Reliable detection of road users is critical to the safety of automated driving systems. While object detectors based on deep neural networks are widely used for this purpose, they remain susceptible to errors that could compromise safety. A promising strategy to mitigate these risks involves run-time perception monitoring mechanisms, commonly referred to in the literature as self-assessment or introspection. Current research in this area predominantly addresses anomaly detection, or monitoring camera-based 2D object detection, with insufficient focus on in-distribution errors and 3D object detection. Additionally, existing 2D studies often monitor activation patterns from the final layers of the network backbone, overlooking earlier activations that preserve higher spatial resolution. Yet, high-resolution early-layer activations can be valuable for detecting errors with sparse 3D point clouds. We also argue that not all objects in a scene should equally influence frame-level error detection, a factor often neglected in current methods. To address these gaps, we propose a novel self-assessment mechanism for 3D object detection that leverages activation patterns from multiple network layers. This mechanism employs spatial filtering to focus the model within an area of interest in the close vicinity of the ego vehicle. Additionally, it utilises an object filtering mechanism, which specifically targets the missed objects by excluding the points in those already detected. We evaluate our method using widely recognised object detectors and public datasets. Additionally, we demonstrate its robustness under domain shifts with real-world LiDAR data collected on motorways in diverse weather conditions. Results show the proposed mechanism provides 6% AUROC improvement over last-layer activation methods with spatial filtering on the NuScenes dataset. It also demonstrates a superior ability to transfer knowledge under domain shifts. Code is available at https://github.com/yatbazhakan/multi-layer-introspection .

This paper addresses the critical challenge of detecting and interpreting cybersecurity anomalies in Autonomous Vehicles (AVs) under high-frequency cyberattacks using a Particle filter. In this approach, we leverage the power of Particle filter-based state estimation, combining it with suitably defined thresholds and anomaly detection metrics to detect cyberattacks. In addition, to demonstrate the superior performance of the Particle filter for cyberattack detection, a comparison between the Kalman filter and the Particle filter has been conducted. The simulation results performed on the HuskyA200 autonomous ground vehicle (AGV) demonstrated that the Particle filter provides superior performance and interpretability during high-frequency attacks compared to the Kalman filter. The feedback from Particle filter-based detection can help the control functions of the vehicle, such as velocity damping and orientation correction, mitigate attack impacts for real-time operation.

The Society of Automotive Engineers (SAE) J2954 Differential Inductive Positioning System (DIPS) is a ground breaking technology introduced to enable positioning technique for electric vehicle (EV) wireless power transfer (WPT). This technology and its related standard have great potential to bring EV wireless charging to mass production and opens the doors for commercializing autonomous vehicles. Although the DIPS standard has defined the hardware requirements [1], the positioning algorithm design has not been addressed in the literature. In this paper, we introduce the industry's first algorithm for DIPS. We mathematically derive the signal model and parameters estimation algorithm, then evaluate the estimation accuracy of the proposed algorithm using Monte Carlo simulations. The evaluation results have shown the algorithm can achieve centimeter-level accuracy and approach the Cramér-Rao bound.

Precise situational awareness is essential for the safe deployment of artificial intelligence in real-world applications, particularly in assisted and automated driving (AAD) systems. Among perception techniques, panoptic segmentation is a promising technique to identify and categorise objects, impending hazards, and drivable space at a pixel level. While panoptic quality might be affected by automotive camera data quality, a comprehensive understanding and modelling of their relationship remains underexplored. Motivated by such a need, this work proposes a unifying pipeline to evaluate the robustness of panoptic segmentation models for automotive cameras, correlating it with 8 traditional image quality metrics (IQA). The proposed pipeline begins by generating a novel degraded dataset, D-Cityscapes+, featuring 19 realistic automotive degradation types at varying severity levels, including novel models for darkness and snowfall conditions with veiling effect. Evaluations on 14 state-of-the-art segmentation model backbones yielded key insights: 1) large-particle degradations (e.g., lens droplets, heavy snow) severely degrade segmentation performance, increasing uncertainty and edge-concentrated segmentation errors; 2) Transformer-based models outperform CNN models under adverse conditions; however, longer processing time, a higher number of parameters, and computational cost are limiting their real-world deployment; 3) frequency-based IQA metrics, such as CW-SSIM, strongly correlate with segmentation performance, serving as reliable predictive tools. 4) visual enhancements via restoration do not coherently benefit downstream segmentation tasks, underscoring the need for perception-specific restoration techniques. The benchmark and code:https://github.com/Warwick-Jocelyn/BRPS.

Deep Reinforcement Learning (DRL) has demonstrated promising capability in solving complex control problems. However, DRL applications in safety-critical systems are hindered by the inherent lack of robust validation techniques to assure their performance in such applications. One of the key requirements of the verification process is the development of effective techniques to explain the system functionality, providing why the system produces specific results in given circumstances. Recently, interpretation methods based on the Counterfactual (CF) explanation approach have been proposed to address the problem of explanation in DRLs. This article proposes a novel CF explainer to interpret the decisions made by a black-box DRL. To evaluate the efficacy of the proposed explanation framework, we carried out several experiments in the domains of Automated Driving Systems (ADSs) and the Atari Pong game. Our analysis demonstrates that the proposed framework generates plausible and meaningful explanations for various decisions made by deep underlying DRLs. Additionally, we discuss the practical implications of our approach for various automotive stakeholders, illustrating its potential real-world impact. Source codes are available at https://github.com/Amir-Samadi/Counterfactual-Explanation .

Abstract In vehicular communications, channel estimation is a complex problem due to the joint time–frequency selectivity of wireless propagation channels. To this end, several signal processing techniques as well as approaches based on neural networks have been proposed to address this issue. Due to the highly dynamic and random nature of vehicular communication environments, precise characterization of temporal correlation across a received data sequence can enable more accurate channel estimation. This paper proposes a new pilot constellation scheme in combination with a small feed‐forward neural network to improve the accuracy of channel estimation in V2X systems while keeping low the implementation complexity. The performance is evaluated in typical vehicular channels using simulated BER curves, and it is found superior to traditional channel estimation methods and state‐of‐the‐art neural‐network‐based implementations such as feed‐forward and super‐resolution. It is illustrated that the improvement becomes pronounced for small subcarrier spacings (or low 5G numerologies); hence, this paper contributes to the development of more reliable mobile services across rapidly varying vehicular communication channels with rich multi‐path interference.

The Industrial Internet of Things (IIoT) has been recognized as having the potential to offer substantial benefit to a wide range of industrial sectors. However, the widespread development and deployment of IIoT pose a set of challenges, including the shortage of spectrum resources and network security. Given the heterogeneity of IIoT devices, conventional cryptographic security techniques are not sufficient, since they suffer from challenges, including computation, storages, latency, and interoperability. In this article, we present a physical layer security analysis for cognitive IIoT networks. In our system, IIoT devices opportunistically utilize the primary spectrum; thereby improving spectrum efficiency and allowing access to a large number of devices. Specifically, the considered network uses space-time block coding (STBC) in conjunction with nonorthogonal multiple access (NOMA) in underlay cognitive mode to realize data transmission for IIoT devices with high-spectrum efficiency. The secure STBC-NOMA transmission model is established by taking into account the interference from a primary user. New approximate and asymptotic expressions of secrecy outage probability (SOP) for the secondary users (SUs) are derived to characterize the system’s secrecy performance. In addition, the SU’s closed-form secrecy ergodic rate (ER) is provided. The proposed STBC-NOMA approach, when compared to the classic single antenna (SA)-based NOMA, achieves better SOP performance for all SUs, as confirmed by both analytical and simulation findings. Furthermore, we show that the proposed STBC-NOMA framework improves the SOP performance of weaker users. Additionally, the STBC-NOMA framework outperforms the SA-NOMA framework in terms of secrecy ER performance for all SUs.

Metaverse, a hypothetical digital environment linking the cyber world and the physical world, is expected to revolutionize the way people interact. In the metaverse, people interact with objects, the environment, and each other through digital representations of themselves or avatars across time and space. For example, in the metaverse, people can have meetings with colleagues hundreds of miles away. They can also walk through the aisles of a store, find the best fit and have it delivered to their doorstep. It is also possible to simulate the optimal process manufacturing line to adjust for product variation and minimize bottlenecks, or test an innovative aircraft wing design without building expensive prototypes.

Metaverse, a hypothetical digital environment linking the cyber world and the physical world, is expected to revolutionize the way people interact. In the metaverse, people interact with objects, the environment, and each other through digital representations of themselves or avatars across time and space. For example, in the metaverse, people can have meetings with colleagues hundreds of miles away. They can also walk through the aisles of a store, find the best fit, and have it delivered to their doorstep. It is also possible to simulate the optimal process manufacturing line to adjust for product variation and minimize bottlenecks, or test an innovative aircraft wing design without building expensive prototypes.

