghogho received his MSc degree in 1993 and PhD degree in 1997 from the National Polytechnic Institute of Toulouse, France. He was an EPSRC Research Fellow with the University of Strathclyde, Glasgow (Scotland), from September 1997 to November 2001. Since December 2001, he has been a faculty member with the school of Electronic and Electrical Engineering at the University of Leeds, UK, where he currently holds a Chair in Signal Processing and Communications. Since 2010, he has also been a Research Director at the International University of Rabat (Morocco). He was awarded the UK Royal Academy of Engineering Research Fellowship in September 2000. He is one of the recipients of the 2013 IBM Faculty award. He is currently an Associate Editor of the IEEE Signal Processing magazine. He served as an Associate Editor of the IEEE Transactions on Signal Processing from 2005 to 2008, the IEEE Signal Processing Letters from 2001 to 2004, and the Elsevier's Digital Signal Processing journal from 2011 to 2012. He is currently a member of the IEEE Signal Processing Society SAM Technical Committee. He served as a member of the IEEE Signal Processing Society SPCOM Technical Committee from 2005 to 2010 and a member of IEEE Signal Processing Society SPTM Technical Committee from 2006 to 2011. He was the General Chair of the 11th IEEE workshop on Signal Processing for Advanced Wireless Communications (SPAWC2010) and the 21st edition of the European Signal Processing Conference (EUSIPCO 2013), and the Technical co-Chair of the MIMO symposium of IWCMC 2007 and IWCMC 2008. His research interests are in signal processing and communication networks. He has published over 260 journal and conferences papers. He was awarded the UK Royal Academy of Engineering Research Fellowship in September 2000. He is also one of the recipients of the 2013 IBM Faculty award and is the EURASIP Liaison in Morocco.

Mobile robots that wish to communicate wirelessly often suffer from fading channels. They need to devise an energy-efficient strategy to search for a high-channel-gain position in a near vicinity from which to begin communications. Such a strategy has recently been introduced through the mobility diversity with multithreshold algorithm (MDMTA). In this paper, we establish the theoretical framework for a generalized version of the MDMTA. This allows improved wireless communications in fading channels for mobile robots via intelligent robotic motion with low mechanical energy expenditure.

The present work is devoted to the comprehensive analysis of the detrimental effects of noise uncertainty in wide-band multimode subband based spectrum sensing over frequency selective channels. Unlike existing analyses that are mostly limited to single-band detection, an analytical model is firstly formulated for the case of wide-band multimode subband based spectrum sensing, considering both noise uncertainty and frequency selectivity. This model is also extended for the case of detecting a reappearing primary user (PU), as well as for the sensing scenarios where the frequency range of a PU is unknown. Novel closed-form expressions are derived for the corresponding probabilities of false alarm and probabilities of detection. The derived expressions are subsequently employed in quantifying the effects of noise uncertainty and frequency selectivity. A subband based scheme for the sensing of multi-channel primary systems is presented and analyzed. The proposed method is found to exhibit significantly reduced sensing time and greatly improved robustness against noise uncertainty compared to the basic wideband energy detection method.

In this paper, we develop a game theoretic formulation for empowering cloud enabled HetNets with adaptive Self Organizing Network (SON) capabilities. SON capabilities for intelligent and efficient radio resource management is a fundamental design pillar for the emerging 5G cellular networks. The C-RAN system model investigated in this paper consists of ultra-dense remote radio heads (RRHs) overlaid by central baseband units that can be collocated with much less densely deployed overlaying macro base-stations (BSs). It has been recently demonstrated that under a user centric scheduling mechanism, C-RAN inherently manifests the trade-off between Energy Efficiency (EE) and Spectral Efficiency (SE) in terms of RRH density. The key objective of the game theoretic framework developed in this paper is to dynamically optimize the trade-off between the EE and the SE of the C- RAN. More specifically, for an ultra-dense C- RAN based HetNet, the density of active RRHs should be carefully dimensioned to maximize the SE. However, the density of RRHs which maximizes the SE may not necessarily be optimal in terms of the EE. In order to strike a balance between these two performance determinants, we develop a game theoretic formulation by employing a Nash bargaining framework. The two metrics of interest, SE and EE, are modeled as virtual players in a bargaining problem and the Nash bargaining solution for RRH density is determined. In the light of the optimization outcome we evaluate corresponding key performance indicators through numerical results. These results offer insights for a C-RAN designer on how to optimally design a SON mechanism to achieve a desired trade-off level between the SE and the EE in a dynamic fashion.

We address the optimal transmit power allocation problem (from the sensor nodes (SNs) to the fusion center (FC)) for the decentralized detection of an unknown deterministic spatially uncorrelated signal which is being observed by a distributed wireless sensor network. We propose a novel fully distributed algorithm, in order to calculate the optimal transmit power allocation for each sensor node (SN) and the optimal number of quantization bits for the test statistic in order to match the channel capacity. The SNs send their quantized information over orthogonal uncorrelated channels to the FC which linearly combines them and makes a final decision. What makes this scheme attractive is that the SNs share with their neighbours just their individual transmit powers at the current states. As a result, the SN processing complexity is further reduced.

