• The novel xApp distillation method transforms conflicting xApps into a single enhanced model, reducing network outages by up to 83.3% compared to team learning approaches while maintaining consistent service quality. • Unlike conventional O-RAN conflict mitigation that discards valuable information, xApp distillation leverages knowledge from multiple xApps (ML-based and heuristic) to create a comprehensive decision-making model that eliminates inter-xApp conflicts. • The proposed approach enables telecommunication providers to extract value from separately purchased xApps through policy distillation, creating a scalable solution that preserves specialized capabilities while improving overall network. The advancements of machine learning-based xApps in Open Radio Access Network (O-RAN) have created research and industrial opportunities. One of the major advantages of Machine Learning (ML)-based xApps over heuristic methods is the ability to learn the dynamics of the environment and predict upcoming instances. Typically, xApps are trained and fine-tuned for specific objectives. However, telecommunication companies often deploy multiple xApps in overlapping areas. Given the different design objectives of xApps, this deployment strategy can lead to conflicts. Current conflict mitigation schemes proposed by the O-RAN Alliance are rule-based, either ignoring some of the xApps or rolling back their actions. This leads to performing the same action for a different state of the network, resulting in suboptimal mitigation. To prevent this suboptimal mitigation, we propose the xApp distillation method. The proposed method distils historical network state, actions and outcome information from multiple xApps (either heuristic or ML-based xApps) and uses this knowledge to train a single model that has retained the capabilities of previous xApps. The simulation results show that xApp distillation has significantly more consistent performance than conventional conflict mitigation methods. Compared conflict mitigation schemes can cause up to 6 times more network outages than xApp distillation in some cases.

Joint Communication and Sensing (JCAS) integrates communication and sensing (C&S) functions into a single system by sharing the same transmit signal. This paper focuses on a radar-centric JCAS system that transmits probing signals to detect targets while delivering communication data to users on the downlink. We aim to design a joint waveform that achieves three objectives: minimizing the mismatch between the desired and actual JCAS beam patterns, reducing multi-user interference (MUI), and adhering to per-antenna power constraints. We propose a waveform design based on multiobjective optimization to address the challenges associated with waveform design, highlighting the trade-offs between sensing and communication performance. To establish a balance between C&S performance, we introduce a trade-off control parameter. Initially, we reformulate the waveform design problem as an unconstrained problem and then present a new low-complexity waveform synthesis algorithm using gradient descent (GD). Finally, numerical results demonstrate the impact of the trade-off control parameter and demonstrate the effectiveness of our proposed algorithm compared to state-of-the-art techniques, ensuring optimal performance for both C&S in JCAS systems.

The Radio Access Network (RAN) is a critical component of modern telecommunications infrastructure, currently evolving towards disaggregated and open architectures. These advancements are pivotal for integrating intelligent, data-driven applications aimed at enhancing network reliability and operational autonomy through the introduction of cognitive capabilities, as exemplified by the emerging Open Radio Access Network (O-RAN) standards. Despite its potential, the nascent nature of O-RAN technology presents challenges, primarily due to the absence of mature operational standards. This complicates the management of data and intelligent applications, particularly when integrating with traditional network management and operational support systems. Divergent vendor-specific design approaches further hinder migration and limit solution reusability. These challenges are compounded by a skills gap in telecommunications business-oriented engineering, which remains a key barrier to effective O-RAN deployment and intelligent application development. To address these challenges, Boldyn Networks developed a novel cloud-native data analytics platform, specifically designed to support scalable Artificial Intelligence (AI) integration within O-RAN deployments. This platform underwent rigorous testing in real-world scenarios, and applied advanced AI techniques to improve operational efficiency and customer experience. Implementation involved adopting Development Operations (DevOps) practices, leveraging data lakehouse architectures tailored for AI applications, and employing sophisticated data engineering strategies. The platform successfully addresses connectivity challenges inherent in real-world offshore wind farm deployments using Long Short-Term Memory (LSTM) models for anomaly detection in network connectivity. After integrating the LSTM models into the network control, more than 90 percent of connectivity issues were reduced in runtime. This marks a step toward autonomous, self-organizing, and self-healing networks.

We apply Federated Learning (FL) to the problem of indoor localisation in a real-world multiple residential house scenario. Fingerprinting of the Received Signal Strength Indicator (RSSI) was used as the localisation method. We show that, given the minimal amount of fine-tuning allowed by constraint on the size of the gradient step in the fit round of FL, a shared model learned this way has strong performance on all houses and is stable with respect to randomness in weight initialisation. Not unexpectedly, the performance is inferior to an individual learning approach. We developed a tuned FL approach - a finetuning step in every round of FL that only affects a subset of the clients' parameters while leaving a common ‘backbone’ unchanged. The FL clients were able to accept the model weights post-tuning or revert to the weights in the previous evaluation round based on their local validation set. Through our extensive evaluation, our results indicate a significant reduction in the performance gap between a completely individual ML and a benchmark traditional FL approach.

This paper presents an achievability bound that evaluates the exact probability of error of an ensemble of random codes that are decoded by a minimum distance decoder. Compared to the state-of-the-art which demands exponential computation time, this bound is evaluated in polynomial time. This improvement in complexity is also attainable for the original random coding bound that utilizes an information density decoder. The general bound is particularized for the binary symmetric channel, the binary erasure channel, and the Gaussian channel.

Security is a major challenge in Internet-of-Things (IoT) systems, and network intrusion detection systems (NIDS) play a key role in proposed solutions. In this paper, we propose VarSec which is a centralised unsupervised algorithm for network anomaly detection. It uses two variational autoencoders (VAEs) to process packet-level data, and combines their output to detect anomalous packets. We evaluate the performance of our proposed algorithm on a variety of attack datasets and compare it with existing solutions.

Consider$n$nodes communicating over an unreliable broadcast channel. Each node has a single packet that needs to be communicated to all other nodes. Time is slotted, and a time slot is long enough for each node to broadcast one packet. Each broadcast reaches a random subset of nodes. The objective is to minimise the time until all nodes have received all packets. We study two schemes, (i) random relaying, and (ii) random linear network coding, and analyse their performance in an asymptotic regime in which$n$tends to infinity. Simulation results for a wide range of$n$are also presented.

The feasibility of integrating the temporal nature of the Bluetooth Low Energy (BLE) Received Signal Strength Indicator (RSSI) into a self-supervised machine learning model for room-level and sub-room-level localization in a realistic residential setting is investigated. The signal is transmitted by a wearable wrist watch and received by multiple access points acting as receivers communicating via the BLE standard. It is found that while the baseline room-level accuracy is sufficiently high for practical applications in rooms separated by a wall, confusion can occur between non-adjacent rooms and thus lower localization performance. Two approaches are explored that exploit the time dimension of the data to mitigate this problem: maximum likelihood estimation in a conditional random field model, and self-supervised contrastive learning based on temporal proximity. Using a real world dataset collected in residential homes, we develop the approaches on the data collected in one residence before evaluating them on data collected in another. On the evaluation residence, we find that conditional random fields do not improve upon the baseline in terms of the weighted F1 score, while contrastive learning leads to an improvement in localization performance.

Measuring customer experience on mobile data is of utmost importance for global mobile operators. The reference signal received power (RSRP) is one of the important indicators for current mobile network management, evaluation and monitoring. Radio data gathered through the minimization of drive test (MDT), a 3GPP standard technique, is commonly used for radio network analysis. Collecting MDT data in different geographical areas is inefficient and constrained by the terrain conditions and user presence, hence is not an adequate technique for dynamic radio environments. In this paper, we study a generative model for RSRP prediction, exploiting MDT data and a digital twin (DT), and propose a data-driven, two-tier neural network (NN) model. In the first tier, environmental information related to user equipment (UE), base stations (BS) and network key performance indicators (KPI) are extracted through a variational autoencoder (VAE). The second tier is designed as a likelihood model. Here, the environmental features and real MDT data features are adopted, formulating an integrated training process. On validation, our proposed model that uses real-world data demonstrates an accuracy improvement of about 20% or more compared with the empirical model and about 10% when compared with a fully connected prediction network.