Accurate and reliable perception of the surrounding environment, e.g., detection and classification of nearby objects, is the primary and most important function of automated/autonomous vehicles. However, onboard perception systems face challenges in complex road segments due to various environmental effects, such as occlusions, or high sensor noise. A potential enhancement is to equip such environments with cost-effective infrastructures that perceive the environment and provide additional perception support to autonomous vehicles through vehicle-to-everything (V2X) communication technologies. This paper develops an experimental study of vehicle detection and tracking on a bird’s eye view (BEV) map using raw video collected from several low-cost roadside monocular cameras with overlapping views installed near a motorway junction to support the merging of autonomous vehicles. The paper explains how to produce vehicle tracks from the camera infrastructure and reports the real-world evaluation of the proposed solution on a physical test bed in the UK’s West Midland region.

Accurate velocity estimation of surrounding moving objects and their trajectories are critical elements of perception systems in Automated/Autonomous Vehicles (AVs) with a direct impact on their safety. These are non-trivial problems due to the diverse types and sizes of such objects and their dynamic and random behaviour. Recent point cloud based solutions often use Iterative Closest Point (ICP) techniques, which are known to have certain limitations. For example, their computational costs are high due to their iterative nature, and their estimation error often deteriorates as the relative velocities of the target objects increase ($>$2 m/sec). Motivated by such shortcomings, this paper first proposes a novel Detection and Tracking of Moving Objects (DATMO) for AVs based on an optical flow technique, which is proven to be computationally efficient and highly accurate for such problems. This is achieved by representing the driving scenario as a vector field and applying vector calculus theories to ensure spatiotemporal continuity. We also report the results of a comprehensive performance evaluation of the proposed DATMO technique, carried out in this study using synthetic and real-world data. The results of this study demonstrate the superiority of the proposed technique, compared to the DATMO techniques in the literature, in terms of estimation accuracy and processing time in a wide range of relative velocities of moving objects. Finally, we evaluate and discuss the sensitivity of the estimation error of the proposed DATMO technique to various system and environmental parameters, as well as the relative velocities of the moving objects.

Automated driving systems (ADSs) aim to improve the safety, efficiency and comfort of future vehicles. To achieve this, ADSs use sensors to collect raw data from their environment. This data is then processed by a perception subsystem to create semantic knowledge of the world around the vehicle. State-of-the-art ADSs’ perception systems often use deep neural networks for object detection and classification, thanks to their superior performance compared to classical computer vision techniques. However, deep neural network-based perception systems are susceptible to errors, e.g., failing to correctly detect other road users such as pedestrians. For a safety-critical system such as ADS, these errors can result in accidents leading to injury or even death to occupants and road users. Introspection of perception systems in ADS refers to detecting such perception errors to avoid system failures and accidents. Such safety mechanisms are crucial for ensuring the trustworthiness of ADSs. Motivated by the growing importance of the subject in the field of autonomous and automated vehicles, this paper provides a comprehensive review of the techniques that have been proposed in the literature as potential solutions for the introspection of perception errors in ADSs. We classify such techniques based on their main focus, e.g., on object detection, classification and localisation problems. Furthermore, this paper discusses the pros and cons of existing methods while identifying the research gaps and potential future research directions.

Collaborative 3D object detection exploits information exchange among multiple agents to enhance accuracy of object detection in presence of sensor impairments such as occlusion. However, in practice, pose estimation errors due to imperfect localization would cause spatial message misalignment and significantly reduce the performance of collaboration. To alleviate adverse impacts of pose errors, we propose CoAlign, a novel hybrid collaboration framework that is robust to unknown pose errors. The proposed solution relies on a novel agent-object pose graph modeling to enhance pose consistency among collaborating agents. Furthermore, we adopt a multiscale data fusion strategy to aggregate intermediate features at multiple spatial resolutions. Comparing with previous works, which require ground-truth pose for training supervision, our proposed CoAlign is more practical since it doesn't require any ground-truth pose supervision in the training and makes no specific assumptions on pose errors. Extensive evaluation of the proposed method is carried out on multiple datasets, certifying that CoAlign significantly reduce relative localization error and achieving the state of art detection performance when pose errors exist. Code are made available for the use of the research community at https://github.com/yifanlu0227/CoAlign.

Camera image simulation is integral to the virtual validation of autonomous vehicles and robots that use visual perception to understand their environment. It also has applications in creating image datasets for training learning-based vision models. As camera image simulation takes into account a wide variety of external and internal parameters, achieving a high-fidelity simulation is a computationally expensive process. Recently, several neural network-based techniques have been proposed to reduce the computational complexity of image rendering, a critical element of the camera simulation pipeline. However, the existing methods are tailored for monocular camera images and are not optimised for stereo images, which are widely used in autonomous driving applications. To address this, we propose a technique based on Stereo Super Resolution (SSR) to speed up the simulation of stereo images. The proposed method first simulates stereo images at a lower resolution, then super-resolves them to their original resolution using our introduced SSR model, ETSSR. We evaluated the performance of our technique using the CARLA driving simulator and created our own synthetic dataset for training ETSSR. The evaluations indicate that our approach can speed up stereo image simulation by a factor of up to 2.57 over various resolutions. Moreover, it shows that our ETSSR achieves on-par or superior performance compared to the state-of-the-art models, using significantly fewer parameters and FLOPs. We have made our source code and dataset available athttps://github.com/hamedhaghighi/ETSSR.

Automated and autonomous vehicles are often required to operate in complex road environments with potential hazards that may lead to hazardous events causing injury or even death. Therefore, a reliable autonomous hazardous event detection system is a key enabler for highly autonomous vehicles (e.g., Level 4 and 5 autonomous vehicles) to operate without human supervision for significant periods of time. One promising solution to the problem is the use of graph-based methods that are powerful tools for relational reasoning. Using graphs to organise heterogeneous knowledge about the operational environment, link scene entities (e.g., road users, static objects, traffic rules) and describe how they affect each other. Due to a growing interest and opportunity presented by graph-based methods for autonomous hazardous event detection, this paper provides a comprehensive review of the state-of-the-art graph-based methods that we categorise as rule-based, probabilistic, and machine learning-driven. Additionally, we present an in-depth overview of the available datasets to facilitate hazardous event training and evaluation metrics to assess model performance. In doing so, we aim to provide a thorough overview and insight into the key research opportunities and open challenges.

In this paper we calculate the exact cumulative distribution function (CDF) of the path distance (L1 norm) between a randomly selected intersection and the k-th nearest node of the Cox point process driven by the Manhattan Poisson line process. The CDF is expressed as a sum over the integer partition function$p\!\left(k\right)$, which allows us to numerically evaluate the CDF in a simple manner. The distance distributions can be used to study the k-coverage of broadcast signals in intelligent transportation systems (ITS) transmitted from a \ac{RSU} that is located at an intersection. They can also be insightful for network dimensioning in urban vehicle-to-everything (V2X) systems, because they can yield the exact distribution of network load within a cell, provided that the \ac{RSU} is located at an intersection. Finally, they can find useful applications in other branches of science like spatial databases, emergency response planning, and districting. We corroborate the applicability of the distance distribution model using the map of an urban area.

Autonomous driving requires robust and accurate real time localisation information to navigate and perform trajectory planning. Although Global Navigation Satellite Systems (GNSS) are most frequently used in this application, they are unreliable within urban environments because of multipath and non-line-of-sight errors. Alternative solutions exist that exploit rich visual content from images that can be corresponded with a stored representation, such as a map, to determine the vehicles location. However, one major cause of reduced location accuracy are variations in environmental conditions between the images captured and those stored in the representation. We tackle this issue directly by collecting a simulated and real-world dataset captured over a single route under multiple environmental conditions. We demonstrate the effectiveness of an end-to-end approach in recognising place and by extension determining vehicle location.

Internet of Vehicles (IoV) is one of the most promising applications of Internet of Things (IoT) in the automotive industry, which can empower moving vehicles to exchange information with neighboring cars, roadside infrastructure, remote servers, traffic control centers, and so on. IoV expects to support a wide range of vehicular services, such as road safety, path planning, infotainment, and smart parking, which will play a vital role in intelligent transportation systems (ITSs)[1]–[3]. The main enabling platforms for IoV consist of dedicated short-range communications (DSRCs)-based networks and cellular networks (C-V2X). However, these terrestrial networks alone might not be able to support the vehicular applications well in all the cases and scenarios, due to the issues of limited coverage and capacity, as well as costly deployment.