Accurate fading characterization and channel capacity determination are of paramount importance in both conventional and emerging communication systems. The present work addresses the non-linearity of the propagation medium and its effects on the channel capacity. Such fading conditions are first characterized using information theoretic measures, namely, Shannon entropy, cross entropy and relative entropy. The corresponding effects on the channel capacity with and without power adaptation are then analyzed. Closed-form expressions are derived and validated through computer simulations. It is shown that the effects of nonlinearities are significantly larger than those of fading parameters such as the scattered-wave power ratio, and the correlation coefficient between the in-phase and quadrature components in each cluster of multipath components.

In this paper, the energy efficiency (EE) of different MIMO diversity schemes is analyzed for the downlink of a two-tier network consisting of both macro- and femto-cells. The locations of the base stations (BSs) in both tiers are modeled by spatial Poisson point processes (PPPs). The EE of the system in b/J/Hz is obtained for different antenna configurations under various diversity schemes. Adaptive modulation is employed to maximize both the throughput and the EE across both tiers. Borrowing well established tools from stochastic geometry, we obtain closed-form expressions for the coverage, throughput, and power consumption for a two tier rate adaptive cellular network. Building on the developed analytical framework, we formulate the resource allocation problem for each diversity scheme with the aim of maximizing the network-wide EE while satisfying a minimum QoS in each tier. We consider that both the number of antennas and the spectrum allocated to each tier constitute the network resource which must be efficiently selected for both tiers to maximize network-wide performance. The best performance in terms of the EE is provided by the schemes which strike a good balance between the achievable maximum throughput and the consumed power (both increasing with the number of RF chains used). In addition, the potential savings in EE by using femto-cells with sleeping mode capabilities are analyzed. It is observed that, when the density of active co-channel femto-cells exceeds a certain threshold, the EE of the system can be significantly improved by sleep scheduling.

In this work, we address the antenna tilt optimization problem for a two-tier cellular network consisting of macrocells and femtocells, where both tiers share the same spectrum and their positions are modeled via two independent Poisson point processes (PPPs). First, we derive the coverage probability for a traditional cellular network consisting only of macrocells and obtain the optimum tilt angle that maximizes the overall energy efficiency (EE). Gains of up to 400% in EE were found for a scenario (approximately) equivalent to a hexagonal cell deployment with cell radius of 200 m when the optimum tilt was selected. We then proceed to model the heterogeneous network (HetNet) scenario where femtocells are also deployed in the network's area. We observe that the macrousers performance is highly sensitive to the interference emanating from the femtocell tier. In order to circumvent this issue, interference coordination employing a guard zone for the macrocell user is proposed. Subsequently, we formulate a joint optimization problem where we derive both, the radius of a guard zone protecting the macrouser and the tilt angle that maximize the EE of the network.

In this article we present an angle of arrival (AoA) multi-robot system for rescue purposes which takes advantage of robots' mobility to improve the position estimate of an unknown target. The robots move according to a certain trajectory (a sequence of stopping points) designed to minimize the variance of the AoA estimation. We present two different techniques to generate these optimal trajectories, with each technique having its own advantage.

A novel approach for ensuring confidential wireless communication is proposed and analysed from a geometrical perspective. In this method, both the legitimate receiver and transmitter generate artificial noise (AN) to impair the eavesdropper's channel. The authors use the concept of insecure region to characterise the security performance when the eavesdropper's channel is unknown. The insecure region is defined as the region where the eavesdropper may decode the secret message. With the aim of minimising the size of the insecure region, an optimum power allocation strategy between the information bearing signal and the AN is proposed. Simulation results show that the proposed method achieves a good performance.

The authors address the physical layer security in multiple‐input‐single‐output communication systems. This study introduces a robust strategy to cope with the channel state information errors in the main link to convey confidential information towards a legitimate receiver while artificial noise is broadcast to confuse an unknown eavesdropper. The authors study how an eavesdropper physically located in the vicinity of the transmitter can put at risk the network's security, and hence, as a countermeasure, a ‘protected zone’ was deployed to prevent the close‐quarters eavesdropping attacks. The authors determine the size of the protected zone and transmission covariance matrices of the steering information and the artificial noise to maximise the worst‐case secrecy rate in a resource‐constrained system and to minimise the use of resources to ensure an average secrecy rate. The proposed robust masked beamforming scheme offers a secure performance even with erroneous estimates of the main channel showing that a protected zone not only enhances the transmission security but it allows us to make an efficient use of energy by prioritising the available resources.

In this article, we develop a comprehensive analytical framework to characterize the area spectral efficiency of a large scale Poisson cognitive underlay network. The developed framework explicitly accommodates channel, topological and medium access uncertainties. The main objective of this study is to launch a preliminary investigation into the design considerations of underlay cognitive networks. To this end, we highlight two available degrees of freedom, i.e., shaping medium access or transmit power. While from the primary user's perspective tuning either to control the interference is equivalent, the picture is different for the secondary network. We show the existence of an area spectral efficiency wall under both adaptation schemes. We also demonstrate that the adaptation of just one of these degrees of freedom does not lead to the optimal performance. But significant performance gains can be harnessed by jointly tuning both the medium access probability and the transmission power of the secondary networks. We explore several design parameters for both adaptation schemes. Finally, we extend our quest to more complex point-to-point and broadcast networks to demonstrate the superior performance of joint tuning policies.