The vision of 5G lies in providing high data rates, low latency (for the aim of near-real-time applications), significantly increased base station capacity, and near-perfect quality of service (QoS) for users, compared to LTE networks. In order to provide such services, 5G systems will support various combinations of access technologies such as LTE, NR, NR-U and Wi-Fi. Each radio access technology (RAT) provides different types of access, and these should be allocated and managed optimally among the users. Besides resource management, 5G systems will also support a dual connectivity service. The orchestration of the network therefore becomes a more difficult problem for system managers with respect to legacy access technologies. In this paper, we propose an algorithm for RAT allocation based on federated meta-learning (FML), which enables RAN intelligent controllers (RICs) to adapt more quickly to dynamically changing environments. We have designed a simulation environment which contains LTE and 5G NR service technologies. In the simulation, our objective is to fulfil UE demands within the deadline of transmission to provide higher QoS values. We compared our proposed algorithm with a single RL agent, the Reptile algorithm and a rule-based heuristic method. Simulation results show that the proposed FML method achieves higher caching rates at first deployment round 21% and 12% respectively. Moreover, proposed approach adapts to new tasks and environments most quickly amongst the compared methods.

Setting the transmit power setting of 5G cells has been a long-term topic of discussion, as optimized power settings can help reduce interference and improve the quality of service to users. Recently, machine learning (ML)-based, especially reinforcement learning (RL)-based control methods have received much attention. However, there is little discussion about the generalisation ability of the trained RL models. This paper points out that an RL agent trained in a specific indoor environment is room-dependent, and cannot directly serve new heterogeneous environments. Therefore, in the context of Open Radio Access Network (O-RAN), this paper proposes a distributed cell power-control scheme based on Federated Reinforcement Learning (FRL). Models in different indoor environments are aggregated to the global model during the training process, and then the central server broadcasts the updated model back to each client. The model will also be used as the base model for adaptive training in the new environment. The simulation results show that the FRL model has similar performance to a single RL agent, and both are better than the random power allocation method and exhaustive search method. The results of the generalisation test show that using the FRL model as the base model improves the convergence speed of the model in the new environment.

The use of Wi-Fi signals for human sensing has gained significant interest over the past decade. Such techniques provide affordable and reliable solutions for healthcare-focused events such as vital sign detection, prevention of falls and long-term monitoring of chronic diseases, among others. Currently, there are two major approaches for Wi-Fi sensing: (1) passive Wi-Fi radar (PWR) which uses well established techniques from bistatic radar, and channel state information (CSI) based wireless sensing (SENS) which exploits human-induced variations in the communication channel between a pair of transmitter and receiver. However, there has not been a comprehensive study to understand and compare the differences in terms of effectiveness and limitations in real-world deployment. In this paper, we present the fundamentals of the two systems with associated methodologies and signal processing. A thorough measurement campaign was carried out to evaluate the human activity detection performance of both systems. Experimental results show that SENS system provides better detection performance in a line-of-sight (LoS) condition, whereas PWR system performs better in a non-LoS (NLoS) setting. Furthermore, based on our findings, we recommend that future Wi-Fi sensing applications should leverage the advantages from both PWR and SENS systems.

The indoor localization and tracking of objects and humans with high accuracy is becoming increasingly important in a number of applications including healthcare, ambient assistant-living, surveillance, among others. Since Ultra-wideband (UWB) systems have a large operating bandwidth, they can provide centimeter (cm) level localization accuracy in Line-of-Sight (LoS) scenarios. However, current commercial UWB systems require the target to carry an active device (tag) so that it can be precisely located and this may be impractical in applications such as security and surveillance. In this work, we process experimental data obtained from a multi-static UWB radar network for the passive indoor localization of a moving target. Current fingerprinting-based passive localization techniques require a substantial radio-map survey in the offline training phase and labor-intensive fingerprint updates when there are changes in the environment. In this work, on the other hand, we propose to use the fine-grained physical layer information, known as Channel Impulse response (CIR), that is exchanged between UWB modules and capitalize on the fact that a moving person induces variations in the CIR that stand out against the background signal. Our results show that a walking target can be passively located with a median distance error as low as 0.55m in an indoor environment.

Edge computing is rapidly changing the IoT-Cloud landscape. Various testbeds are now able to run multiple Docker-like containers developed and deployed by end-users on edge devices. However, this capability may allow an attacker to deploy a malicious container on the host and compromise it. This paper presents a dataset based on the Linux Auditing System, which contains malicious and benign container activity. We developed two malicious scenarios, a denial of service and a privilege escalation attack, where an adversary uses a container to compromise the edge device. Furthermore, we deployed benign user containers to run in parallel with the malicious containers. Container activity can be captured through the host system via system calls. Our time series auditd dataset contains partial labels for the benign and malicious related system calls. Generating the dataset is largely automated using a provided AutoCES framework. We also present a semi-supervised machine learning use case with the collected data to demonstrate its utility. The dataset and framework code are open-source and publicly available.

Within this work, the challenge of developing maintenance planning solutions for networked assets is considered. This is challenging due to the very nature of these systems which are often heterogeneous, distributed and have complex co-dependencies between the constituent components for effective operation. We develop a Multi-Agent Reinforcement Learning (MARL) solution for this domain and apply it to a simulated Radio Access Network (RAN) comprising of nine Base Stations (BS). Through empirical evaluation we show that our model outperforms fixed corrective and preventive maintenance policies in terms of network availability whilst generally utilizing less than or equal amounts of maintenance resource.

This paper presents Vesta, a digital health platform composed of a smart home in a box for data collection and a machine learning based analytic system for deriving health indicators using activity recognition, sleep analysis and indoor localization. This system has been deployed in the homes of 40 patients undergoing a heart valve intervention in the United Kingdom (UK) as part of the EurValve project, measuring patients health and well-being before and after their operation. In this work a cohort of 20 patients are analyzed, and 2 patients are analyzed in detail as example case studies. A quantitative evaluation of the platform is provided using patient collected data, as well as a comparison using standardized Patient Reported Outcome Measures (PROMs) which are commonly used in hospitals, and a custom survey. It is shown how the ubiquitous in-home Vesta platform can increase clinical confidence in self-reported patient feedback. Demonstrating its suitability for digital health studies, Vesta provides deeper insight into the health, well-being and recovery of patients within their home.

Human sensing using WiFi signal transmissions is attracting significant attention for future applications in e-healthcare, security, and the Internet of Things (IoT). The majority of WiFi sensing systems are based around processing of channel state information (CSI) data which originates from commodity WiFi access points (APs) that have been primed to transmit high data-rate signals with high repetition frequencies. However, in reality, WiFi APs do not transmit in such a continuous uninterrupted fashion, especially when there are no users on the communication network. To this end, we have developed a passive WiFi radar system for human sensing which exploits WiFi signals irrespective of whether the WiFi AP is transmitting continuous high data-rate Orthogonal Frequency-Division Multiplexing (OFDM) signals, or periodic WiFi beacon signals while in an idle status (no users on the WiFi network). In a data transmission phase, we employ the standard cross ambiguity function (CAF) processing to extract Doppler information relating to the target, while a modified version is used for lower data-rate signals. In addition, we investigate the utility of an external device that has been developed to stimulate idle WiFi APs to transmit usable signals without requiring any type of user authentication on the WiFi network. In this article, we present experimental data which verifies our proposed methods for using any type of signal transmission from a standalone WiFi device, and demonstrate the capability for human activity sensing.

Wireless localisation is becoming increasingly important in various applications such as smart homes, elderly healthcare facilities and in industry where centimetre (cm) level localisation accuracy is desired. Ultra-wideband (UWB) systems can be used for such applications since they can achieve a ranging precision below 10 cm in a Line-of-Sight (LoS) setup. However, in non LoS(NLoS) scenarios these systems provide a lower accuracy. In this paper, we exploit the high resolution Channel Impulse Response (CIR) provided by the Decawave EVK1000 boards for localisation in a residential environment. We employ a single anchor node and use the CIR obtained from five different locations as fingerprints to investigate whether the location of the tag can be accurately estimated in NLoS scenarios. Our investigation showed that the CIR can be effectively used as fingerprints to provide a location classification accuracy as high as 98% when the environment remains relatively stable. However, using the CIR data recorded in a second experiment (same setup as first experiment) as test data and applying the trained model of the first experiment to it showed a significant degradation in performance (50-60% accuracy) due to the changes in the environment. On the other hand, by using five features extracted from the UWB signals for location classification, an accuracy in excess of 99% is obtained during testing in both experiments.