3D object detection is a common function within the perception system of an autonomous vehicle and outputs a list of 3D bounding boxes around objects of interest. Various 3D object detection methods have relied on fusion of different sensor modalities to overcome limitations of individual sensors. However, occlusion, limited field-of-view and low-point density of the sensor data cannot be reliably and cost-effectively addressed by multi-modal sensing from a single point of view. Alternatively, cooperative perception incorporates information from spatially diverse sensors distributed around the environment as a way to mitigate these limitations. This article proposes two schemes for cooperative 3D object detection using single modality sensors. The early fusion scheme combines point clouds from multiple spatially diverse sensing points of view before detection. In contrast, the late fusion scheme fuses the independently detected bounding boxes from multiple spatially diverse sensors. We evaluate the performance of both schemes, and their hybrid combination, using a synthetic cooperative dataset created in two complex driving scenarios, a T-junction and a roundabout. The evaluation shows that the early fusion approach outperforms late fusion by a significant margin at the cost of higher communication bandwidth. The results demonstrate that cooperative perception can recall more than 95% of the objects as opposed to 30% for single-point sensing in the most challenging scenario. To provide practical insights into the deployment of such system, we report how the number of sensors and their configuration impact the detection performance of the system.

This paper presents the design of a robust distributed state-feedback controller in the discrete-time domain for homogeneous vehicle platoons with undirected topologies, whose dynamics are subjected to external disturbances and under random single packet drop scenario. A linear matrix inequality (LMI) approach is used for devising the control gains such that a bounded$H_{\infty }$norm is guaranteed. Furthermore, a lower bound of the robustness measure, denoted as$\gamma $gain, is derived analytically for two platoon communication topologies, i.e., the bidirectional predecessor following (BPF) and the bidirectional predecessor leader following (BPLF). It is shown that the$\gamma $gain is highly affected by the communication topology and drastically reduces when the information of the leader is sent to all followers. Finally, numerical results demonstrate the ability of the proposed methodology to impose the platoon control objective for the BPF and BPLF topology under random single packet drop.

Positioning and navigation are critical functions of automated driving functions, which help autonomous vehicles determine their absolute and relative positions in the environment that they operate. Integrity Monitoring (IM) systems, which are intended to assess the reliability and trustworthiness of the information provided by the navigation systems, are crucial for ensuring the safety of automated driving functions. This paper provides a comprehensive review of the existing IM frameworks for safety-critical navigation applications and expands on the state-of-the-art of the most recent development of such systems for connected automated vehicles. We mainly focus on IM methods for Global Navigation Satellite Systems (GNSS) and Inertial Navigation Systems (INS). However, we also cover IM for map assisted and wireless signal augmented navigation systems, which are promising for high-performance navigation applications, such as automated driving functions. For each main category of solutions, key aspects such as the characteristics of measurement errors and faults related to various data sources are discussed to provide deeper insights into designing of reliable IM systems. Also, some of the major open research challenges to the best knowledge of the authors have been identified and discussed.

The state-of-the-art decision and planning approaches for autonomous vehicles have moved away from manually designed systems, instead focusing on the utilisation of large-scale datasets of expert demonstration via Imitation Learning (IL). In this paper, we present a comprehensive review of IL approaches, primarily for the paradigm of end-to-end based systems in autonomous vehicles. We classify the literature into three distinct categories: 1) Behavioural Cloning (BC), 2) Direct Policy Learning (DPL) and 3) Inverse Reinforcement Learning (IRL). For each of these categories, the current state-of-the-art literature is comprehensively reviewed and summarised, with future directions of research identified to facilitate the development of imitation learning based systems for end-to-end autonomous vehicles. Due to the data-intensive nature of deep learning techniques, currently available datasets and simulators for end-to-end autonomous driving are also reviewed.

Behaviour prediction function of an autonomous vehicle predicts the future states of the nearby vehicles based on the current and past observations of the surrounding environment. This helps enhance their awareness of the imminent hazards. However, conventional behavior prediction solutions are applicable in simple driving scenarios that require short prediction horizons. Most recently, deep learning-based approaches have become popular due to their promising performance in more complex environments compared to the conventional approaches. Motivated by this increased popularity, we provide a comprehensive review of the state-of-the-art of deep learning-based approaches for vehicle behavior prediction in this article. We firstly give an overview of the generic problem of vehicle behavior prediction and discuss its challenges, followed by classification and review of the most recent deep learning-based solutions based on three criteria: input representation, output type, and prediction method. The article also discusses the performance of several well-known solutions, identifies the research gaps in the literature and outlines potential new research directions.

Next-generation intelligent transportation systems (ITS) are envisioned to greatly improve transportation safety and efficiency by incorporating wireless communications and informatics technologies into transportation systems. As the cornerstone for ITS, vehicular communication networks enable vehicles on the go to exchange information with other vehicles and the external environments, which expect to play a significant role in supporting a variety of services such as road safety, traffic management, and infotainment. However, the existing terrestrial networks including dedicated shortrange communications (DSRC)-based networks and cellular networks alone cannot serve the vehicular applications very well in different scenarios, due to the inherent issues of deployment, coverage, and capacity. It is imperative to exploit other communication infrastructures, such as low-earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and high-altitude platforms, to support vehicular applications better, resulting in space-air-ground integrated networks (SAGIN). SAGIN can provide more comprehensive and three-dimensional network connectivity for moving vehicles, anywhere and anytime, by exploiting their respective advantages in terms of coverage, flexibility, reliability, and availability.

In the 5G network, dense deployment and millimetre wave (mmWave) are some of the key approaches to boost network capacity. Dense deployment of mmWave small cells using narrow directional beams will escalate the cell and beam related handovers for high mobility of vehicles, which may in turn limits the performance gain promised by 5G-mmWave based vehicle-to-infrastructure (V2I) communication. One of the research issues in mmWave handover is to minimise the handover needs by identifying long lasting connections. In this paper, we first develop an analytical model to derive the vehicle sojourn time within a beam coverage. When multiple connections offered by nearby all mmWave small cells are available when upon a handover event, we further derive the longest sojourn time among all potential connections which represents the theoretical upper-bound limit of the sojourn time performance. We then design a Fuzzy Logic (FL) based distributed beam-centric handover decision algorithm to maximise vehicle sojourn time. Simulation experiments are conducted to validate our analytical model and show the performance advantage of our proposed FL-based solution when compared with commonly used approach of connecting to the strongest connection.

High reliability is one of the key requirements for the future fully connected autonomous vehicles. This paper proposes a novel highly reliable multiple access technique for Vehicle-to-Everything (V2X) networks. The proposed technique uses Index Modulation (IM) in conjunction with Non-Orthogonal Multiple Access (NOMA), which significantly improves the performance of detection at the receiver side. It also benefits from superimposed IM with repetition coding and power allocation factor in V2X networks. We investigate mapping rules for IM-aided NOMA for multiple vehicles. Performance evaluation in this paper shows that both diversity order and power gain can be improved if the proposed scheme is deployed, resulting in a lower probability of index and symbol errors in presence of sparsely activated sub-carriers compared to Orthogonal Multiple Access (OMA) or NOMA.

Automated vehicles are increasingly getting main-streamed and this has pushed development of systems for autonomous manoeuvring (e.g., lane-change, merge, and overtake) to the forefront. A novel framework for situational awareness and trajectory planning to perform autonomous overtaking in high-speed structured environments (e.g., highway and motorway) is presented in this paper. A combination of a potential field like function and reachability sets of a vehicle are used to identify safe zones on a road that the vehicle can navigate towards. These safe zones are provided to a tube-based robust model predictive controller as reference to generate feasible trajectories for combined lateral and longitudinal motion of a vehicle. The strengths of the proposed framework are: 1) it is free from non-convex collision avoidance constraints; 2) it ensures feasibility of trajectory even if decelerating or accelerating while performing lateral motion; and 3) it is real-time implementable. The ability of the proposed framework to plan feasible trajectories for high-speed overtaking is validated in a high-fidelity IPG CarMaker and Simulink co-simulation environment.

3D object classification can be realised by rendering views of the same object from different angles and aggregating all the views to build a classifier. Although this approach has been previously proposed for general objects classification, most existing works did not consider visual impairments. In contrast, this paper considers the problem of 3D object classification for driving applications under impairments (e.g. occlusion and sensor noise) by generating an application-specific dataset. We present a cooperative object classification method where multiple images of the same object seen from different perspectives (agents) are exploited to generate more accurate classification. We consider model generalisation capability and its resilience to impairments. We introduce an occlusion model with higher resemblance to real-world occlusion and use a simplified sensor noise model. The experimental results show that the cooperative model, relying on multiple views, significantly outperforms single-view methods and is effective in mitigating the effects of occlusion and sensor noise.