The aim of this work is the formulation and derivation of the η-μ/Inverse Gaussian composite distribution which corresponds to a physical fading model. The η-μ distribution is a generalized small-scale fading model which accounts effectively for non-line-of-sight scenarios and includes as special cases the widely known Nakagami-m, Rayleigh, Hoyt and one sided Gaussian distributions. Similarly, the inverse Gaussian (IG) distribution is a convenient model which was recently shown to characterize shadowing more efficiently than the widely used gamma distribution. To this effect, the proposed η-μ/IG model provides an overall efficient characterization of multipath and shadowing effects which typically occur simultaneously. The offered modelling accuracy is achieved thanks to the remarkable flexibility of its parameters. This is also verified by the fact that the proposed model is capable of providing good fittings to experimental data that correspond to realistic wireless communication scenarios while they include as special cases the widely known Nakagami-m/IG, Rayleigh/IG and Hoyt/IG composite fading models. Novel analytic expressions are derived for the envelope and power probability density function (pdf) of the η-μ/IG model. The derived expressions can be utilized in various studies in radio communications, free space optical communications and ultrasound imaging, among others. Indicatively, an analytic expression is derived for the outage probability (OP) of η-μ/IG fading channels.

Sensing-throughput tradeoff under outage detection constraint has been studied before by assuming no uncertainty in estimation of the noise power, but this might not be the case in practice for an energy detector (ED). In this paper we examine analytically the effect of spectrum sensing on both the secondary user (SU) and the primary user (PU) throughputs under outage detection probability constraint in the presence of noise uncertainty and flat fading channels. First, we apply Jensen's Inequality to derive a new tight closed-form bound for the energy threshold that satisfies a certain outage detection probability. In addition, we derive both the secondary and the primary throughputs over flat fading channels in terms of the new threshold. The simulation results show that there exists an optimum sensing time that maximizes the secondary throughput. Finally, we show that the secondary throughput is more sensitive to noise uncertainty compared to the primary throughput.

In this article, we quantify the achievable spatial throughput of a multi-antenna Poisson cognitive radio network (CRN) collocated with a Poisson multi-antenna primary network. CR users employ Slotted-ALOHA medium access control. The success probability (SP) of a primary link is quantified in the presence of the secondary and primary interferers. It is demonstrated that two fold gains are experienced by employing multiple antennas at primary, i.e., (i) the fixed high desired SP threshold is met; (ii) CRs can also be accommodated without QoS deterioration. Further in this paper, the maximum permissible medium access probability (MAP) for CRN is derived from the link SP and primary users QoS constraint. The impact of the number of antennas and modulation employed at the primary on the permissible MAP of the CRN is also explored. Assuming that CR users employ multi-hop communication, QoS aware relaying with a radian sector forwarding area is studied. The average forward progress (AFP) and isolation probability for a CR user with QoS based connectivity is characterized under the permissible MAP. The spatial throughput for the CRN is quantified by the analysis of the AFP and the permissible MAP. It is shown that there exists an optimal MAP which maximizes the spatial throughput of the CRN. This optimal MAP is coupled with the permissible MAP, density of users, number of antennas and modulation schemes employed in both primary and secondary networks. Lastly, a few important design questions are investigated for multi-hop MIMO underlay CRNs.

We address physical layer security in multiple- input-multiple-output (MISO) communications in the presence of an unknown passive eavesdropper. Beamforming and artificial noise broadcasting are chosen to increase communications security. We first study the effect of a close eavesdropper on security and then we define a “Protected Zone” in the transmitter's vicinity. We present an optimisation strategy that intelligently sets the transmission power and the size of the protected zone to probabilistically achieve secrecy at a specified target secrecy rate. The results show that this strategy can achieve a high probability of secrecy by efficiently prioritising the use of the available resources.

In this paper, we investigate practical turbo receiver design for throughput-efficient relay ARQ transmissions over broadband cooperative MIMO channels. Our setup is comprised of three multi-antenna nodes: a source, a destination, and a relay node operating under the amplify-and-forward half-duplex relaying mode. To attain higher system average throughput, we adopt a two-slot transmission strategy where the ARQ mechanism is activated on top of the amplify-and-forward protocol. We derive a soft sub-packet combiner allowing the destination node to jointly perform sub-packet combining (over both time-slots and multiple ARQ rounds) and frequency domain (FD) MMSE filtering. Its computational load is then smoothly relaxed via a recursive implementation alleviating the memory requirements when the number of ARQ rounds increases. Simulation results show that the proposed transmission strategy along with turbo sub-packet combining at the destination side achieves significant average throughput performance gain compared with conventional ARQ-based cooperative relaying, especially in situations where the relay node is close to the source node.

This paper investigates the effects of power saving strategies on the performance of wireless local area networks (WLANs). More specifically, a power management model is formulated as an integer linear program that the network planner can use in order to achieve power savings while maintaining an acceptable quality of service (QoS), measured by the signal to interference ratio (SIR) for interference limited WLAN. Furthermore, through a network simulation implemented in NS-2, it is shown that the adaptive power saving scheme can guarantee the same average throughput as the non-adaptive counterpart, while significantly reducing the total transmitted power. Considering a realistic scenario, we show that, using the proposed power management model, one can save about 55% of the transmitted power while the SIR is increased by 6 dB thus improving the QoS. Also, using a simple experiment with two access points it is shown that, in the case of users within the overlap of the two coverage areas, the throughput remains constant when the transmit power is changed from a low value to a high value although a minor degradation of the average delay is noticed. As a conclusion, the commonly assumed fact that increasing the transmit power results in better network performance is not necessarily true and can result as shown in this paper in energy waste.