Passive wireless sensing using WiFi signals has become a very active area of research over the past few years. Such techniques provide a cost-effective and non-intrusive solution for human activity sensing especially in healthcare applications. One of the main approaches used in wireless sensing is based on fine-grained WiFi Channel State Information (CSI) which can be extracted from commercial Network Interface Cards (NICs). In this paper, we present a new signal processing pipeline required for effective wireless sensing. An experiment involving five participants performing six different activities was carried out in an office space to evaluate the performance of activity recognition using WiFi CSI in different physical layouts. Experimental results show that the CSI system has the best detection performance when activities are performed half-way in between the transmitter and receiver in a line-of-sight (LoS) setting. In this case, an accuracy as high as 91% is achieved while the accuracy for the case where the transmitter and receiver are co-located is around 62%. As for the case when data from all layouts is combined, which better reflects the real-world scenario, the accuracy is around 67%. The results showed that the activity detection performance is dependent not only on the locations of the transmitter and receiver but also on the positioning of the person performing the activity.

This paper studies the indoor localisation of WiFi devices based on a commodity chipset and standard channel sounding. First, we present a novel shallow neural network (SNN) in which features are extracted from the channel state information (CSI) corresponding to WiFi subcarriers received on different antennas and used to train the model. The single-layer architecture of this localisation neural network makes it lightweight and easy-to-deploy on devices with stringent constraints on computational resources. We further investigate for localisation the use of deep learning models and design novel architectures for convolutional neural network (CNN) and long-short term memory (LSTM). We extensively evaluate these localisation algorithms for continuous tracking in indoor environments. Experimental results prove that even an SNN model, after a careful handcrafted feature extraction, can achieve accurate localisation. Meanwhile, using a well-organised architecture, the neural network models can be trained directly with raw data from the CSI and localisation features can be automatically extracted to achieve accurate position estimates. We also found that the performance of neural network-based methods are directly affected by the number of anchor access points (APs) regardless of their structure. With three APs, all neural network models proposed in this paper can obtain localisation accuracy of around 0.5 metres. In addition the proposed deep NN architecture reduces the data pre-processing time by 6.5 hours compared with a shallow NN using the data collected in our testbed. In the deployment phase, the inference time is also significantly reduced to 0.1 ms per sample. We also demonstrate the generalisation capability of the proposed method by evaluating models using different target movement characteristics to the ones in which they were trained.

Deep clustering has increasingly been demonstrating superiority over conventional shallow clustering algorithms. Deep clustering algorithms usually combine representation learning with deep neural networks to achieve this performance, typically optimizing a clustering and non-clustering loss. In such cases, an autoencoder is typically connected with a clustering network, and the final clustering is jointly learned by both the autoencoder and clustering network. Instead, we propose to learn an autoencoded embedding and then search this further for the underlying manifold. For simplicity, we then cluster this with a shallow clustering algorithm, rather than a deeper network. We study a number of local and global manifold learning methods on both the raw data and autoencoded embedding, concluding that UMAP in our framework is able to find the best clusterable manifold of the embedding. This suggests that local manifold learning on an autoencoded embedding is effective for discovering higher quality clusters. We quantitatively show across a range of image and time-series datasets that our method has competitive performance against the latest deep clustering algorithms, including outperforming current state-of-the-art on several. We postulate that these results show a promising research direction for deep clustering. The code can be found at https://github.com/rymc/n2d.

In a world where Artificial Intelligence revolutionizes inference, prediction and decision-making tasks, Digital Twins emerge as game-changing tools. A case in point is the development and optimization of Cooperative Intelligent Transportation Systems (C-ITSs): a confluence of cyber-physical digital infrastructure and (semi)automated mobility. Herein we introduce Digital Twin for self-dRiving Intelligent VEhicles (DRIVE). The developed framework tackles shortcomings of traditional vehicular and network simulators. It provides a flexible, modular, and scalable implementation to ensure large-scale, city-wide experimentation with a moderate computational cost. The defining feature of our Digital Twin is a unique architecture allowing for submission of sequential queries, to which the Digital Twin provides instantaneous responses with the "state of the world", and hence is an Oracle. With such bidirectional interaction with external intelligent agents and realistic mobility traces, DRIVE provides the environment for development, training and optimization of Machine Learning based C-ITS solutions.

Automated Vehicles are an integral part of Intelligent Transportation Systems (ITSs) and are expected to play a crucial role in the future mobility services. This paper investigates two classes of self-driving vehicles: (i) Level 4&5 Automated Vehicles (AVs) that rely solely on their on-board sensors for environmental perception tasks, and (ii) Connected and Automated Vehicles (CAVs), leveraging connectivity to further enhance perception via driving intention and sensor information sharing. Our investigation considers and quantifies the impact of each vehicle group in large urban road networks in Europe and in the USA. The key performance metrics are the traffic congestion, average speed and average trip time. Specifically, the numerical studies show that the traffic congestion can be reduced by up to a factor of four, while the average flow speeds of CAV group remains closer to the speed limits and can be up to 300% greater than the human-driven vehicles. Finally, traffic situations are also studied, indicating that even a small market penetration of CAVs will have a substantial net positive effect on the traffic flows.

The emerging Internet of Things has the potential to solve major societal challenges associated with healthcare provision. Low-power wireless protocols for residential Health Internet of Things applications are characterized by high reliability requirements, the need for energy-efficient operation, and the need to operate robustly in diverse environments in the presence of external interference. We enhance and experimentally evaluate the Time-Slotted Channel Hopping protocol from the IEEE 802.15.4 standard to address these challenges. Our contributions are a new schedule and an adaptive channel selection mechanism to increase the performance of time-slotted channel hopping in this domain. Evaluation in a test house shows that the enhanced system is suitable for our e-Health application and compares favorably with state-of-the-art options. The schedule provides higher reliability compared with the minimal scheduling function from the IETF 6TiSCH Working Group and has a better energy-efficiency/reliability tradeoff than the Orchestra scheduler. Results from 29 long-term residential deployments confirm the suitability for the application and show that the system is able to adapt and avoid channels used by WiFi. In these uncontrolled environments, the system achieves 99.96% average reliability for networks that generate 7.5 packets per second on average.

WiFi signals for physical activity sensing show great practical potentials for pervasive healthcare applications due to the widespread WiFi deployments and high levels of public acceptance of such systems. Traditionally, WiFi-based sensing uses the Channel State Information (CSI) from an off-the-shelf WiFi Access Point (AP) which transmits signals that have high pulse repetition frequencies. However, when there are no users on the local network only beacon signals are transmitted from the WiFi AP which significantly deteriorates the sensitivity and specificity of such systems. Surprisingly, WiFi based sensing under these conditions have received little attention given that WiFi APs are frequently in idle state. This paper presents a practical system based on passive radar techniques which does not require any special setup or firmware changes to be able to work with any commercial WiFi device. To cope with the low duty cycles associated with beacon signal transmissions, a modified Cross Ambiguity Function (CAF) has been proposed to reduce redundant samples. In addition, an external device has been developed to send WiFi probe request signals which stimulates an idle AP to transmit WiFi probe responses, thus generate usable transmission signals for sensing applications without the need to authenticate and join the network. Detection performance shows that the proposed concept can significantly improve activity detection and is a viable candidate in future healthcare applications.

Security has traditionally been dealt with at layers higher than the physical layer but in the wake of 5G, security at all layers is necessary to deal with the variations in complexity of connected devices. Low power devices may use physical layer security as a solution, while other devices may use physical layer security to complement security at higher layers. One key metric for physical layer security is the secrecy capacity. This is the maximum rate that a system can transmit with perfect secrecy. Multiple Input Multiple Output (MIMO) and Massive MIMO systems look likely to play a part in 5G, but the secrecy capacity for such systems is not fully understood. For a Gaussian MIMO channel, the secrecy capacity is a non-convex optimisation problem for which a general solution is not available. This paper presents an optimisation scheme that enables us to determine the secrecy capacity of a MIMO system with a single eavesdrop antenna. It is shown that, for certain parameters, the presented scheme is a concave problem which can therefore be solved efficiently using existing convex optimisation software.

