Chaotic antenna arrays (CAAs) have been shown to produce enhanced and robust RF fingerprints, enabling more reliable device authentication using machine learning (ML) compared to traditional methods based on subtle hardware imperfections. In this paper, we present a novel CAA-specific multipath channel model to accurately capture the CAA’s phase errors across all propagation paths providing a basis for processing schemes that remove the channel effect, enabling extraction of the RF signature of the CAA. In addition, we provide a comprehensive experimental validation of CAA-based authentication under practical wireless channel conditions and include full details covering the design, fabrication, and characterization of custom CAA nodes, along with their integration within a software-defined radio (SDR) testbed for over-the-air measurements. This manuscript shows, for the first time, that training of the ML-based authenticator on the CAA RF fingerprints can be conducted under line-of-sight (LOS) conditions and effectively generalized to diverse scenarios, including non-line-of-sight (NLOS) environments, as long as a dominant propagation path exists. High authentication accuracy is consistently achieved when this key spatial channel characteristic is preserved. Accuracy exceeds 90% in LOS and reaches up to 87% in NLOS conditions with a single dominant reflected path. This study provides the first practical validation of spatially varying CAA fingerprints, underscoring their promise for secure and robust physical layer authentication across varied wireless conditions.

Time series forecasting is crucial for various applications, such as weather forecasting, power load forecasting, and financial analysis. In recent studies, MLP-mixer models for time series forecasting have been shown as a promising alternative to transformer-based models. However, the performance of these models is still yet to reach its potential. In this paper, we propose Wavelet Patch Mixer (WPMixer), a novel MLP-based model, for long-term time series forecasting, which leverages the benefits of patching, multi-resolution wavelet decomposition, and mixing. Our model is based on three key components: (i) multi-resolution wavelet decomposition, (ii) patching and embedding, and (iii) MLP mixing. Multi-resolution wavelet decomposition efficiently extracts information in both the frequency and time domains. Patching allows the model to capture an extended history with a look-back window and enhances capturing local information while MLP mixing incorporates global information. Our model significantly outperforms state-of-the-art MLP-based and transformer-based models for long-term time series forecasting in a computationally efficient way, demonstrating its efficacy and potential for practical applications.

Wireless sensor networks (WSNs) can consist of many inexpensive sensors that communicate using the same wireless channel. In some applications, localization of these sensors can be as crucial as collecting their monitoring data. As the number of sensors increases, the complexity of processing data in such large networks grows significantly. In general, localization methods in WSNs typically rely on data containing sensor-specific information. However, the problem becomes more challenging when data contains no sensor-specific information. To address this issue, we propose a mixer-based deep neural network to estimate sensor positions using the received cumulative signal strength that is devoid of explicit sensor-specific information. Our approach employs wavelet decomposition to extract information from the input time series, combined with patching, embedding, and mixer techniques for position estimation. We compare the performance of our model with a nonlinear Kalman filter-based state estimation method. Extensive evaluations using data generated from our simulator demonstrate that our method consistently outperforms the unscented Kalman filter (UKF) method across all scenarios.

Weakly supervised video anomaly detection is an important problem in many real-world applications where during training there are some anomalous videos, in addition to nominal videos, without labelled frames to indicate when the anomaly happens. State-of-the-art methods in this domain typically focus on offline anomaly detection without any concern for real-time detection. Most of these methods rely on ad hoc feature aggregation techniques and the use of metric learning losses, which limit the ability of the models to detect anomalies in real-time. In line with the premise of deep neural networks, there also has been a growing interest in developing end-to-end approaches that can automatically learn effective features directly from the raw data. We propose the first real-time and end-to-end trained algorithm for weakly supervised video anomaly detection. Our training procedure builds upon recent action recognition literature and trains a large video model to learn visual features. This is in contrast to existing approaches which largely depend on pre-trained feature extractors. The proposed method significantly improves the anomaly detection speed and AUC performance compared to the existing methods. Specifically, on the UCF-Crime dataset, our method achieves 86.94% AUC with a decision period of 6.4 seconds while the competing methods achieve at most 85.92% AUC with a decision period of 273 seconds.

Most video anomaly detection approaches are based on data-intensive end-to-end trained neural networks, which extract spatiotemporal features from videos. The extracted feature representations in such approaches are not interpretable, which prevents the automatic identification of anomaly cause. To this end, we propose a novel framework which can explain the detected anomalous event in a surveillance video. In addition to monitoring objects independently, we also monitor the interactions between them to detect anomalous events and explain their root causes. Specifically, we demonstrate that the scene graphs obtained by monitoring the object interactions provide an interpretation for the context of the anomaly while performing competitively with respect to the recent state-of-the-art approaches. Moreover, the proposed interpretable method enables cross-domain adaptability (i.e., transfer learning in another surveillance scene), which is not feasible for most existing end-to-end methods due to the lack of sufficient labeled training data for every surveillance scene. The quick and reliable detection performance of the proposed method is evaluated both theoretically (through an asymptotic optimality proof) and empirically on the popular benchmark datasets.