In this paper we study the effect of phase noise (PHN) in a single carrier cyclic prefix (SCCP) system operating with a known cyclic prefix. The PHN process is a limiting factor in high throughput communication systems as higher-order constellations are extremely sensitive to it. In this paper we propose a robust and non-iterative scheme to enhance both the estimation of, and compensation for, the PHN process and this also includes channel estimation. In contrast to existing approaches for PHN compensation, in this paper we use a known cyclic prefix, pilot bins and interpolation over the PHN estimates. Simulation results show that the performance of SCCP with a known cyclic prefix is superior to SCCP systems with conventional compensation schemes in the moderate and higher SNR region for all values of PHN.

In this letter, we address physical layer security in MISO communications in the presence of passive eavesdroppers, i.e., the eavesdroppers' channels are unknown to the transmitter. Spatial beamforming and artificial noise broadcasting are chosen as the strategy for secure transmission. With the aim of guaranteeing a given probability of secrecy, defined by quality of service constraints at the intended receiver and at the eavesdroppers, an optimum power allocation strategy between transmitted information and artificial noise is proposed. Both power constrained and power unconstrained systems are considered. Our proposed outage probability-based approach compares favourably to an existing second-order-statistic-based approach.

Robust operation of wireless sensor networks deployed in harsh environment is important in many application. In this paper, we develop a robust technique for distributed detection, localization, and estimation of a diffusive target. The algorithm shows superior performance to the conventional non-linear least square method under low measurement SNR and small number of sensor nodes.

This paper addresses the problem of beamforming (BF) design for orthogonal frequency division multiplexing (OFDM) based relay networks over frequency-selective channels. Both frequency-domain (FD) BF and time-domain (TD) BF are investigated. The later requires less feedback from the destination to perform BF. The BF vectors are designed by maximizing the minimum signal-to-noise-ratio (SNR) over all subcarriers at the destination, first under the total power constraint (TPC) and then under the per-relay power constraint (PPC). We show that both TPC and PPC BF designs lead to a quasi-convex optimization problem, which can be solved by bisection search method efficiently. Simulation results demonstrate that based on max-min SNR criterion, the performance of TD-BF rapidly approaches that of FD-BF when increasing the filter length. Moreover, it is found that for TD-BF, the minimum filter length required to achieve optimum performance under PPC is longer than that under TPC.

We present a probabilistic framework for physical layer secrecy when the locations and channels of the eavesdroppers are unknown. The locations are modeled by a Poisson point process. The channels include path loss and Rayleigh fading. Beamforming and frequency-selectivity of the fading channels are shown to greatly increase the probability of secure communications.

We first establish the dynamic Radar Cross Section (RCS) signal model for a conical ballistic missile warhead with precession motion. Scintillation is modeled as a log-normal multiplicative noise. The distribution of the obtained RCS signal is nonGaussian and cannot be obtained in closed-form. Hence, the exact Maximum Likelihood Estimation (MLE) of the pertinent parameter, the missile precession frequency, is untractable. We propose three pseudo MLE approaches. The first approach, called GML, enforces a Gaussian distribution on both the additive and multiplicative noise components. The second approach, called ML8, ignores the additive noise in the measured RCS. The third approach, called AOML, ignores the multiplicative noise. Simulations show that accounting for the multiplicative noise in the estimation significantly improves estimation performance.

This letter addresses the problem of beamforming design for an amplify-and-forward (AF) based two-way relaying network (TWRN) which consists of two terminal nodes and several relay nodes. Considering a two-time-slot relaying scheme, we design the optimal beamforming coefficients to maximize the sum-rate of AF-based TWRN under total relay power constraint (TRPC). Although the optimization problem is neither convex nor concave, we show that the global optimal solution can be obtained by the branch-and-bound algorithm. To address the computational complexity concern, we also propose a low-complexity suboptimal solution which is obtained by optimizing a cost function over one real variable only. Simulation results show that the proposed optimal solution outperforms existing schemes significantly. Moreover, we show that the suboptimal solution only suffers small sum-rate losses compared to the optimal solution.

Spectrum sensing in cognitive radio becomes a challenging task when the signals received at the secondary users' transmitters exhibit low power. Locally optimum detectors (LOD) are therefore desirable thanks to their optimality in the low SNR regime. Here, we assume that the primary user transmits a training sequence, and propose a semi-blind LOD (SBLOD). In the case of BPSK signals, the test statistic of the proposed SBLOD is shown to be a weighted sum of the matched filter output, the energy and pseudo-energy. For higher size constellations, the SBLOD reduces to a linear combination of the matched filter and the energy detector. Although combining the matched filter and energy detector is a classical approach, our study provides a systematic and (locally) optimal way of combining these detectors. Simulations results show the merits of the proposed detector.

We consider the problem of transmit beamforming (BF) design for cyclic prefixed (CP) transmissions over MISO frequency selective channels. Both CP single carriers (SC) and orthogonal frequency-division multiplexing (OFDM) systems are investigated. To reduce receiver complexity, frequency domain BF is adopted. The BF is designed by minimizing the arithmetic mean of the error probabilities at the receiver, first under the total power constraint (TPC) and then under the per-antenna power constraint (PPC). The solutions under the PPC are obtained using convex optimization tools. The simulation results show that although BF for SC only slightly outperforms BF for OFDM under TPC, the gap in performance becomes large under PPC. It is also shown that for large number of transmit antennas, the phase-rotation BF (PRB) is nearly optimal under PPC.