Future Connected and Autonomous Vehicles (CAVs) will be equipped with a large set of sensors. The large amount of generated sensor data is expected to be exchanged with other CAVs and the road-side infrastructure. Both in Europe and the US, Dedicated Short Range Communications (DSRC) systems, based on the IEEE 802.11p Physical Layer, are key enabler for the communication among vehicles. Given the expected market penetration of connected vehicles, the licensed band of 75 MHz, dedicated to DSRC communications, is expected to become increasingly congested. In this paper, we investigate the performance of a vehicular communication system, operated over the unlicensed bands 2.4 GHz-2.5 GHz and 5.725 GHz-5.875 GHz. Our experimental evaluation was carried out in a testing track in the centre of Bristol, UK and our system is a full-stack ETSI ITS-G5 implementation. Our performance investigation compares key communication metrics (e.g., packet delivery rate, received signal strength indicator) measured by operating our system over the licensed DSRC an the considered unlicensed bands. In particular, when operated over the 2.4 GHz-2.5 GHz band, our system achieves comparable performance to the case when the DSRC band is used. On the other hand, as soon as the system, is operated over the 5.725 GHz-5.875 GHz band, the packet delivery rate is 30% smaller compared to the case when the DSRC band is employed. These findings prove that operating our system over unlicensed ISM bands is a viable option. During our experimental evaluation, we recorded all the generated network interactions and the complete data set has been publicly available.

The emergence of new connectivity services for automated transportation marks a paradigm shift for the operation of wireless networks. Furthermore, the advent of blockchain technology promises to enable a plethora of smart mobility services, which are not contingent on any central authorities. Concepts such as distributed ledger require efficient and reliable data dissemination between vehicles. Traditional techniques based on Automatic Repeat Request (ARQ) are well known to scale poorly in all-cast networks due to the feedback implosion problem. Fountain and network coding techniques are arguably the most promising alternative solutions. In this paper we derive new analytical bounds on transmit message lengths and quantify bandwidth delay trade-offs for fountain coding based data dissemination for CAVs.

Millimeter Waves (mmWaves) will play a pivotal role in the next- generation of Intelligent Transportation Systems (ITSs). However, in deep urban environments, sensitivity to blockages creates the need for more sophisticated network planning. In this paper, we present an agile strategy for deploying road-side nodes in a dense city scenario. In our system model, we consider strict Quality-of-Service (QoS) constraints (e.g. high throughput, low latency) that are typical of ITS applications. Our approach is scalable, insofar that takes into account the unique road and building shapes of each city, performing well for both regular and irregular city layouts. It allows us not only to achieve the required QoS constraints but it also provides up to 50\% reduction in the number of nodes required, compared to existing deployment solutions.

Connected and Automated Vehicles (CAVs) are expected to constantly interact with a network of processing nodes installed in secure cabinets located at the side of the road - - thus, forming Fog Computing-based infrastructure for Intelligent Transportation Systems (ITSs). Future city-scale ITS services will heavily rely upon the sensor data regularly off-loaded by each CAV on the Fog Computing network. Due to the broadcast nature of the medium, CAVs' communications can be vulnerable to eavesdropping. This paper proposes a novel data offloading approach where the Random Linear Network Coding (RLNC) principle is used to ensure the probability of an eavesdropper to recover relevant portions of sensor data is minimized. Our preliminary results confirm the effectiveness of our approach when operated in a large-scale ITS networks.

Future Connected and Automated Vehicles (CAVs) will be supervised by cloud-based systems overseeing the overall security and orchestrating traffic flows. Such systems rely on data collected from CAVs across the whole city operational area. This paper develops a Fog Computing-based infrastructure for future Intelligent Transportation Systems (ITSs) enabling an agile and reliable off-load of CAV data. Since CAVs are expected to generate large quantities of data, it is not feasible to assume data off-loading to be completed while a CAV is in the proximity of a single Road-Side Unit (RSU). CAVs are expected to be in the range of an RSU only for a limited amount of time, necessitating data reconciliation across different RSUs, if traditional approaches to data off-load were to be used. To this end, this paper proposes an agile Fog Computing infrastructure, which interconnects all the RSUs so that the data reconciliation is solved efficiently as a by-product of deploying the Random Linear Network Coding (RLNC) technique. Our numerical results confirm the feasibility of our solution and show its effectiveness when operated in a large-scale urban testbed.

Cryptography is one of the most widely employed means to ensure confidentiality in the Internet of Things (IoT). Establishing cryptographically secure links between IoT devices requires the prior consensus to a secret encryption key. Yet, IoT devices are resource-constrained and cannot employ traditional key distribution schemes. As a result, there is a growing interest in generating secret random keys locally, using the shared randomness of the communicating channel. This article presents a secret key generation scheme, named SKYGlow, which is targeted at resource-constrained IoT platforms and tested on devices that employ IEEE 802.15.4 radios. We first examine the practical upper bounds of the number of secret bits that can be extracted from a message exchange. We contrast these upper bounds with the current state-of-the-art, and elaborate on the workings of the proposed scheme. SKYGlow applies the Discrete Cosine Transform (DCT) on channel observations of exchanged messages to reduce mismatches and increase correlation between the generated secret bits. We validate the performance of SKYGlow in both indoor and outdoor scenarios, at 2.4 GHz and 868 MHz respectively. The results suggest that SKYGlow can create secret 128-bit keys of 0.9978 bits entropy with just 65 packet exchanges, outperforming the state-of-the-art in terms of energy efficiency.

We are interested in hot and cold water flow detection in domestic kitchen and bathroom taps for smart home environments. Water flow monitoring is particularly valuable for long-term behavioural monitoring systems for health-related applications, as it enables the collection of long-term data on the hydration levels of the house residents, and it is associated with several activities of daily life, such as cooking and cleaning. This paper presents a water flow sensing device that is based on sensing the vibrations on the pipe when water is flowing through them. The proposed solution is noninvasive and energy efficient, as it does not require cutting the water pipes or altering the plumbing system, and consumes less then 2 uA in continuous operation. The proposed water flow sensor has been integrated to SPHERE, a sensing platform of non-medical sensors for healthcare monitoring and behavioural analytics in a home environment, and deployed to more than 15 residential properties.

We consider a lossy multicast network in which the reliability is provided by means of random linear network coding. Our goal is to characterize the performance of such network in terms of the probability that a source message is delivered to all destination nodes. Previous studies considered coding over large finite fields, small numbers of destination nodes or specific, often impractical, channel conditions. In contrast, we focus on a general problem, considering arbitrary field size and number of destination nodes, and a realistic channel. We propose a lower bound on the probability of successful delivery, which is more accurate than the approximation commonly used in the literature. In addition, we present a novel performance analysis of the systematic version of RLNC. The accuracy of the proposed performance framework is verified via extensive Monte Carlo simulations, where the impact of the network and code parameters are investigated. Specifically, we show that the mean square error of the bound for a ten-user network can be as low as 9 · 10-5for non-systematic RLNC.

Networks deployed in real-world conditions have to cope with dynamic, unpredictable environmental temperature changes. These changes affect the clock rate on network nodes, and can cause faster clock de-synchronization compared to situations where devices are operating under stable temperature conditions. Wireless network protocols, such as time-slotted channel hopping (TSCH) from the IEEE 802.15.4-2015 standard, are affected by this problem, since they require tight clock synchronization among all nodes for the network to remain operational. This paper proposes a method for autonomously compensating temperature-dependent clock rate changes. After a calibration stage, nodes continuously perform temperature measurements to compensate for clock drifts at runtime. The method is implemented on low-power Internet of Things (IoT) nodes and evaluated through experiments in a temperature chamber, indoor and outdoor environments, as well as with numerical simulations. The results show that applying the method reduces the maximum synchronization error more than ten times. In this way, the method allows reduction in the total energy spent for time synchronization, which is practically relevant concern for low data rate, low energy budget TSCH networks, especially those exposed to environments with changing temperature.

The upcoming Internet of Things (IoT) applications include real-time human activity monitoring with wearable sensors. Compared to the traditional environmental sensing with low-power wireless nodes, these new applications generate a constant stream of a much higher rate. Nevertheless, the wearable devices remain battery powered and therefore restricted to low-power wireless standards such as IEEE 802.15.4 or Bluetooth Low Energy (BLE). Our work tackles the problem of building a reliable autonomous schedule for forwarding this kind of dynamic data in IEEE 802.15.4 TSCH networks. Due to the a priori unpredictability of these data source locations, the quality of the wireless links, and the routing topology of the forwarding network, it is wasteful to reserve the number of slots required for the worst-case scenario, under conditions of high expected datarate, it is downright impossible. The solution we propose is a hybrid approach where dedicated TSCH cells and shared TSCH slots coexist in the same schedule. We show that under realistic assumptions of wireless link diversity, adding shared slots to a TSCH schedule increases the overall packet delivery rate and the fairness of the system.