Automated Driving (AD) technology is rapidly advancing and being tested in large-scale pilots. The L3Pilot project will test AD functions at SAE automation level 3 and 4 in 100 cars, from 12 vehicle owners (car manufacturers and suppliers), with 1,000 drivers across 10 different countries in Europe. The project will thus generate a huge amount of data, part of which will be aggregated and shared among project participants and publicly for answering a set of research questions about the evaluation of such aspects as technical and traffic issues, user acceptance, impact, socio-economic impact. This paper investigates the design of an Internet of Things (IoT) framework aimed at storing data collected from the prototype vehicles and pre-processed in the various test sites, in order to protect privacy and intellectual property (IP). The framework is expected to make such aggregated information easily available to analysts through a web interface. The framework, of which we present also a pre-pilot lab test, is accessible through a RESTful API and features different user roles to meet the different information access requirements.

One of the cutting edge requirements envisioned for next-generation mobile networks is to support ultra-reliable and low latency communication (URLLC), as well as to meet massive traffic demand in the next few years. Although network densification has been considered as one of the promising solutions to boost capacity and high throughput, the impact of mobility on latency and reliability in dense networks has not been well investigated. Moreover, handovers, especially in dense networks, can cause extra delay to the communication and degrade reliability performance. In this paper, we aim to analyse the impact of different handover hysteresis parameters on the performance metrics, such as end-to-end delay and packet loss ratio (PLR). In this regard, we compare latency and PLR performance around cell borders including the handover process with the overall period of simulation. Simulation results show that the impact of mobility becomes more significant in dense networks due to frequent exposure to cell borders and handovers.

Summary Experts believe that the Internet of Things (IoT) is a new revolution in technology and has brought many advantages for our society. However, there are serious challenges in terms of information security and privacy protection. Smart objects usually do not have malware detection due to resource limitations and their intrusion detection work on a particular network. Low computation power, low bandwidth, low battery, storage, and memory contribute to a resource‐constrained effect on information security and privacy protection in the domain of IoT. The capacity of fog and cloud computing such as efficient computing, data access, network and storage, supporting mobility, location awareness, heterogeneity, scalability, and low latency in secure communication positively influence information security and privacy protection in IoT. This study illustrates the positive effect of fog and cloud computing on the security of IoT systems and presents a decision‐making model based on the object's characteristics such as computational power, storage, memory, energy consumption, bandwidth, packet delivery, hop‐count, etc. This helps an IoT system choose the best nodes for creating the fog that we need in the IoT system. Our experiment shows that the proposed approach has less computational, communicational cost, and more productivity in compare with the situation that we choose the smart objects randomly to create a fog.

Mobility, node density, and the demand for large volumes of data exchange have aggravated competition for limited resources in the wireless communications environment. This paper proposes a novel MAC scheme called segmentation MAC (SMAC), which can be used in large-scale vehicle-to-everything (V2X) communication networks. SMAC functions to support the dynamical allocation of radio channels. It is compatible with the asynchronous multi-channel MAC sub-layer extension of the IEEE 802.11p standard. A key innovate feature of SMAC is that the segmentation of the network and channel allocations are dynamically adjusted according to the density of vehicles. We also propose a novel efficient forwarding mechanism to ensure inter-segment connectivity. To evaluate the performance of inter-segment connectivity, a rigorous analytical model is proposed to measure the multi-hop dissemination latency. The proposal is evaluated in network simulator NS2 as well as the standard IEEE 1609.4 and two asynchronous multi-channel MAC benchmarks. Both analytical and simulation results demonstrate better effectiveness of the proposed scheme compared with the existing similar schemes in the literature.

An autonomous vehicle (AV) requires an accurate perception of its surrounding environment to operate reliably. The perception system of an AV, which normally employs machine learning (e.g., deep learning), transforms sensory data into semantic information that enables autonomous driving. Object detection is a fundamental function of this perception system, which has been tackled by several works, most of them using 2D detection methods. However, the 2D methods do not provide depth information, which is required for driving tasks, such as path planning, collision avoidance, and so on. Alternatively, the 3D object detection methods introduce a third dimension that reveals more detailed object's size and location information. Nonetheless, the detection accuracy of such methods needs to be improved. To the best of our knowledge, this is the first survey on 3D object detection methods used for autonomous driving applications. This paper presents an overview of 3D object detection methods and prevalently used sensors and datasets in AVs. It then discusses and categorizes the recent works based on sensors modalities into monocular, point cloud-based, and fusion methods. We then summarize the results of the surveyed works and identify the research gaps and future research directions.

We investigate the energy efficiency of a conventional collaborative compressed sensing (CCCS) scheme in cognitive radio networks. In particular, we derive expressions for the throughput, energy consumption and energy efficiency, and analyze the trade-off between the achievable throughput and the energy consumption of the underlying CCCS scheme. Furthermore, we formulate a multiple variable non-convex optimization problem to determine the optimum compression level that maximizes the energy efficiency, subject to interference constraints. We propose a sub-optimal solution based on tight approximations to simplify the aforementioned optimization problem, and further demonstrate that the energy efficiency achieved by the CCCS scheme is higher than that of conventional collaborative sensing scheme, under the same predefined conditions. It is further shown that the increase in the energy efficiency of CCCS scheme is due to the considerable decrease in the energy consumption, which is particularly noticeable with a large number of sensors.

In time-division-duplexing (TDD) massive multiple-input multiple-output (MIMO) systems, channel reciprocity is exploited to overcome the overwhelming pilot training and the feedback overhead. However, in practical scenarios, the imperfections in channel reciprocity, mainly caused by radio-frequency mismatches among the antennas at the base station side, can significantly degrade the system performance and might become a performance limiting factor. In order to compensate for these imperfections, we present and investigate two new calibration schemes for TDD-based massive multi-user MIMO systems, namely, relative calibration and inverse calibration. In particular, the design of the proposed inverse calibration takes into account a compound effect of channel reciprocity error and channel estimation error. We further derive closed-form expressions for the ergodic sum rate, assuming maximum ratio transmissions with the compound effect of both errors. We demonstrate that the inverse calibration scheme outperforms the traditional relative calibration scheme. The proposed analytical results are also verified by simulated illustrations.

For an autonomous vehicle to operate safely and effectively, an accurate and robust localization system is essential. While there are a variety of vehicle localization techniques in literature, there is a lack of effort in comparing these techniques and identifying their potentials and limitations for autonomous vehicle applications. Hence, this paper evaluates the state-of-the-art vehicle localization techniques and investigates their applicability on autonomous vehicles. The analysis starts with discussing the techniques which merely use the information obtained from on-board vehicle sensors. It is shown that although some techniques can achieve the accuracy required for autonomous driving but suffer from the high cost of the sensors and also sensor performance limitations in different driving scenarios (e.g., cornering and intersections) and different environmental conditions (e.g., darkness and snow). This paper continues the analysis with considering the techniques which benefit from off-board information obtained from V2X communication channels, in addition to vehicle sensory information. The analysis shows that augmenting off-board information to sensory information has potential to design low-cost localization systems with high accuracy and robustness, however, their performance depends on penetration rate of nearby connected vehicles or infrastructure and the quality of network service.

In this paper, we investigate the application of differential modulation in simultaneous wireless information and power transfer (SWIPT) relay networks. Considering time switching (TS) and power splitting (PS) receiver architectures, we adopt a moments-based approach to derive novel expressions for the outage probability and throughput of SWIPT relay systems with the amplify-and-forward (AF) relaying protocol. We quantify the impact of several system parameters involving the energy conversion efficiency and the TS and PS ratio assumptions, imposed on the energy harvesting (EH) relay terminal. Our results reveal that the throughput performance of the TS protocol is superior to that of the PS protocol at lower receive signal-to-noise (SNR) values, which is in contrast to point-to-point SWIPT systems. A Monte Carlo simulation study is presented to corroborate the proposed analysis.

In this paper, a novel hybrid precoding algorithm is proposed for the downlink of a multiantenna multiuser massive multiple-input multiple-output (MIMO) system. Firstly, a modified block diagonalization precoding technique is presented. Then, it is shown that combining the modified block diagonalization with hybrid beamforming can achieve a similar sum-rate as block diagonalization with digital beamforming can. Moreover, the performance of the presented algorithm is comparable to the achievable rate in the single-user scenario where there is a full collaboration among the receivers. In a sparse scattering channel, the proposed technique has significantly higher sum-rate compared to a zero- forcing based hybrid beamformer. When digital phase shifters with 3-bits of resolution are used at the RF beamformer, the proposed algorithm achieves the performance of a hybrid beamforming algorithm with analog phase shifting. \n

Channel reciprocity in time-division duplexing (TDD) massive multiple-input multiple-output (MIMO) systems can be exploited to reduce the overhead required for the acquisition of channel state information (CSI). However, perfect reciprocity is unrealistic in practical systems due to random radio-frequency (RF) circuit mismatches in uplink and downlink channels. This can result in a significant degradation in the performance of linear precoding schemes, which are sensitive to the accuracy of the CSI. In this paper, we model and analyse the impact of RF mismatches on the performance of linear precoding in a TDD multi-user massive MIMO system, by taking the channel estimation error into considerations. We use the truncated Gaussian distribution to model the RF mismatch, and derive closed-form expressions of the output signal-to-interference-plus-noise ratio for maximum ratio transmission and zero forcing precoders. We further investigate the asymptotic performance of the derived expressions, to provide valuable insights into the practical system designs, including useful guidelines for the selection of the effective precoding schemes. Simulation results are presented to demonstrate the validity and accuracy of the proposed analytical results.