The multipath diversity and coding gain metrics for cyclic-prefixed single-carrier (SC-CP) systems, which characterize the bit error rate (BER) at high SNR, have not been carefully studied in the literature. We first show that, unlike OFDM, the diversity and coding gains for SC-CP are data-realization-dependent. Then, we show that there is a signal-to-noise ratio (SNR) threshold beyond which the dominant diversity order starts deviating from the maximum diversity order to eventually reduce to one at higher SNRs. Using the averaged pairwise probability, we derive an analytical expression for this SNR threshold. The latter is shown to increase with the block length and to be unrealistically high for moderate/high block lengths. Comparisons of SC-CP with rotated constellations and zero-padded SC systems are also provided.

In this paper, we investigate the timing and carrier frequency offset (CFO) synchronization problem in decode and forward cooperative systems operating over frequency selective channels. A training sequence which consists of one OFDM block having a tile structure in the frequency domain is proposed to perform synchronization. Timing offsets are estimated using correlation-type algorithms. And since some subcarriers are nulled in the proposed tile structure, CFOs are readily estimated using subspace-based methods. By judiciously designing the size of the tile, these algorithms are shown to have better performance, in terms of synchronization errors and bit error rate, than the computationally demanding SAGE algorithm.

Leakage-based methods which are based on the harmonic mean signal-to-interference ratio (SIR) maximization deliver a good tradeoff between the sum rate and bit-error rate (BER). In this paper, we present the joint design of linear transmit and receive beamformers that maximize the weighted harmonic mean signal-to-interference-and-noise ratio (SINR) for the downlink of the multiuser MIMO channel. By exploiting the uplink-downlink SINR duality, the non-convex optimization problem is transformed into a series of simpler problems which can be solved by geometric programming. When the minimum mean square error (MMSE) receive beamformers are employed, it is shown that the harmonic mean SINR maximization becomes a close approximation to the sum MSE minimization problem in the high SINR region. The simulations show that the proposed scheme outperforms other leakage-based schemes in terms of harmonic mean SINR, sum MSE, BER and sum rate.

Opportunistic relaying is an efficient way of acheiving diversity in wireless cooperative communication systems. In this paper, we analyze the performance of a proactive opportunistic non-regenerative relaying protocol, considering both maximum ratio combining (MRC) and selection combining (SC) schemes at the destination. We derive a closed-form expression for the cummulative distribution function (CDF) of the end-to-end signal-to-noise ratio (SNR) in the presence of Nakagami-m fading and a SC receiver. Utilizing this statistical result, we then derive a new closed-form expression for average symbol error probability (ASEP), valid for many generic modulations, assuming identical integer fading parameters. Furthermore, we also derive a new closed-form expression for the moment generating function (MGF) of the end-to-end SNR considering Nakagami-m fading environment and MRC at the destination. Moreover, using the MGF-based approach, we derive and analyze new closed-form expressions for ASEP when MRC is employed at the destination.

This paper addresses the problem of data detection in cooperative space-time block coded (STBC) orthogonal frequency division multiplexing (OFDM) systems in the presence of multiple carrier frequency offsets (CFO). An enhanced iterative maximum-likelihood detector (EIMLD) is proposed. This method consists of first removing inter-carrier interference (ICI), and then performing iterative symbol detection and inter-symbol interference reduction. Simulation results show that EIMLD significantly outperforms existing iterative methods. Comparisons with the zero-forcing and minimum-mean square error detectors, which require complex matrix inversion, are also carried out.

We address the problem of estimating doubly-selective channels using pilot clusters that are time-division multiplexed with the data. The pilot clusters consist of zero-padded pilot symbols in order to decouple channel estimation from data detection. Channel estimation is carried out using the basis expansion model (BEM-)based method, where different BEMs are investigated, and the exact MMSE method which requires full knowledge of the channel statistics. For a fixed number of pilot symbols, we attempt to optimize the power and placement of the pilot symbols at the transmitter side used in transmission, and the number of BEM coefficients used in channel estimation, in the sense of minimizing the total mean-square estimation error (MSE) that includes modelling error. Simulation results confirm that for a wide range of SNR and Doppler spread values, this optimization greatly reduces the MSE and the bit-error rate, and that modelling error, which was ignored in existing work on training design, should be taken into account. The effects of uncertainty in the channel statistics are also studied.

One of the fundamental problems in the wireless sensor networks is the coverage problem. The coverage problem fundamentally address the quality of service (surveillance or monitoring) provided in the desired area. In past, several studies have proposed different formulation and solutions to this problem. Nevertheless none of them has addressed minimum cost solution to the coverage problem. In this paper we consider minimum cost wireless sensor network which provides full coverage to any arbitrary geometric profile under deterministic/random deployment. We then formulate probabilistic coverage model which provides k - coverage probability for minimum cost wireless sensor networks.1

In this paper we propose a new multiaccess protocol that combines the concept of splitting tree algorithms for collision resolution with two of the most relevant cross-layer technologies for random access: retransmission diversity (e.g., NDMA-Network Diversity Multiple Access) and multipacket reception (MPR). The proposed protocol is shown to outperform all the existing algorithms based on either NDMA or MPR. Additionally, the protocol formulation provides an important generalization of the model used for the analysis of solutions in these three fields. Unlike conventional NDMA protocols, in which all the colliding users are requested to immediately retransmit in the next time-slot, our proposed algorithm calculates the optimum set of users allowed to retransmit at each one of the following time-slots. The optimization is based on the previous collected transmissions and on the MPR and source separation (SS) probabilities, thus maximizing throughput and minimizing access-delay. Two possible suboptimal algorithms with simplified feedback assumption and hence suitable for distributed resolution are further derived from the original algorithm: an enhanced version of NDMA assisted by MPR and a fair splitting tree algorithm assisted by MPR and SS. The capacity/stability region of the protocol for several system configurations with two active users is employed to assess the benefits of the proposed algorithms.