The confidentiality of communications in the Internet of Things (IoT) is critical, with cryptography currently being the most widely employed method of ensuring it. Establishing cryptographically secure communication links between two transceivers requires the pre-agreement on some key, unknown to an external attacker. In recent years there has been growing attention in techniques that generate a shared random key through observation of the channel and its effects on the exchanged messages. In this work we present SKYGlow, a novel scheme for secret-key generation, designed for IoT devices, such as IEEE 802.15.4 and Bluetooth Low Energy (BLE) transceivers. SKYGlow employs the Discreet Cosine Transform (DCT) of channel observations and Slepian-Wolf coding for information reconciliation. Real-life experiments have resulted in the creation of 128-bit secret keys with only 65 packet exchanges and with an entropy of 0.9978 bits, making our scheme much more energy-efficient compared with others in the existing literature.

Physical activity classification is an important tool for various applications such as activity of daily living (ADL) recognition and fall detection. Additionally, the non-contact nature of radar systems provides minimally invasive sensing platform. Doppler-based radar has been used for activity classification in the past. However, most of these studies considered supervised classification which requires labeled training data sets. In this paper, we propose a novel procedure of using micro Doppler radar for unsupervised classification with Hidden Markov Models (HMM). A low-complexity time alignment method for capturing activity is developed and an Elbow test has been adopted for model selection. Test results confirm the efficacy of the selected feature set and the proposed methodology. The results prove the proposed system can deliver a very good performance in ADL recognition tasks.

Millimetre Waves (mmWave) systems have the potential of enabling multi-gigabit-per-second communications in future Intelligent Transportation Systems (ITSs). Unfortunately, because of the increased vehicular mobility, they require frequent antenna beam realignments - thus significantly increasing the in-band Beamforming (BF) overhead. In this paper, we propose Smart Motion-prediction Beam Alignment (SAMBA), a MAC-layer algorithm that exploits the information broadcast via DSRC beacons by all vehicles. Based on this information, overhead-free BF is achieved by estimating the position of the vehicle and predicting its motion. Moreover, adapting the beamwidth with respect to the estimated position can further enhance the performance. Our investigation shows that SAMBA outperforms the IEEE 802.11ad BF strategy, increasing the data rate by more than twice for sparse vehicle density while enhancing the network throughput proportionally to the number of vehicles. Furthermore, SAMBA was proven to be more efficient compared to legacy BF algorithm under highly dynamic vehicular environments and hence, a viable solution for future ITS services.

The successful deployment of safe and trustworthy Connected and Autonomous Vehicles (CAVs) will highly depend on the ability to devise robust and effective security solutions to resist sophisticated cyber attacks and patch up critical vulnerabilities. Pseudonym Public Key Infrastructure (PPKI) is a promising approach to secure vehicular networks as well as ensure data and location privacy, concealing the vehicles' real identities. Nevertheless, pseudonym distribution and management affect PPKI scalability due to the significant number of digital certificates required by a single vehicle. In this paper, we focus on the certificate revocation process and propose a versatile and low- complexity framework to facilitate the distribution of the Certificate Revocation Lists (CRL) issued by the Certification Authority (CA). CRL compression is achieved through optimized Bloom filters, which guarantee a considerable overhead reduction with a configurable rate of false positives. Our results show that the distribution of compressed CRLs can significantly enhance the system scalability without increasing the complexity of the revocation process.

With the ubiquity of sensing technologies in our personal spaces, the protection of our privacy and the confidentiality of sensitive data becomes a major concern. In this letter, we focus on wearable embedded systems that communicate data periodically over the wireless medium. In this context, we demonstrate that private information about the physical activity levels of the wearer can leak to an eavesdropper through the physical layer. Indeed, we show that the physical activity levels strongly correlate with changes in the wireless channel that can be captured by measuring the signal strength of the eavesdropped frames. We practically validate this correlation in several scenarios in a real residential environment, using data collected by our prototype wearable accelerometer-based sensor. Finally, we propose a privacy enhancement algorithm that mitigates the leakage of this private information.

In this paper, error correction is introduced to the Bluetooth low energy and IEEE 802.15.4 standards by utilizing data redundancy provided by cyclic redundancy check (CRC) codes used by both protocols to detect erroneous packets. A scenario with an energy-constrained transmitter and a constraint-free infrastructure is assumed that enables additional signal processing at the receiving side, keeping the transmitter intact. CRC error correction is achieved using a novel approach of applying iterative decoding techniques. The proposed methods are evaluated based both on simulated and real packets. It is shown that by enabling CRC error correction, up to 2.5 dB of the signal to noise ratio gain can be achieved, while up to 35% of real corrupted packets can be corrected, at no extra cost for the transmitter. This results in potential range extension and longer battery life caused by a reduced number of retransmissions.

The Internet of Things (IoT) is expected to connect billions of devices, that will interact with their physical environment through sensors or actuators. The measurements created from these sensors have varying levels of precision, leading to measurements that follow a distribution, whose variance presents an additional challenge for the employed security schemes. In this work we assume a smart attacker would attempt to mask his attack in the inherent uncertainty of the measurements, and attempt to manipulate the distribution of measurements as covertly as possible to affect the final meaningful value that the system would result in. We employ Game Theory to examine the best strategies to slowly corrupt the integrity of an IoT network, similar to ETSI's Low Throughput Networks (LTN). We examine the extent of the changes that can be made to the distribution without assuming a priori knowledge of it by the attacker, for different scenarios and compromisation patterns. To the best of our knowledge this is the first attempt to examine the limits of the compromise that could be applied by a smart attacker on an IoT/LTN-type network without triggering outlier-alarms, and can be applied in the design of better targeted defensive measures.

In this paper, we analyze the performance of a single-relay network in which the reliability is provided by means of Random Linear Network Coding (RLNC). We consider a scenario when both source and relay nodes can encode packets. Unlike the traditional approach to relay networks, we introduce a passive relay mode, in which the relay node simply retransmits collected packets in case it cannot decode them. In contrast with the previous studies, we derive a novel theoretical framework for the performance characterization of the considered relay network. We extend our analysis to a more general scenario, in which coding coefficients are generated from non-binary fields. The theoretical results are verified using simulation, for both binary and non-binary fields. It is also shown that the passive relay mode significantly improves the performance compared with the active-only case, offering an up to two-fold gain in terms of the decoding probability. The proposed framework can be used as a building block for the analysis of more complex network topologies.

Physical activity envelope provides invaluable information in numerous pervasive health applications. Physical activity is traditionally gleaned using a range of wearable inertial sensors and/or video technology. This paper introduces a novel opportunistic and non-intrusive monitoring system which can quantify activity levels based on analysis of ambient WiFi signal scatter. A real-time signal processing framework is developed, and the proposed system is implemented in software defined radio platform. Experimental results corroborate the efficacy of the proposed system in long term ADL monitoring in residential healthcare applications.

Non-contact breathing monitoring systems are very attractive for a range of e-Healthcare applications. This paper proposes a passive radar based system for measuring human breathing rate. A novel signal processing method is introduced to extract breathing rate based on micro Doppler derived from cross ambiguity function (CAF). The passive radar system is built within a software defined radio (SDR) platform. The proposed system uses opportunistically energy harvesting transmitter as an illumination signal. Passive detection is compared and verified using the ground truth from clinical chest belt respiration detector. Two experiments have been conducted to show the feasibility of passive detection system in the line of sight and also through-wall conditions. We conclude that a low frequency narrow band signal with non-contact passive detection can offer a realistic alternative to UWB based radars for future e-Healthcare passive sensing applications.

Wearable technologies play a key role in the shift of traditional healthcare services towards eHealth and self-monitoring. Maintenance overheads, such as regular battery recharging, impose a limitation on the applicability of such technologies in some groups of the population. In this paper, we propose an activity monitoring system that is based on wearable sensors that are powered by textile inductive charging surfaces. By strategically positioning these surfaces on pieces of furniture that are routinely used, the system passively charges the wearable sensor whilst the user is present. As a proof-of-concept example, experiments conducted on a prototype implementation of the system suggest that 36 minutes of daily desktop computer usage are on average sufficient to maintain a wearable sensor energy neutral.