Hybrid vehicular networks (HVNs) are foreseen to provision effective solutions in terms of broadcast services, increased capacity and expanding coverage. Cooperative Awareness Messages (CAMs) are one of the standard means of exchanging information among connected vehicles and smart road infrastructures. Timely dissemination of CAMs is a non-trivial challenge, particularly, in congested areas. This paper proposes a novel and effective decentralised network segmentation based multichannel MAC scheme for large-scale dense HVNs, namely, Decentralised Cognitive Segmentation-based Multichannel MAC (DCSMMAC). The proposed scheme helps reduce the contention level in each single hop via the segmentation of the large-scale network and efficient channel allocation scheme. DCSMMAC is a fully distributed solution, in the sense that it handles access to the shared channels without relying on the assistance from roadside units, cluster heads or other interfaces. This approach eliminates the overhead associated with channel allocation and clustering algorithm making the proposed scheme suitable for large-scale self-organised networks. Performance of the proposed scheme is evaluated and compared with the benchmark IEEE 802.11p, in terms of overall packet delivery rate, packet delivery rate on distance basis, and penetration rate. These results demonstrate that DCSMMAC is superior to the benchmark scheme and reliable at offering the desired QoS in the dense large-scale vehicular networks.

This study proposes a novel adaptive stochastic radio access selection scheme for mobile users in heterogeneous cellular‐wireless local area network (WLAN) systems. In this scheme, a mobile user located in dual coverage area randomly selects WLAN with probability of ω when there is a need for downloading a chunk of data. The value of ω is optimised according to the status of both networks in terms of network load and signal quality of both cellular and WLAN networks. An analytical model based on continuous time Markov chain is proposed to optimise the value of ω and compute the performance of proposed scheme in terms of energy efficiency, throughput, and call blocking probability. Both analytical and simulation results demonstrate the superiority of the proposed scheme compared with the mainstream network selection schemes: namely, WLAN‐first and load balancing .

This study investigates the downlink resource allocation problem in orthogonal frequency division multiple access heterogeneous networks consisting of macrocells and femtocells sharing the same frequency band. The focus is to devise optimised policies for femtocells' access to the shared spectrum, in terms of femtocell transmissions, in order to maximise femto‐users (FUEs) sum data rate while ensuring that certain level of quality of service (QoS) for the macro‐cell users in the vicinity of femtocells is provided. The optimal solution to this problem is obtained by employing the well‐known dual Lagrangian method and the optimal femtocell transmit power and resource allocation solution is derived in detail. However, the optimal solution introduces high computational complexity. To this end, a heuristic solution to the problem is proposed. The algorithms to implement both optimal and efficient suboptimal schemes in a practical system are also given in detail while their complexity is compared. Simulation results show that proposed dynamic resource allocation scheme (a) ensures the macro‐users QoS requirements compared with the Reuse‐1 scheme, where femtocells are allowed to transmit at full power and bandwidth; (b) can maintain FUE data rates at high levels; (c) provides performance close to the optimal solution, while introducing much lower complexity.

In this letter, the performance of differential modulation in simultaneous wireless information and power transfer (SWIPT) cooperative amplify-and-forward (AF) networks is investigated. In particular, we derive novel closed-form expressions for the probability density function (pdf) of the end-to-end signal-to-noise ratio (SNR) and the average bit error rate (ABER) of the considered SWIPT cooperative scenario. Based on the derived results, we analyze the impact of the underlying system parameters on the system performance. Numerical results show that the optimum location of the relay terminal is closer to the source than to the destination. Moreover, it is demonstrated that the value of the power splitting (PS) ratio at the relay significantly impacts the system performance. The results of Monte Carlo simulations are provided to corroborate the analysis.

This paper proposes an asymptotically optimal hybrid beamforming solution for large antenna arrays by exploiting the properties of the singular vectors of the channel matrix. It is shown that the elements of the channel matrix with Rayleigh fading follow a normal distribution when large antenna arrays are employed. The proposed beamforming algorithm is effective in both sparse and rich propagation environments, and is applicable for both point-to-point and multiuser scenarios. In addition, a closed-form expression and a lower bound for the achievable rates are derived when analog and digital phase shifters are employed. It is shown that the performance of the hybrid beamformers using phase shifters with more than 2-bit resolution is comparable with analog phase shifting. A novel phase shifter selection scheme that reduces the power consumption at the phase shifter network is proposed when the wireless channel is modeled by Rayleigh fading. Using this selection scheme, the spectral efficiency can be increased as the power consumption in the phase shifter network reduces. Compared with the scenario that all of the phase shifters are in operation, the simulation results indicate that the spectral efficiency increases when up to 50% of phase shifters are turned OFF.

Abstract This paper presents a comprehensive study of the performance of routing protocols in distributed vehicular networks. We propose a novel and efficient routing protocol, namely cross‐layer, weighted, position‐based routing, which considers link quality, mobility and utilisation of nodes in a cross layer manner to make effective position‐based forwarding decisions. An analytic hierarchy process approach is utilised to combine multiple decision criteria into a single weighting function and to perform a comparative evaluation of the effects of aforementioned criteria on forwarding decisions. Comprehensive simulations are performed in realistic representative urban scenarios with synthetic and real traffic. Insights on the effect of different communication and mobility parameters are obtained. The results demonstrate that the proposed protocol outperforms existing routing protocols for vehicular ad hoc networks, including European Telecommunications Standards Institute (ETSI's) proposed greedy routing protocol, greedy traffic aware routing protocol and advanced greedy forwarding in terms of combined packet delivery ratio, end‐to‐end delay and overhead. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, we analyse the total network en- ergy efficiency (EE) of cellular-WLAN heterogeneous network (HetNet) that employs cell range expansion (CRE) technique, in order to control the user association to either WLAN or cellular network. To this end, we model the system with OFDM based cellular macro-cells and WiFi access points for a saturated (i.e., full-buffer) downlink scenario, considering practical aspects of each type of access technology. Then we evaluate the EE of network by considering realistic power consumption models for each access network type. We compare the performance of the CRE scheme with two benchmark user association schemes; namely, WLAN-first and Max-RSRP (Reference Signal Receive Power). The results demonstrate that CRE with negative biasing performs best in terms of network EE, while the WLAN-first scheme demonstrates the worst performance. However, the CRE with negative biasing lacks fairness in terms of user throughput, while the WLAN-first scheme shows better fairness. Hence, there is a trade-off between the user fairness and the system EE. We show that by optimising the bias factor of each APs individually, with appropriate utility function, a better balance of this trade-off can be achieved.

This paper investigates the downlink resource allocation problem in Orthogonal Frequency Division Multiple Access (OFDMA) Heterogeneous Networks (HetNets) consisting of macrocells and femtocells sharing the same frequency band. The focus is to devise an optimised policy for femtocells' access to the shared spectrum, in terms of femtocell transmissions, in order to keep femto users sum data rate at high levels while ensuring that certain level of quality of service (QoS) for the macro-cell users in the vicinity of femtocells is provided. Both data and control channel constraints are considered, to insure that not only the macro-cell users' data rate demands are meet, but also a certain level of Bit Error Rate (BER) is ensured for the control channel information. The problem is addressed by our proposed linear binary integer programming heuristic algorithm and the performance is compared with the conventional Reuse-1 scheme. Results show a negligible drop in femtocell performance for our proposed scheme, as a trade-off for ensuring all macro users data rate demands when Reuse-1 scheme can even lead up to 40% outage. Discussion is also presented for the implementation possibility of our proposed in a practical LTE network.

The present work is devoted to the analytic performance evaluation of cooperative spectrum sensing (CSS) over generalized fading channels. The proposed analysis is based on the efficient Gaussian-Finite-Mixture (GFM) that allows the derivation of a simple and accurate closed-form expression for the average probability of energy detection (ED) under different fading environments. Capitalizing on this, we derive generalized closed-form expressions for the global probabilities of detection for the CSS with two main hard centralized fusion rules, namely, the AND and the OR rules. The efficiency and usefulness of the proposed expressions is justified by comparing the corresponding complementary receiver operating characteristic (ROC) curves for both multipath and composite multipath/shadowing fading channels, which are otherwise particularly difficult to obtain. The offered analytic results are corroborated by respective results from computer simulations and it is shown that the corresponding performance depends significantly on both the severity of fading and the involved number of users in the collaborative network.