We address the problem of estimating doubly-selective channels using pilot clusters that are time-division multiplexed with the data. Channel estimation is carried out using different basis expansion models (BEMs), and direct MMSE channel estimation using the channel statistics. For a fixed number of pilot symbols, we attempt to optimize the power and placement of the pilot symbols used in transmission, and the number of BEM coefficients used in channel estimation, in the sense of minimizing the mean-square estimation error (MSE) that includes modelling error, which is normally neglected in existing work. Simulation results confirm that for a wide range of SNR and Doppler spread values, this optimization greatly reduces the MSE and the bit-error rate, and that modelling error should be taken into account when optimizing training. The effects of uncertainly in the channel statistics are also studied.

Frame timing and carrier frequency offsets in orthogonal frequency division multiplexing (OFDM) may drastically degrade performance if not accurately compensated. In practice, these offsets are estimated by transmitting a training block at the beginning of each frame. By designing this training block to have a repetitive structure, various estimation methods have been proposed in the literature. These existing estimation methods rely on the repetitive structure and not on the actual training block. In other words, these methods are based on some second-order statistics of the received signal. Here, we instead use first-order statistics and two modified versions of the nonlinear least squares method. The proposed methods are shown to provide significantly more accurate frame and frequency synchronization at the expense of a slight increase in implementation complexity. As a by-product, an accurate channel estimate is obtained with the same preamble, thus reducing the resources allocated to training.

Noncoherent receivers are favored for UWB-IR systems because of their low implementation complexity compared with coherent correlation receivers. However, existing noncoherent schemes, such as transmitted reference (TR) systems and frame-level differential receivers (FDR), suffer from performance degradation and energy efficiency loss. We propose to use block-coded modulation and develop a novel energy detection-based noncoherent reception scheme for UWB-IR systems. Our scheme is capable of effective noise/interference mitigation without loss of energy efficiency and data rate. Performance evaluation shows that even in the presence of strong inter-frame interference and multiuser interference, our scheme is robust.

Carrier frequency offset (CFO) estimation is a key challenge in multicarrier systems such as OFDM. Often, this task is carried out using a preamble made of a number, say J, of repetitive-slots (RS). Here, we address the issue of optimal RS preamble design using the Cramer -Rao bound. We show that the optimal value of J is a trade-off between the multipath diversity gain and the number of unknowns to be estimated. In the case of correlated channel taps, we show that uniform power loading of the active subcarriers is not optimal (in contrast with the uncorrelated case) and a better power loading scheme is proposed. The theoretical results are supported by computer simulations.

Spatial multiplexing in the multi-user MIMO (MU-MIMO) downlink allows each user in the system to receive multiple data subchannels simultaneously using the same time and spectral resources. In this paper, we propose an iterative interference minimization scheme for the MU-MIMO downlink which is capable of transmitting multiple data subchannels to each user, at a relaxed constraint compared to schemes that rely on perfect cancellation of the co-channel interference (CCI). The proposed scheme minimizes inter-user interference (IUI) based on signal-to-leakage ratio (SLR) maximization and performs subchannel decoupling to eliminate the inter-symbol interference (ISI) within each user's signal.

Broadband beamforming, including frequency invariant beamforming, is often achieved by processing the received sensor signals through tapped delay-lines. Unlike most of the existing techniques, we propose a novel design method for three-dimensional frequency invariant beamformers without employing tapped delay-lines. The resultant beamformer, which can form a beam steerable along both the elevation angle and the azimuth angle, has a very simple implementation. A design example is provided to show the effectiveness of the proposed method.

In this paper, we propose a new resource allocation mechanism which is designed to improve the multiuser detection of wireless network diversity multiple access (NDMA) protocols. The mechanism consists of allocating an average number of time- slots to the active user population according to a prescribed quality of service requirement. It is dubbed virtual because it does not rely on user scheduling over different time-slots, but instead it is controlled by adjusting the probability of false alarm of each user. The allocation mechanism improves the throughput of conventional NDMA protocols at the expense of both an access delay degradation and an increased system complexity. The system requires to recover the signals from a mixing system with more outputs (the collected network transmissions) than inputs (the collided packets). By setting the average allocated time-slots to remain constant over different traffic loads, both the optimum transmission probabilities and the stability region of the protocol are approximated by relevant closed-form expressions. Also, the proposed analytical formulation extends the expressions of conventional NDMA systems to the asymmetrical user case (i.e. users with different data rates and detection statistics). Finally, it is shown that under extreme traffic loads, multiuser detection conditions and quality of service requirements, the proposed system degrades into the equivalent of the traditional networking protocols TDMA (time division multiple access) and Slotted- ALOHA (S-ALOHA).

In channel estimation based on Data-Dependent Superimposed Training (DDST) certain frequency components are removed from the data symbols, prior to transmission. Since this means information is removed at the transmitter, the receiver may not find it possible to correctly recover the data. In this paper conditions for data identifiability are given when using a QAM constellation, and an analytical expression for the likelihood of correct detection is given for the noise-free case. A new detection method is then proposed, that can allow the use of larger constellations, and its performance is compared to the existing method.