In this paper, we investigate the application of iterative decoding algorithms to error correction of cyclic redundancy check (CRC) codes widely used in low-energy communication standards. We consider the case when traditional error correction codes are not available due to energy constraints at the transmitter. Using the CRC-24 code adopted by the Bluetooth Low Energy standard as an example, we show how two iterative techniques traditionally used for decoding of low-density parity check codes - Belief Propagation (BP) and the Alternating Direction Method of Multipliers (ADMM) - can be applied to the high-density parity check matrix of the code. The performance of both techniques is evaluated through simulation, and it is demonstrated that a gain of up to 1.7 dB in terms of the SNR per bit and a total reduction of the packet error rate by more than 70% can be achieved compared with the non-correction scenario, at no extra cost for the transmitter. We also compare the two techniques and use the standard syndrome look-up method as a benchmark. Both schemes enable the correction of multiple errors, with the ADMM-based decoder demonstrating better overall performance than BP.

This paper discusses malicious false data injection attacks on the wide area measurement and monitoring system in smart grids. First, methods of constructing sparse stealth attacks are developed for two typical scenarios: 1) random attacks in which arbitrary measurements can be compromised; and 2) targeted attacks in which specified state variables are modified. It is already demonstrated that stealth attacks can always exist if the number of compromised measurements exceeds a certain value. In this paper, it is found that random undetectable attacks can be accomplished by modifying only a much smaller number of measurements than this value. It is well known that protecting the system from malicious attacks can be achieved by making a certain subset of measurements immune to attacks. An efficient greedy search algorithm is then proposed to quickly find this subset of measurements to be protected to defend against stealth attacks. It is shown that this greedy algorithm has almost the same performance as the brute-force method, but without the combinatorial complexity. Third, a robust attack detection method is discussed. The detection method is designed based on the robust principal component analysis problem by introducing element-wise constraints. This method is shown to be able to identify the real measurements, as well as attacks even when only partial observations are collected. The simulations are conducted based on IEEE test systems.

Heterogeneous networks have a key role in the design of future mobile communication networks, since the employment of small cells around a macrocell enhances the network's efficiency and decreases complexity and power demand. Moreover, research on Blind Interference Alignment (BIA) has shown that optimal Degrees of Freedom (DoF) can be achieved in certain network architectures, with no requirement of Channel State Information (CSI) at the transmitters. Our contribution is a generalised model of BIA in a heterogeneous network with one macrocell with K users and K femtocells each with one user, by using Kronecker (Tensor) Product representation. We introduce a solution on how to vary beamforming vectors under power constraints to maximize the sum rate of the network and how optimal DoF can be achieved over K+1 time slots.

Passive communications based on modulated backscatter can dramatically extend the lifespan of wireless sensor networks. Because wireless passive sensor networks reduce the dependence on the batteries, they have great potential to be used in body sensing applications. In a typical sensor network deployment, multiple passive sensor nodes modulate a common carrier. The superimposed reflected signals can be resolved by using Code division multiple access (CDMA) principles. In practical systems however, a strong carrier component will leak to the receiver, causing offset to the constellation points and as a result it will destroy cross correlation properties of the CDMA codes. To address this problem, we propose a code shift modulation instead of vanilla CDMA. We develop maximum likelihood receiver and analyse its performance. Since the complexity of ML detection strategy scales exponentially with the number of sensors, we develop a reduced complexity detection technique based on Probabilistic Data Association (PDA). We demonstrate via numerical simulations that our reduced complexity detector approaches the ML performance.

Even though the main motivation behind wireless access for vehicular environments was to support time‐sensitive safety applications, typical Internet applications are also required to attract private investment that would help expand the network and reduce the cost of implementing the systems. It is well known that the Automatic Repeat reQuest scheme used in the current IEEE 802.11p standard is highly inefficient for vehicular applications. Therefore a raptor‐coded decode‐and‐forward relaying scheme with an efficient feedback channel, named the ‘r‐DF scheme’ for infrastructure‐to‐vehicle infotainment applications is proposed in this study. The scheme is evaluated using a multi‐layered simulator combining a realistic IEEE 802.11p physical and MAC layer design considering the presence of interference in a highway and an urban scenario. The simulation shows that the scheme achieved up to double the average aggregate throughput and tens of decoding time improvements over the state‐of‐the‐art.

In this paper we consider Basis Pursuit De-Noising (BPDN) problems in which the sparse original signal is drawn from a finite alphabet. To solve this problem we propose an iterative message passing algorithm, which capitalises not only on the sparsity but by means of a prior distribution also on the discrete nature of the original signal. In our numerical experiments we test this algorithm in combination with a Rademacher measurement matrix and a measurement matrix derived from the random demodulator, which enables compressive sampling of analogue signals. Our results show in both cases significant performance gains over a linear programming based approach to the considered BPDN problem. We also compare the proposed algorithm to a similar message passing based algorithm without prior knowledge and observe an even larger performance improvement.

In an urban environment, vehicle mobility is limited by road structure and obstacles. This gives rise to extremely challenging network design for large data transfers due to unpredictable network dynamics, limited connection lifetime and unstable channel conditions. Conventional approaches using retransmission schemes do not perform well in these conditions due to nodes leaving the network in the middle of the download. Rateless codes have been identified as a solution to address interrupted downloads with minimal handshaking and overhead requirements. Varying channel conditions calls for the need of multi-rate vehicular communications, where low rate robust communication is used when bad channel is experienced and higher rate modulation is used in good channel conditions. This paper presents a novel coding scheme based on systematic raptor codes for multihop data dissemination that is based on multi-rate vehicular communications. With this scheme, even vehicles which are outside the roadside infrastructure coverage can be provided with reliable access to infrastructure services.

In this paper we introduce a novel modulation and multiplexing method which facilitates highly efficient and simultaneous communication between multiple terminals in wireless ad-hoc networks. We term this method Combinatorial Channel Signature Modulation (CCSM). The CCSM method is particularly efficient in situations where communicating nodes operate in highly time dispersive environments. This is all achieved with a minimal MAC layer overhead, since all users are allowed to transmit and receive at the same time/frequency (full simultaneous duplex). The CCSM method has its roots in sparse modelling and the receiver is based on compressive sampling techniques. Towards this end, we develop a new low complexity algorithm termed Group Subspace Pursuit. Our analysis suggests that CCSM at least doubles the throughput when compared to the state-of-the art.

Ergodic interference alignment, as introduced by Nazer et al (NGJV), is a technique that allows high-rate communication in n-user interference networks with fast fading. It works by splitting communication across a pair of fading matrices. However, it comes with the overhead of a long time delay until matchable matrices occur: the delay is qn2for field size q. In this paper, we outline two new families of schemes, called JAP and JAP-B, that reduce the expected delay, sometimes at the cost of a reduction in rate from the NGJV scheme. In particular, we give examples of good schemes for networks with few users, and show that in large n-user networks, the delay scales like qT, where T is quadratic in n for a constant per-user rate and T is constant for a constant sum-rate. We also show that half the single-user rate can be achieved while reducing NGJV's delay from qn2to q(n-1)(n-2).

Compressive sensing (CS) is a new signal acquisition framework, which allows for a signal recovery from far fewer samples than what is required by traditional sampling methods. In this paper we propose new strategies for adaptively adjusting the number of CS samples in wireless sensor networks (WSNs). Additionally, in the signal reconstruction procedure we apply homotopy algorithm to update the reconstructed signals. The reduction of CS samples and the homotopy update reduce the computational complexity and save processing time and energy for both the fusion centre and wireless sensors. The proposed techniques are investigated numerically in various WSN scenarios.

In this study the authors perform a diversity analysis for an energy detection-based non-coherent spectrum sensing scheme and benchmark it against a genie aided coherent spectrum sensing scheme, which has full knowledge of the fading channel. In both cases the sensor has access to multiple independent channel observations. Results show that both schemes achieve full diversity. However, the authors find that the coherent scheme approaches this asymptotic limit faster than the non-coherent scheme. Through analysis the authors determine the penalty of the non-coherent scheme over the coherent scheme in converging to the asymptotic limit as a function of the SNR and the number of diversity branches. With these results the authors substantiate analysis, previously made, and thus provide further insights into the trade-off between complexity and performance for spectrum sensing schemes.

Car-to-car safety applications that demand real-time and reliable communications in vehicular ad hoc networks (VANETs) requires a new paradigm of coding techniques. In this paper, we propose a novel coding approach using a systematic Raptor code for car-to-car post-crash warning broadcast applications. A cross-layer simulator model is developed to evaluate the performance of Raptor codes against repetition codes using also multiple antennas spatial diversity techniques. The end-to-end delay and packet delivery ratio are used as performance metrics to demonstrate the latency and reliability problems of repetition codes that are addressed using Raptor codes.