We propose a new antenna selection scheme for a massive MIMO system with a single user terminal and a base station with a large number of antennas. We consider a practical scenario where there is a realistic correlation among the antennas and imperfect channel estimation at the receiver side. The proposed scheme exploits the sparsity of the channel matrix for the effective selection of a limited number of antennas. To this end, we compute a sparse channel matrix by minimising the mean squared error. This optimisation problem is then solved by the well-known orthogonal matching pursuit algorithm. Widely used models for spatial correlation among the antennas and channel estimation errors are considered in this work. Simulation results demonstrate that when the impacts of spatial correlation and imperfect channel estimation introduced, the proposed scheme in the paper can significantly reduce complexity of the receiver, without degrading the system performance compared to the maximum ratio combining.

This study investigates energy efficient allocation of radio resource blocks in orthogonal frequency division multiple access (OFDMA) systems while considering the status of the users’ data buffers to reduce packet dropping rate by the downlink scheduler. The proposed scheme exploits the fluctuations of traffic load to efficiently schedule users’ data packets by reducing the overall energy consumption of the system whenever the status of data buffers permits. From information theory point of view, the proposed scheme exploits the fundamental trade‐off between energy efficiency and spectral efficiency to perform scheduling. First, the problem is formulated as an optimisation problem and then a novel solution, based on dynamic programming, is applied. By comparing the analytical solution with the ones obtained by exhaustive search, it is demonstrated that the proposed scheme is close to the optimal solution, with low computational complexity. In addition, comprehensive simulations are conducted to evaluate the performance of the suggested algorithm. Both analytical and simulation results demonstrate the superiority of the proposed algorithm compared with the well‐known benchmark schemes in terms of energy efficiency and packet dropping rate.

This study proposes a performance analysis of the measurements mechanism applicable to the terminal controlled cell reselection algorithm in a mobile cellular communication system. The cell reselection mechanism decides on which cell, a user equipment (UE) is camped on, when it is in the idle mode. The analysis demonstrates the impact of the neighbour cells measurement threshold setting during the reselection on the serving cell pilot channel quality and a measurement effort in the UE, in terms of the time spent measuring neighbour cells. The authors demonstrate that there is a trade‐off between the cell reselection performance, in terms of the signal quality during the mobility and the battery life in the mobile terminal with different system configurations. Furthermore, a novel approach and enhanced load based measurement threshold configuration scheme is proposed, in order to improve this trade‐off and also the overall system performance and the user experience. Performance analysis results are given in different scenarios to demonstrate the efficacy of the proposed scheme.

This paper examines the uplink of cellular systems employing base station cooperation for joint signal processing. We consider clustered cooperation and investigate effective techniques for managing inter-cluster interference to improve users' performance in terms of both spectral and energy efficiency. We use information theoretic analysis to establish general closed form expressions for the system achievable sum rate and the users' Bit-per-Joule capacity while adopting a realistic user device power consumption model. Two main inter-cluster interference management approaches are identified and studied, i.e., through: 1) spectrum re-use; and 2) users' power control. For the former case, we show that isolating clusters by orthogonal resource allocation is the best strategy. For the latter case, we introduce a mathematically tractable user power control scheme and observe that a green opportunistic transmission strategy can significantly reduce the adverse effects of inter-cluster interference while exploiting the benefits from cooperation. To compare the different approaches in the context of real-world systems and evaluate the effect of key design parameters on the users' energy-spectral efficiency relationship, we fit the analytical expressions into a practical macrocell scenario. Our results demonstrate that significant improvement in terms of both energy and spectral efficiency can be achieved by energy-aware interference management.

The continuous increase in the energy consumption of wireless networks has led to extensive research and development into energy-efficient communications. Towards this objective, this paper employs a novel technique for maximizing the energy efficiency (EE) of wireless networks, using weighted average EE contours with multiple decoding policies (DPs), where users are prioritized based on different criteria such as channel condition. Moreover, our EE based resource allocation method is extended such that other system targets such as rate-fairness and quality of service (QoS) are satisfied. Results indicate that our EE-based resource allocation scheme achieves the highest EE when DP 2 is employed, i.e. the user with the best channel gain achieves its single user bound, whilst other users experience residual interference. Moreover, both the fairness and QoS constraints increase user satisfaction, in terms of achievable data rate, which comes at the cost of a higher transmit power, and therefore lower EE.

In this paper, the analysis for the average bit error probability (ABEP) of a dual-hop fixed-gain relay network is conducted. To this end, we consider two different scenarios: 1) the second hop (relay- destination link) is subject to composite multipath/shadowing and the first hop (source-relay link) experiences only multipath fading; 2) the first hop is perturbed by the composite multipath/shadowing and the second hop undergoes only multipath fading. We develop new and exact closed-form expressions of the ABEP for the first scenario in terms of the Meijer-G and Lommel functions. Since the exact closed-form expressions for the second scenario are mathematically intractable, we derive a new approximation and bounds. These approximation and bounds are shown to be tight for medium to high average signal-to-noise ratio (SNR) regime. In addition, we also provide new and relatively simpler asymptotic expressions of the ABEP for both the scenarios. It is shown that some physical insights (e.g., diversity order) of the system can readily be obtained by using these asymptotic expressions. All our analytical results are corroborated by the Monte-Carlo simulations.

In this paper, we analyze the current binary partition multi-hop broadcast protocol and propose an enhanced solution, namely, Trinary Partition Black-Burst based Broadcast Protocol (3P3B) for Emergency Message (EM) dissemination. 3P3B provides low and constant latency regardless density and size of networks compared to existing solutions. It also enhances message dissemination speed and message progress distance. The main technique in the 3P3B is that 3P3B uses mini-slot DIFS to give a preemptive priority to very urgent EMs and deploys a trinary partition mechanism to select the furthest forwarder of the next communication hop. We prove that 3P3B recues the delay, increases dissemination speed, message progress distance, and outperforms the well-known existing broadcast protocols for EM dissemination in VANET.

This study investigates collaboration among neighbouring base stations for the downlink of multi‐carrier cellular networks, in the absence of a centralised control unit, which is a defining characteristic of future wireless networks. The authors propose a novel scheme for collaboration in resource allocation among a cluster of three neighbouring base stations. In this scheme, the results of an initial calculation are shared among neighbouring cells, then the scheduling decision is made locally and independently by each cell. This scheme does not require complex and iterative calculation. The information is exchanged only once during each scheduling epoch, which results in reduced overhead on the backhaul links. The scheme is implemented in a distributed manner. Simulation‐based performance analysis demonstrates effectiveness of the proposed collaborative resource allocation scheme among the neighbouring base stations for multi‐carrier systems, particularly for the users located near the cell edges.

In this paper, we propose a handover algorithm for WiMAX networks. The algorithm relies on the computation of the received signal-to-noise ratio (SNR) at a Mobile Station (MS) from neighboring Base Stations (BSs) combined with the capacity estimation of the targeting cell. The proposed handover algorithm is implemented by a joint decision between the MS and BS nodes. The performance of the proposed algorithm is evaluated in terms of call dropping ratio and system throughput. A comparison with the conventional hard handover algorithm is presented.

In this paper we present simulation results for our implementation of Adaptive Route Change (ARC) application for cooperative Intelligent Transportation Systems (ITS). The general purpose of the application is to generate recommendations for alternative driving routes in order to avoid traffic congestion. The Adaptive Route Change (ARC) application is implemented in an integrated cooperative ITS simulation platform. For the evaluation we chose a reference scenario defining two distinct traffic flows through an urban area that provides four crossings with traffic light controls. We were interested to evaluate the impacts of ARC on fuel and traffic efficiency. For that we introduced five performance metrics (average trip duration, average fuel consumption, average stop duration, maximum queue size and average queue size behind traffic lights) and evaluated ARC in a series of simulations with varied application penetration rates and traffic volume. The results indicate that ARC systems could reduce traffic congestion in intersections and improve fuel consumption. We observe up to one quarter reduction in average trip time and almost one third reduction in average stop time. Fuel consumption is also reduced by up to 17.3%, while average queue size and maximum queue size reduce more than 50%.