Timing synchronization is a preeminent challenge in ultra-wideband impulse radios (UWB-IRs). The conventional all-digital synchronization methods encounter some formidable implementation difficulties such as high rate sampling and high complexity RAKE structure. To avoid these challenges, semi-analog methods have been motivated recently. We have recently proposed a code-assisted blind synchronization (CABS) algorithm to realize timing synchronization blindly with the help of the discriminative property of both time hopping codes and well- designed polarity codes. The algorithm requires sampling at the frame rate only and bypasses channel estimation during the synchronization phase. This paper analyzes the identifiability and both the probability of acquisition and the the mean square error performance of CABS analytically. A data-aided code-assisted synchronization (CAS) algorithm is also proposed and a modified version of CABS which relies solely on the time hopping code is investigated.

Non-line-of-sight (NLOS) propagation degrades the performance of wireless location systems. Thus, developing algorithms that are robust to NLOS is of great importance. This paper introduces a new location technique that utilizes time of arrival (TOA) and angle of arrival (AOA) measurements. In the proposed method, we assume the signal from the mobile station reaches each base station via one dominant scatterer. By including the scatterer's coordinates as unknowns in a TOA/AOA-based cost function and imposing some equality and inequality constrains, the location of the mobile station (MS) is shown to significantly improve. The performance of the proposed algorithm is assessed and compared with that of existing algorithms through extensive simulations.

For MIMO systems operating over frequency-selective channels, we establish the Cramer-Rao bound (CRB) for the CFO and channel parameters. We derive training sequences so that the resulting CRB on the CFO is independent of the channel. We show that these designs lead to simple implementation of the maximum likelihood estimators of the CFO and channel parameters, Simulation results illustrate the performance of the proposed designs

Synchronization is a critical issue in ultra-wideband (UWB) communications. In conventional UWB systems employing time-hopping, the time-shifted pulses in each symbol interval have the same polarity. Here, to ease synchronization, we modify the polarity of the pulses using carefully designed binary codes. The proposed algorithm is based on the correlation of the received waveform and the code template. The proposed code-assisted blind synchronization (CABS) requires symbol sampling rate only, and it is shown via simulations to significantly outperform existing blind synchronization methods in terms of both mean-square errors and bit error rate, at the expense of a moderate increase in computational complexity.

We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation, phase-shift keying, and pulse amplitude modulation communications systems. We study the performance of a standard CFO estimate, which consists of first raising the received signal to the Mth power, where M is an integer depending on the type and size of the symbol constellation, and then applying the nonlinear least squares (NLLS) estimation approach. At low signal-to noise ratio (SNR), the NLLS method fails to provide an accurate CFO estimate because of the presence of outliers. In this letter, we derive an approximate closed-form expression for the outlier probability. This enables us to predict the mean-square error (MSE) on CFO estimation for all SNR values. For a given SNR, the new results also give insight into the minimum number of samples required in the CFO estimation procedure, in order to ensure that the MSE on estimation is not significantly affected by the outliers

We address the problem of frequency-selective channel estimation and symbol detection using superimposed training. Both single and multiple antenna systems are studied. The superimposed training consists of the sum of a known sequence and a data-dependent sequence unknown to the receiver. The data-dependent sequence cancels the effects of the unknown data on channel estimation. The performance of the proposed approach is shown to outperform significantly existing methods based on superimposed training.

We address the problem of frequency-selective channel estimation and symbol detection using superimposed training. The superimposed training consists of the sum of a known sequence and a data-dependent sequence that is unknown to the receiver. The data-dependent sequence cancels the effects of the unknown data on channel estimation. The performance of the proposed approach is shown to significantly outperform existing methods based on superimposed training (ST).

We address the problem of harmonic retrieval in the presence of multiplicative and additive noise. We derive the finite-sample Cramer Rao bound (CRB) as well as the asymptotic (large sample) CRB when the multiplicative noise is complex-valued and noncircular. These bounds are then analyzed with respect to the signal parameters. Finally, we prove that the square-power based frequency estimate, which is equivalent to the so-called nonlinear least square estimate, is asymptotically efficient when the multiplicative noise is white.

Due to the high sensitivity of OFDM to carrier frequency-offset, accurate estimation algorithms are required in order to achieve high performance. We show that many of the existing methods share the same underlying approach, which is the exploitation of null subcarriers. Based on this, a novel computationally simple estimator, named the approximate nonlinear squares estimator (ANLS), is developed. We show that the performance of the ANLS estimator is very close to that of the computationally more demanding NLS estimator, and superior to existing low-complexity estimators.

We address the problem of carrier frequency offset (CFO) synchronization in OFDM-based communications systems in the context of frequency-selective fading channels. A blind CFO estimator was recently developed, which exploits the virtual subcarriers (VSC) in a practical OFDM system. Here, we propose a semi-blind approach where a few (either zero or non-zero) pilots are inserted in the OFDM block. The semi-blind estimator (SBE) is shown to significantly outperform the blind estimator even with a few pilots provided they are equispaced. Further, the SME based on zero pilots consistently outperforms that based on non-zero pilots. Reliable estimation of an unknown frequency-selective channel requires that some (or all) of the pilots be non-zero, since zero pilots are useless for channel estimation. It is shown that if the total number of pilots is (much) larger that the channel order, zero pilots and non-zero pilots lead to almost the same CFO estimation performance. Therefore, in this case, non-zero pilots should be preferred to zero pilots.