One of the important applications to be available in vehicular ad-hoc networks are value-added or infotainment services. However, vehicular communication suffers from high packet loss due to challenging channel characteristics such as huge Doppler spread and multipath fading. This makes current IEEE 802.11p standard for vehicular network based on the ARQ scheme inefficient. Therefore, the highly scalable and fault-tolerant properties offered by rateless code makes this a promising area of research. This paper investigates the implementation of a systematic Raptor codes for a broadcast service in infrastructure-to-vehicular communications. The code performance in terms of the decoding probability of success, mean decoding time and mean aggregate throughput are presented.

We consider a dense K user Gaussian interference network formed by paired transmitters and receivers placed independently at random in a fixed spatial region. Under natural conditions on the node position distributions and signal attenuation, we prove convergence in probability of the average per-user capacity CΣ/K to 1/2E log(1 + 2SNR). The achievability result follows directly from results based on an interference alignment scheme presented in recent work of Nazer et al. Our main contribution comes through an upper bound, motivated by ideas of "bottleneck capacity" developed in recent work of Jafar. By controlling the physical location of transmitter-receiver pairs, we can match a large proportion of these pairs to form so-called ε-bottleneck links, with consequent control of the sum capacity.

We consider a dense n-user Gaussian interference network formed by paired transmitters and receivers placed independently at random in Euclidean space. Under natural conditions on the node position distributions and signal attenuation, we prove convergence in probability of the average per-user capacity CΣ/n to ½ E log(1 + 2SNR). The achievability result follows directly from results based on an interference alignment scheme presented in recent work of Nazer et al. Our main contribution comes through the converse result, motivated by ideas of `bottleneck links' developed in recent work of Jafar. An information theoretic argument gives a capacity bound on such bottleneck links, and probabilistic counting arguments show there are sufficiently many such links to tightly bound the sum-capacity of the whole network.

In this paper, we evaluate the performance of broadcast safety applications in a vehicular-to-vehicular (V2V) communication scenario using a BER-based reception model from a detailed IEEE 802.11p physical layer simulator for a more accurate interpretation of the vehicular communication system, instead of the prebuilt SNR threshold model available in a typical network simulators. Our main contribution is the spatial diversity analysis provided using MIMO-STBC, that is specific to modulation types, vehicular speeds and range of SNR values. To further improve the accuracy of the analysis, realistic vehicular traces from a bidirectional highway are used as the mobility model. Finally, we included post-crash warning message prioritization over periodic status updates by enabling a hybrid EDCA MAC. Our analysis shows that MIMO-STBC achieves up to 80% range extension of the 1-hop safety broadcast in a V2V when compared against single antenna system.

In this paper we consider a non-coherent and coherent spectrum sensing scheme for OFDM signals. In addition to perfect symbol timing information we also assume full channel knowledge in the coherent case. For the non-coherent and the coherent scheme we derive analytical expressions for the detection error probability. In a comparison of these analytical and additional numerical results we find that the increased complexity of the coherent scheme does not necessarily result in a better performance. We thus provide an important insight in the complexity performance trade-off of spectrum sensing schemes.

In this contribution, we consider design of distributed LT codes, i.e., independent rateless encodings of multiple sources which communicate to a common relay, where relay is able to combine incoming packets from the sources and forwards them to receivers. We provide density evolution formulae for distributed LT codes, which allow us to formulate distributed LT code design problem and prove the equivalence of performance of distributed LT codes and LT codes with related parameters in the asymptotic regime. Furthermore, we demonstrate that allowing LT coding apparatus at both the sources and the relay may prove advantageous to coding only at the sources and coding only at the relay.

A novel approach to provide unequal error protection (UEP) using rateless codes over erasure channels, named Expanding Window Fountain (EWF) codes, is developed and discussed. EWF codes use a windowing technique rather than a weighted (non-uniform) selection of input symbols to achieve UEP property. The windowing approach introduces additional parameters in the UEP rateless code design, making it more general and flexible than the weighted approach. Furthermore, the windowing approach provides better performance of UEP scheme, which is confirmed both theoretically and experimentally.

Fountain codes are a robust solution for data multicasting to a large number of receivers which experience variable channel conditions and different packet loss rates. However, the standard fountain code design becomes inefficient if all receivers have access to some side information correlated with the source information. We focus our attention on the cases where the correlation of the source and side information can be modelled by a binary erasure channel (BEC) or by a binary input additive white Gaussian noise channel (BIAWGNC). We analyse the performance of fountain codes in data multicasting with side information for these cases, derive bounds on their performance and provide a fast and robust linear programming optimization framework for code parameters. We demonstrate that systematic Raptor code design can be employed as a possible solution to the problem at the cost of higher encoding/decoding complexity, as it reduces the side information scenario to a channel coding problem. However, our results also indicate that a simpler solution, non-systematic LT and Raptor codes, can be designed to perform close to the information theoretic bounds.

In this contribution, duals of fountain codes are introduced and their use for lossy source compression is investigated. It is shown both theoretically and experimentally that the source coding dual of the binary erasure channel coding problem, binary erasure quantization, is solved at a nearly optimal rate with application of duals of LT and raptor codes by a belief propagation-like algorithm which amounts to a graph pruning procedure. Furthermore, this quantizing scheme is rate adaptive, i.e., its rate can be modified on-the-fly in order to adapt to the source distribution, very much like LT and raptor codes are able to adapt their rate to the erasure probability of a channel.

In this contribution, we consider the application of digital fountain (DF) codes to the problem of data transmission when side information is available at the decoder. The side information is modelled as a "virtual" channel output when original information sequence is the input. For two cases of the system model, which model both the virtual and the actual transmission channel either as a binary erasure channel or as a binary input additive white Gaussian noise (BIAWGN) channel, we propose methods of enhancing the design of standard non-systematic DF codes by optimizing their output degree distribution based on the side information assumption. In addition, a systematic Raptor design has been employed as a possible solution to the problem.

In this contribution, we construct two novel IR-HARQ (automatic repeat request) schemes based on fountain codes, which combine the punctured and rateless IR-HARQ schemes, in order to attain the advantageous properties of both: nearly optimal performance of the former at the high signal-to-noise ratio (SNR) region and ratelessness of the latter. The preliminary simulation results indicate that these schemes are particularly suitable for scenarios where the transmission is originally assumed to occur at the very high SNR region, but resilience to severe deterioration of channel conditions is required.

The multilevel structure of the N-QAM modulation constellations is exploited to significantly reduce the complexity of the sequential Gaussian approximation (SGA) algorithm for near optimal symbol detection in spatial multiplexing multiple- input multiple-output (MIMO) system. We propose two multilevel SGA algorithms (MSGA) which are based on depth- first search (DFS) and breadth-first search (BFS) respectively. Additionally, an important methodological contribution to this multilevel technique is proposed where the mismatch between the pseudo symbols and the true symbols is taken into consideration for the computation of posterior probabilities of symbol combinations. We justify this from a theoretical perspective as well as with numerical results. Simulation results show that the performance of the two proposed multilevel algorithms can approach that of the optimal a posteriori probability (APP) detector while its total computation cost is at most 81% and 48% of that of the original SGA algorithm for 16QAM and 64QAM modulation MIMO systems with 4 transmit/receive antennas respectively.

Detection of spatially multiplexed data transmissions subject to frequency flat fading is considered. Optimal decoders require knowledge of the marginal posterior distributions of the transmitted symbols, but their exact computation is not feasible for practical systems. Hence sub-optimal approaches are generally sought. By recasting this problem into the graphical model framework, we investigate here a recently proposed suboptimal approach which relies on a tree-based reparameterization principle. For quasi-static fading channels, the resulting decoder complexity has an order which is at most quadratic in the number of transmit antennas. However, in its standard form, the algorithm often fails to converge, severely restricting its practical usability. We here develop a novel methodology to ensure systematic convergence of the algorithm in this communication scenario at the expense of the introduction of a minimal bias on the computation of the symbol marginal posterior probabilities. This bias is quantified theoretically and its innocuity for the problem at hand is ultimately demonstrated through numerical simulations. For a system using 16-QAM modulation with four transmit and receive antennas, the proposed detector achieves a bit-error rate of 10-4requiring only 3 dB greater SNR than the optimal method.