In this paper, we introduce the concept of average per-user rate to the multiuser Multiple-Input, Multiple-Output (MIMO) system with the frequency domain packet scheduler (FDPS) at base stations, which provides an estimate of the rate that the system could provide for each admitted user. The proposed admission control is designed by comparing the user's quality of service (QoS) requirements with the transmission rate that the system can offer. The analytical model is based on the generalized 3GPP LTE downlink transmission for which two Spatial Division Multiplexing (SDM) multiuser MIMO schemes are investigated, namely, Single User (SU) and Multi-user (MU) MIMO schemes. The main contribution of this paper is the derivation of the achievable rate for each user in the SDM MIMO systems based on a mathematical model of the Signal to Interference plus Noise Ratio (SINR) distribution with the frequency domain packet scheduler. The achievable rate provides insights into the system's performance from a different perspective.

Collaboration among neighbouring eNBs in radio resource allocation, in the absence of a centralized control unit, is one of the challenges raised from the flat architecture suggested for the Long Term Evolution (LTE) networks. This paper investigates the system performance of a collaborative resource allocation scheme, in a scenario that consists of two tiers of collaborative Regions (CoR), and considers the gain achieved from the eNB collaboration and performance degradation due to the interference from neighbouring eNBs. Our results indicate that interference introduced from the cells outside the collaborating cluster can have significant impact on the system performance. However, Monte Carlo simulation based performance analysis demonstrates the effectiveness of collaborative resource allocation among adjacent eNBs for the LTE networks.

This paper investigates energy efficiency (EE) performance of a virtual multiple-input multiple- output (MIMO) wireless system using the receiver- side cooperation with the compress-and-forward protocol. We derive a linear approximation of EE as a function of spectral efficiency (SE) in the low SE operation regime. In addition, we obtain a closed-form lower bound for EE which is valid for both low and high SE regions. This lower bound can be used for optimizing the power allocation between the transmitter and the relay in order to minimize the overall energy per bit consumption in the system. Both analytical and simulation results demonstrate that the virtual MIMO system using the receiver-side cooperation outperforms the multiple- input single-output (MISO) case in terms of energy efficiency. Finally we show that, with the optimal power allocation, the virtual-MIMO system achieves an EE performance close to that of an ideal MIMO system.

This paper proposes an analytical model for the throughput of the enhanced distributed channel access (EDCA) mechanism in the IEEE 802.11p medium-access control (MAC) sublayer. Features in EDCA such as different contention windows (CW) and arbitration interframe space (AIFS) for each access category (AC) and internal collisions are taken into account. The analytical model is suitable for both basic access and the request-to-send/clear-to-send (RTS/CTS) access mode. Different from most of existing 3-D or 4-D Markov-chain-based analytical models for IEEE 802.11e EDCA, without computation complexity, the proposed analytical model is explicitly solvable and applies to four access categories of traffic in the IEEE 802.11p. The proposed model can be used for large-scale network analysis and validation of network simulators under saturated traffic conditions. Simulation results are given to demonstrate the accuracy of the analytical model. In addition, we investigate service differentiation capabilities of the IEEE 802.11p MAC sublayer.

This paper investigates the impacts of deploying Mobile Femtocell (MFemtocell) in LET networks. We investigate access delay, capacity, and feedback signalling overhead required for implementation of opportunistic scheduling in LTE cellular networks. We particularly study the impacts of deploying MFemtocells stations on the signalling overhead for opportunistic scheduling. Our system level simulation results indicate that one potential advantage of deploying MFemtocells can contribute to improve spectral efficiency by reducing the amount of feedback signalling.

This paper proposes a Green Light Optimized Speed Advisory (GLOSA) application implementation in a typical reference area, and presents the results of its performance analysis using an integrated cooperative ITS simulation platform. Our interest was to monitor the impacts of GLOSA on fuel and traffic efficiency by introducing metrics for average fuel consumption and average stop time behind a traffic light, respectively. For gathering the results we implemented a traffic scenario defining a single route through an urban area including two traffic lights. The simulations are varied for different penetration rates of GLOSA-equipped vehicles and traffic density. Our results indicate that GLOSA systems could improve fuel consumption and reduce traffic congestion in junctions.

This paper proposes a novel MAC sub-layer mechanism in order to improve reliability of emergency message dissemination in Vehicular Ad-hoc Networks. We consider a VANET that uses IEEE 802.11e MAC sub-layer. We propose enhancements in the multi-access policies of the original IEEE 802.11e standard in order to automatically reduce the volume of data traffic for non-safety applications allowing less contention for safety related message dissemination. Comprehensive simulation results in different scenarios demonstrate the effectiveness of the proposed scheme in terms of packet reception rate and medium access delay for safety related applications.

This paper investigates collaboration among neighboring Base Stations (BSs) in OFDMA based cellular networks in the absence of a centralized control unit, which is a defining characteristic of 4G wireless networks. We propose a novel scheme for collaboration between the base stations. Monte Carlo simulation based performance analysis demonstrates effectiveness of collaborative resource allocation among adjacent base stations for OFDMA systems, particularly for the users in the cell edges.

In this paper, we analyze the effect of feedback delay and channel estimation errors on the performance of a decode-and-forward (DF) cooperative transmission scenario with relay selection. In our relay selection scheme, only one relay with the best relay-to-destination (R → D) channel quality is selected among the set of relays that decode the source information correctly. Specifically, the destination terminal first estimates the channel state information (CSI) of all active R → D links and then sends the index of the best relay to the relay terminals via a delayed feedback link. Due to the time varying nature of the fading channels, selection is performed based on the old version of the channel estimate. Closed-form expressions for the outage probability, average capacity and average symbol error rate (ASER) are derived. Through asymptotic diversity order analysis, we show that the presence of feedback delay reduces the asymptotic diversity order to one, while the effect of channel estimation errors reduces it to zero. Finally, simulation results are presented to corroborate the analytical results.

ABSTRACT This paper studies the impacts of vehicular communications on efficiency of traffic in urban areas. We consider a Green Light Optimized Speed Advisory application implementation in a typical reference area and present the results of its performance analysis using an integrated cooperative intelligent transportation systems simulation platform. In addition, we study route alternation using vehicle‐to‐infrastructure and vehicle‐to‐vehicle communications. Our interest was to monitor the impacts of these applications on fuel and traffic efficiency by introducing metrics for average fuel consumption, average stop time behind a traffic light and average trip time, respectively. For gathering the results, we implemented two traffic scenarios defining routes through an urban area including traffic lights. The simulations are varied for different penetration rates of application‐equipped vehicles, driver's compliance to the advised speed and traffic density. Our results indicate that Green Light Optimized Speed Advisory systems could improve fuel consumption, reduce traffic congestion in junctions and the total trip time. Copyright © 2011 John Wiley & Sons, Ltd.

Planning and optimization of 3G networks is more than just frequency allocation and coverage planning, due to the nature of WCDMA coding. It usually involves solution of an NP-Hard problem. In this paper we propose an effective method for optimizing the Common Pilot Channel (CPICH) transmit power, along with maximizing the number of served users and minimizing the number of cell sites and compare use of two meta-heuristic methods: Taboo Search (TS) and Genetic Algorithm (GA) in planning and optimization of UMTS radio networks.

this paper investigates and proposes the techniques that reduce the number of required feedback channels for implementation of opportunistic scheduling. It is shown that by implementing effective schemes we can exploit significant multiuser diversity gain using a modest number of feedback channels. Simulation based performance analysis is given to demonstrate effectiveness of the proposed schemes in terms of high spectrum efficiency and low feedback overhead.

This paper proposes an analytical model for the throughput of the Enhanced Distributed Channel Access (EDCA)mechanism in IEEE 802.11p MAC sub-layer. Features in EDCA such as different Contention Windows (CW) and Arbitration Interframe Space (AIFS) for each Access Category (AC), and internal collisions are taken into account. The analytical model is suitable for both basic access and the Request-To-Send/Clear-To-Send (RTS/CTS) access mode. The proposed analytical model is validated against simulation results to demonstrate its accuracy.

Fair weights have been implemented to maintain fairness in recent resource allocation schemes. However, designing fair weights for multiservice wireless networks is not trivial because users' rate requirements are heterogeneous and their channel gains are variable. In this paper, we design fair weights for opportunistic scheduling of heterogeneous traffic in orthogonal frequency division multiple access (OFDMA) networks. The fair weights determine each user's share of rate for maintaining a utility notion of fairness. We then present a scheduling scheme which enforces users' long term average transmission rates to be proportional to the fair weights. The proposed scheduler takes the advantage of users' channel state information and the inherent flexibility of OFDMA resource allocation for efficient resource utilization. Furthermore, using the fair weights allows flexibility for realization of different scheduling schemes which accommodate a variety of requirements in terms of heterogeneous traffic types and user mobility. Simulation based performance analysis is presented to demonstrate efficacy of the proposed solution in this paper.