We address the problem of frequency synchronization in OFDM-based communications systems in the context of frequency-selective fading channels. Frequency offsets are estimated by inserting null sub-carriers into a single OFDM block. The paper clarifies issues related to acquisition range and identifiability of carrier frequency offset (CFO), and performance of estimators. A deterministic maximum likelihood estimation approach is adopted. We derive necessary and sufficient conditions on the number of null-subcarriers and their placement in order to ensure identifiability. The Cramer-Rao bound (CRB) for the CFO is derived; for a given number of null sub-carriers, the optimal placement which minimizes the CRB is derived. We show that if the number of null sub-carriers is less than half the total number of sub-carriers, performance is optimal when the null sub-carriers are equispaced.

Blind synchronization of minimum-shift-keying signals in the context of time-selective fading communication channels is considered. Strictly feedforward algorithms based on second- and fourth-order cyclic statistics are proposed for frequency and timing offset estimation. The new estimators are shown to outperform existing methods. Extensions of the algorithm to GMSK are also studied. Finally, fourth-order cyclic statistics are shown to provide an accurate estimate of the Doppler spread.

This paper considers the problem of estimating the parameters of chirp signals with randomly time-varying amplitude. Two methods for solving this problem are presented. First, a nonlinear least-squares approach (NLS) is proposed. It is shown that by minimizing the NLS criterion with respect to all samples of the time-varying amplitude, the problem reduces to a two-dimensional maximization problem. A theoretical analysis of the NLS estimator is presented and an expression for its asymptotic variance is derived. It is shown that the NLS estimator has a variance very close to the Cramer-Rao bound. The second approach combines the principles behind the high-order ambiguity function (HAF) and the NLS approach. It provides a computationally simpler but suboptimum estimator. A statistical analysis of this estimator is also carried out. Numerical examples attest to the validity of the theoretical analysis and establish a comparison between the two proposed methods.

The problem of estimating the phase parameters of a phase modulated signal in the presence of coloured multiplicative noise (random amplitude modulation) and additive white noise, both Gaussian, is addressed. Closed-form expressions for the exact and large-sample Cramer-Rao bounds (CRB) are derived. It is shown that the CRB is not significantly affected by the colour of the modulating process, especially when the signal-to-noise ratio is high. Hence, maximum likelihood type estimators which ignore the noise colour and optimize a criterion with respect to only the phase parameters are proposed. These estimators are shown to be equivalent to the nonlinear least squares estimators which consist of matching the squared observations with a constant amplitude phase modulated signal when the mean of the multiplicative noise is forced to zero. Closed-form expressions are derived for the efficiency of these estimators, and are verified via simulations.

This paper addresses the problem of detecting deterministic signals in multiplicative noise. The multiplicative noise model is appropriate for modelling coherent imaging systems such as SAR and laser. Locally optimum (LO) detectors are derived for any arbitrary multiplicative noise distribution. The gamma and generalized Gaussian distributions are studied in detail. We also introduce an extension of the generalized Gaussian density to include asymmetry. The performance of the LO detectors is studied and compared with that of the linear correlation detector. The paper gives insight into the influence of the tail length of the noise distribution on the detection power.

The problem of interest is the estimation of the parameters of harmonics in the presence of additive and multiplicative noise. Expressions for the asymptotic performance of the cyclic-variance (CV) based method are derived when the multiplicative noise has a non-zero mean. We show that the CV-based method may yield more accurate results than methods based on the cyclic mean (CM), depending upon the color of the noise and the intrinsic and local SNRs. The performance is analyzed in detail for several special cases of the multiplicative noise, such as white Gaussian, AR and generalized-Gaussian noise.

The problem of detection and estimation of harmonics in multiplicative and additive noise is addressed. The problem may be solved using (i) the cyclic mean if the harmonic amplitude is not zero mean or (ii) the cyclic variance if the harmonic amplitude is zero mean. Solution (ii) may be used when the amplitude of the harmonic is not zero mean while solution (i) fails in the case of zero mean harmonic amplitudes. The paper answers the following questions: given a multiplicative and additive noisy environment, which solution is optimal? The paper determines thresholds on the coherent to non-coherent sine powers ratio which delimitate the regions of optimality of the two solutions. Comparison with higher-order cyclic statistics is presented. Gaussian as well as non-Gaussian noise sources are studied.

The problem of estimation of harmonics with randomly time-varying amplitude is addressed. We deduce closed forms of the Cramer-Rao bounds by modeling these fluctuations by a Gaussian or non-Gaussian AR process. For the frequency estimation, the paper compares a parametric approach based on the ARMA representation of the signal and a non-parametric approach based on filtered version of higher-order statistics. Convergence and asymptotic normality of the estimators are established.

Parameter estimation for multiplicative noisy data is a pertinent signal processing problem encountered in a wide range of signalling and data-processing applications, including radar, sonar, radio astronomy, seismology and vibroacoustics. The assumption of additive noise is, in these contexts, insufficient for adequate signal modeling. The model considered here incorporates the Gaussian amplitude-modulated sinusoids. New algorithms are developed for frequency estimation. The corresponding probability density, prediction, innovation process and ergodicity property are presented. Higher order statistics are used, especially when the process is also contaminated by an additive noise.