In this paper we present an analog implementation of the mean-field algorithm for inference in Undirected Graphical Models (UGM). One of its numerous applications is signal detection in wireless receivers which can benefit from low power analog circuitry. An example detector for a double Alamouti Space-Time Block Code (STBC) has been designed and laid out in a 0.25μm SiGe BiCMOS process.

BCJR algorithm is an exact and efficient algorithm to compute the marginal posterior distributions of state variables and pairs of consecutive state variables of a trellis structure. Due to its overwhelming complexity, reduced complexity variations, such as the M-BCJR algorithm, have been developed. In this paper, we propose improvements upon the conventional M-BCJR algorithm based on modified active state selection criteria. We propose selecting the active states based on estimates of the fixed-lag smoothed distributions of the state variables. We also present Gaussian approximation techniques for the low-complexity estimation of these fixed-lag smoothed distributions. The improved performance over the M-BCJR algorithm is shown via computer simulations.

A novel Gaussian approximation based mixture reduction algorithm is proposed for semi-blind joint channel tracking and symbol detection for spatial multiplexing multiple-input multiple-output (MIMO) systems with frequency-flat time-selective channels. The proposed algorithm is based on a modified sequential Gaussian approximation detector (SGA) which takes into account channel uncertainty, and the first order generalized pseudo-Bayesian (GPB1) channel estimator. Simulation results show that the proposed algorithm performs better than the conventional and computationally expensive decision-directed method with Kalman filter based channel estimation and a posteriori probability (APP) symbol detection.

In this paper we propose an analog VLSI approach to maximum a posteriori (MAP) detection in multiple-input multiple-output (MIMO) systems. This detector can be seen as an extension of the well known analog decoding concept for error correcting codes, as it is constructed using similar building blocks. Therefore, it can naturally interact with analog decoders in order to perform turbo detection in MIMO systems. First transistor-level simulations for a small analog MIMO detector in a 0.25mum BiCMOS process agree well with floating-point digital simulations

This paper investigates iterative channel estimation and symbol detection for spatial multiplexing multiple input multiple output (MIMO) systems with frequency flat block fading channels using the expectation-maximization (EM) algorithm. The maximum likelihood (ML) estimation of the MIMO channels via the EM algorithm requires the computation of the posterior mean and covariance of transmit symbol vectors which involve an exhaustive search of all possible symbol combinations and are computationally prohibitive for large systems. However, most of the symbol combinations contribute very little to the estimation. Therefore, we suggest that sequential Gaussian approximation (SGA) algorithm can be used to identify the M most significant symbol combinations and we can approximate the mean and covariance based on those symbol combinations. Simulation results are provided to illustrate the proposed algorithm.

This work investigates the performance of a new reduced-complexity trellis decoding algorithm (termed the EP-MBCJR algorithm) when employed for the task of equalization in an indoor multiple input multiple output wireless environment. The algorithm is a generic approximate reduced-state variation of the BCJR algorithm modeled after the conventional M-BCJR algorithm. Instead of choosing the active states based on the filtered distribution of states in the forward recursion, the EP-MBCJR algorithm selects the active states based on "beliefs" on the states. This can be seen as an application of the concept of "expectation propagation" and leads to identical forward and backward recursions which can be iterated to improve system performance. A receiver architecture comprising of channel estimation, sampling phase selection and turbo equalization is proposed and its performance evaluated through computer simulations. For the simulation of channels closely resembling the physical environment, we have used channels generated in accordance with the IEEE 802.11n TGn channel models

The inferential task of computing the marginal posterior probability mass functions of state variables and pairs of consecutive state variables of a hidden Markov model is considered. This can be exactly and efficiently performed using a message passing scheme such as the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm. We present a novel iterative reduced complexity variation of the BCJR algorithm that uses reduced support approximations for the forward and backward messages, as in the M-BCJR algorithm. Forward/backward message computation is based on the concept of expectation propagation, which results in an algorithm similar to the M-BCJR algorithm with the active state selection criterion being changed from the filtered distribution of state variables to beliefs of state variables. By allowing possibly different supports for the forward and backward messages, we derive identical forward and backward recursions that can be iterated. Simulation results of application for trellis-based equalization of a wireless communication system confirm the improved performance over the M-BCJR algorithm.

The paper reports on a new class of equalisation/detection for wideband multiple input-multiple output (MIMO) communications systems. The proposed scheme is somewhat akin to a multi-carrier MIMO system, and more precisely, builds upon a single carrier frequency domain equalised (SCFDE) MIMO system. At the core of our system lies a novel concept of iterative self-reused equalisation/detection (bootstrapping) combined with semi-hard decision making. The bootstrapping concept is derived from a new formulation of Tikhonov regularisation. The proposed scheme achieves a remarkable performance/complexity trade-off. In particular we show that our system approaches the performance of ML-MIMO-OFDM while being only slightly more complex than MMSE-MIMO-OFDM. The coded version achieves virtually the same performance as turbo MIMO-OFDM at a fraction of the complexity. The resulting system is termed zero tailed bootstrap frequency equalised (ZTBFE) MIMO system.

A novel multi-user detection (MUD) technique for CDMA systems is introduced. The new method is well suited for cases in which the code cross correlation matrix is ill-conditioned. In practice, this case coincides with having code lengths approximately equal to the number of users - a desirable condition in terms of bandwidth efficiency. Our approach employs an iterative formulation of a well-known regularization method for linear inverse problems, which is suited to the MUD problem. The technique allows knowledge of the finite set in which the solution belongs to be exploited in a computationally efficient manner in order to improve the quality of the estimate iteratively.

The paper considers a multiple-input multiple-output (MIMO) communication system, which uses space-time trellis coding (STTC). A novel method of decoding STTC without a need to transmit training sequences is developed. The technique uses only a single channel estimate to acquire a complete set of the channels' estimates while performing STTC detection. The method is akin to blind trellis search techniques (per-survivor processing - PSP) and adaptive Viterbi. Our solution consists of the deployment of a bank of Kalman filters. The bank of Kalman filters is coupled with Viterbi type decoders, which produce tentative decisions based on Kalman channel predictions. In return, the Kalman filters use the tentative decisions to update and track the MIMO channels corresponding to a number of tracked hypotheses. The proposed technique is particularly applicable to space-time systems operating in rapidly fading environments, where STTC can be decoded and MIMO channels efficiently tracked without relying on periodic pilot sequences.

This paper considers an OFDM system with a multiple-input multiple-output (MIMO) configuration, which uses space-frequency trellis coding (SFTC). A novel method of decoding SFTC without a need to transmit separate training sequences is developed. The technique uses only a single frequency tone to acquire a complete set of the channels' estimates while performing SFTC detection. The method is akin to blind trellis search techniques (per-survivor processing - PSP) and adaptive Viterbi. Our solution consists of the deployment of a bank of Kalman filters. The bank of Kalman filters is coupled with Viterbi type decoders, which produce tentative decisions based on Kalman channel predictions. In return, the Kalman filters use the tentative decisions to update and track the MIMO channels corresponding to a number of tracked hypotheses.

We examine the performance of a space-time coded (STC) OFDM system. As an example, two emerging WLAN standards (IEEE 802.11a and ETSI HIPERLAN/2) are modified to accommodate the STC technique. Performance results using statistical multi-element channel models are compared with those using state-of-the-art indoor MIMO channel measurements. We conclude that the gap between the ideal setting and our measured channel is 4, 5 and 6 dB for 1, 2 and 3 receive antennas respectively. We observe also that STC with (2Tx, 3Rx) over the measured channels doubles the capacity as compared to the equivalent mode of HIPERLAN/2.

In order to evaluate the performance of third-generation mobile communication systems, radio channel models are required. The models should be capable of handling nonstationary scenarios with dynamic evolution of multipath. In this context and due to the introduction of advanced antenna systems to exploit the spatial domain, a further expansion is needed in order to include the nonstationary characteristics of the channel. In an attempt to solve these problems, this paper presents a new stochastic spatio-temporal propagation model. The model is a combination of the geometrically-based single reflection and the Gaussian wide-sense stationary uncorrelated scattering models, and is further enhanced in order to be able to handle nonstationary scenarios. The probability density functions of the number of multipath components, the scatterers' lifetime, and the angle of arrival are calculated to support these features. The input parameters of the model are based on results from measurement campaigns published in the open literature.