Abstract In this paper, we propose a finite‐state Markov model for per‐user service of an opportunistic scheduling scheme over Rayleigh fading channels, where a single base station serves an arbitrary number of users. By approximating the power gain of Rayleigh fading channels as finite‐state Markov processes, we develop an algorithm to obtain dynamic stochastic model of the transmission service, received by an individual user for a saturated scenario, where user data queues are highly loaded. The proposed analytical model is a finite‐state Markov process. We provide a comprehensive comparison between the predicted results by the proposed analytical model and the simulation results, which demonstrate a high degree of match between the two sets. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract In this paper, a rate‐based admission control scheme for a single shared wireless base station with opportunistic scheduling and adaptive modulation and coding (AMC) is proposed. The proposed admission scheme maintains minimum average rates of the admitted users, i.e., new users will be admitted if the base station has enough resources to support the required minimum average transmission rates of all users. The proposed scheme relies on an analytical model for the average per‐user rates of an opportunistic scheduling in an unsaturated scenario, where some queues may be empty for certain periods of time. We provide extensive simulation results to demonstrate the accuracy of the base analytical model on which our admission scheme relies. Copyright © 2009 John Wiley & Sons, Ltd.

Maintaining fairness using weighting factors is a common approach in resource allocation. However, computing weighting factors for multiservice wireless networks is not trivial because users' rate requirements are heterogeneous and their channel gains are variable. In this paper, we propose weighting factor computation and scheduling schemes for orthogonal frequency division multiple access (OFDMA) networks. The weighting factor computation scheme determines each user's share of rate for maintaining a utility notion of fairness. We then present a scheduling scheme which takes the users' weighting factors into consideration to allocate sub-carriers and power in OFDMA networks. The simulation results demonstrate that the proposed scheduling scheme outperforms an opportunistic scheme in terms of fairness performance in different scenarios, where the users are fixed or mobile.

In this paper, we consider maximum rate opportunistic scheduling from a single wireless base station with a single antenna to multiple mobile users, each equipped with a single antenna. We show that a finite-state Markovian model can capture the dynamics of a single user's service, namely peruser service. We consider a saturated scenario, where the base station always has buffered data for transmission.

A novel approach for implementation of opportunistic scheduling without explicit feedback channels is proposed in this paper, which exploits the existing ARQ signals instead of feedback channels to reduce the complexity of implementation. Monte Carlo simulation results demonstrate the efficacy of the proposed approach in harvesting multiuser diversity gain. The proposed approach enables implementation of opportunistic scheduling in a variety of wireless networks, such as the IEEE 802.11, without feedback facilities for collecting partial channel state information from users.

Abstract A point‐to‐point quantum key distribution (QKD) system takes advantage of the laws of quantum physics to establish secret keys between two communicating parties. Compared to the classical methods, such as public‐key infrastructures, QKD offers unconditional security, which makes it attractive for very high security applications. However, this unprecedent level of security is mitigated by the inherent constraints of quantum communications, such as the limited rates and ranges of an individual point‐to‐point QKD link. A QKD network, which can be built by combining multiple point‐to‐point QKD devices, can alleviate the constraints and enable point‐to‐multi‐point key distribution based on QKD technology. The European project, secure communication based on quantum cryptography (SeCoQC) aims at deploying a prototype QKD network, which will be demonstrated in September 2008, by developing the architecture and the protocols, as well as the specific hardware for long‐range QKD networks. This paper discusses the important aspects of the architecture and the network layer protocols of the SeCoQC QKD network. Copyright © 2008 John Wiley & Sons, Ltd.

Quantum key distribution (QKD) is an alternative key distribution technique that, unlike the classical approaches, can provide unconditionally secure keys for data communications over public communication networks. The European project Secoqc (secure communication based on quantum cryptography) aims at developing a global network for unconditionally secure key distribution. This paper specifies the major elements of the transport layer protocols of the Secoqc QKD network.

Abstract Efficient packet scheduling in CDMA cellular networks is a challenging problem due to the time variant and stochastic nature of the channel fading process. Selection diversity is one of the most effective techniques utilizing random and independent variations of diverse channels to improve the performance of communication over fading channels. In this paper, we propose two packet scheduling schemes exploiting base station selection diversity in the downlink of CDMA cellular networks. The proposed schemes rely on the limited instantaneous channel state information (CSI) to select the best user from the best serving base station at each time slot. This technique increases the system throughput by increasing multiuser diversity gain and reducing the effective interference among adjacent base stations. Results of Monte Carlo simulations are given to demonstrate the improvement of system throughput using the proposed scheduling schemes. In addition, we investigate fairness issue of wireless scheduling schemes. Due to different characteristics of wireless scheduling schemes, the existing fairness indexes may result in misleading comparison among different schemes. We propose a new fairness index to compare the overall satisfaction of the network users for different scheduling schemes. Copyright © 2006 John Wiley & Sons, Ltd.

In this paper, we propose two analytical models for per-user throughput of an opportunistic scheduling scheme over a broadcast fading channel. For the first model, we use a piecewise linear approximation of the achievable transmission rates versus the values of Signal to Noise and Interference Ratio (SINR). We obtain the conditional average transmission rate of a mobile station, given the maximum channel quality of the other competing mobile stations. Using the probability distribution function of the maximum channel quality of the competing mobile stations, we obtain a closed form unconditional average transmission rate, i.e., per-user throughput, of a mobile station. For the second model, we use a similar approach, but with a precise model of the achievable rates. Furthermore, statistically nonidentical channels for different mobile stations are considered. Thus, the second model is more general and provides more accurate solution, but it requires more computations. The proposed models are useful for call admission control as well as performance studies of wireless networks. Simulation results are given to demonstrate the accuracy of the proposed analytical models.

The two-state Markov chain has been widely used to model fading channels in the performance study of upper-layer communication protocols in wireless networks. It can be used to model transmission success/failure based on the physical characteristics of the transmission channel. However, for shared wireless links, packet transmission depends on both the status of the link and the scheduling strategy used. In this poster, we propose a novel four-state Markov model, which takes into consideration the impacts of channel fading and scheduling on packet transmission over shared wireless links. It is further abstracted to an effective two-state Markov chain to facilitate analytical performance evaluation. To demonstrate the efficacy of the proposed model, we apply it to study the throughput, delay and delay jitter of a saturated traffic source, and the packet dropping probability at the network layer for data traffic under a buffer overflow dropping policy. Simulation results to demonstrate the reasonableness of the proposed model are also presented. © 2006 ACM.

In this paper, we propose a novel scheduling scheme for the downlink of IEEE 802.16 networks. A scheduler at the Base Station (BS) decides the order of downlink bursts to be transmitted. The decision is made based on the quality of the channel and the history of transmissions of each Subscriber Station (SS). The scheduler takes advantage of temporal channel fluctuations to increase the BS's throughput and maintain fairness by balancing the long term average throughput of SSs. Simulation results are given to demonstrate the performance of the proposed scheduling scheme.

Efficient packet scheduling in CDMA cellular networks is a challenging problem due to the time variant and stochastic nature of the channel fading process. Selection diversity is one of the most effective techniques utilizing random and independent variations of diverse channels to improve the performance of communication over fading channels. Exploiting base station selection diversity, in this paper, we propose two scheduling schemes for the downlink of CDMA cellular networks. The proposed schemes rely on the limited instantaneous Channel State Information to transmit to the best user from the best serving base station in each time slot. This technique increases the system throughput by increasing multi-user diversity gain and reducing the effective interference among adjacent base stations. Results of Monte Carlo simulations are given to demonstrate the improvement of system throughput using the proposed scheduling schemes. We also investigate the issue of fairness analysis of wireless scheduling schemes. Due to the unique characteristics of wireless scheduling schemes, the existing fairness indexes fail to provide a proper comparison among different scheduling schemes. We propose a new fairness index to compare the overall satisfaction of the network users among different wireless scheduling schemes. This approach complies with the definition of max-min fairness which is a widely accepted notion of fairness for data communication networks.

In wireless channels, the bursty nature of block errors render immediate packet retransmissions at the link level ineffective. Cooperative communication is a promising technique to combat the negative impacts of channel fading by providing diverse channels between peers in wireless ad-hoc networks. In this paper, an analytical model is proposed for the throughput of the node cooperative automatic repeat request scheme for wireless ad-hoc networks. The model is based on a two-state Markov model for block errors in the wireless fading channels. Simulation results are given to demonstrate effectiveness of the analytical model

In this paper, we investigate the measurement of fairness, discuss well known fairness notions, and propose a new utility-based framework to evaluate the degree of fairness of resource allocation schemes in wireless access networks. The proposed framework has certain desirable features. It offers clear definitions and relevant methodology, takes into account both effort and service unfairness, and can be customized for different application types with different QoS requirements. Numerical examples and case studies are given to demonstrate the effectiveness of the proposed framework.