Hospital capacity expansion planning is critical for a healthcare authority, especially in regions with a growing diverse population. Policymaking to this end often requires satisfying two conflicting objectives, minimizing capacity expansion cost and minimizing the number of denial of service (DoS) for patients seeking hospital admission. The uncertainty in hospital demand, especially considering a pandemic event, makes expansion planning even more challenging. This work presents a multi-objective reinforcement learning (MORL) based solution for healthcare expansion planning to optimize expansion cost and DoS simultaneously for pandemic and non-pandemic scenarios. Importantly, our model provides a simple and intuitive way to set the balance between these two objectives by only determining their priority percentages, making it suitable across policymakers with different capabilities, preferences, and needs. Specifically, we propose a multi-objective adaptation of the popular Advantage Actor-Critic (A2C) algorithm to avoid forced conversion of DoS discomfort cost to a monetary cost. Our case study for the state of Florida illustrates the success of our MORL based approach compared to the existing benchmark policies, including a state-of-the-art deep RL policy that converts DoS to economic cost to optimize a single objective.

In the 21st century, the world has experienced devastating wars that have caused people to migrate, creating problems in host countries. Among these migration routes, maritime migration routes are more desirable compared to other routes because coasts cannot be controlled as strictly as the alternative passages. However, maritime migration poses life-threatening risks due to unsafe boats, transportation between undesignated areas, lack of life-saving equipment, and dangerous weather conditions chosen for covert operations. Refugees and migrants may die or go missing at sea during these migrations. Most refugees are unaware of high risks they are facing as they hopelessly set out in search of better living conditions. In this study, we propose that such suicide-like maritime migration activities can be detected to some extent through cell phone activities and there may be a way to track early signs of migrants coming together from other regions and migrating through sea routes. By collecting media reports of failed attempts by immigrants and linking them to the D4R cell phone data, we were able to gain some indications of the possibility of early warning systems through analysis of cell phone calls.

While anomaly detection in time series has been an active area of research for several years, most recent approaches employ an inadequate evaluation criterion leading to an inflated F1 score. We show that a rudimentary Random Guess method can outperform state-of-the-art detectors in terms of this popular but faulty evaluation criterion. In this work, we propose a proper evaluation metric that measures the timeliness and precision of detecting sequential anomalies. Moreover, most existing approaches are unable to capture temporal features from long sequences. Self-attention based approaches, such as transformers, have been demonstrated to be particularly efficient in capturing long-range dependencies while being computationally efficient during training and inference. We also propose an efficient transformer approach for anomaly detection in time series and extensively evaluate our proposed approach on several popular benchmark datasets.

Anomaly detection in videos has been attracting an increasing amount of attention. Despite the competitive performance of recent methods on benchmark datasets, they typically lack desirable features such as modularity, cross-domain adaptivity, interpretability, and real-time anomalous event detection. Furthermore, current state-of-the-art approaches are evaluated using the standard instance-based detection metric by considering video frames as independent instances, which is not ideal for video anomaly detection. Motivated by these research gaps, we propose a modular and unified approach to the online video anomaly detection and localization problem, called MOVAD, which consists of a novel transfer learning based plug-and-play architecture, a sequential anomaly detector, a mathematical framework for selecting the detection threshold, and a suitable performance metric for real-time anomalous event detection in videos. Extensive performance evaluations on benchmark datasets show that the proposed framework significantly outperforms the current state-of-the-art approaches.

While video anomaly detection has been an active area of research for several years, recent progress is limited to improving the state-of-the-art results on small datasets using an inadequate evaluation criterion. In this work, we take a new comprehensive look at the video anomaly detection problem from a more realistic perspective. Specifically, we consider practical challenges such as continual learning and few-shot learning, which humans can easily do but remains to be a significant challenge for machines. A novel algorithm designed for such practical challenges is also proposed. For performance evaluation in this new framework, we introduce a new dataset which is significantly more comprehensive than the existing benchmark datasets, and a new performance metric which takes into account the fundamental temporal aspect of video anomaly detection. The experimental results show that the existing state-of-the-art methods are not suitable for the considered practical challenges, and the proposed algorithm outperforms them with a large margin in continual learning and few-shot learning tasks.

Intelligent vehicular systems and smart city applications are the fastest growing Internet-of-Things (IoT) implementations at a compound annual growth rate of 30%. In view of the recent advances in IoT devices and the emerging new breed of IoT applications driven by artificial intelligence (AI), the fog radio access network (F-RAN) has been recently introduced for the fifth-generation (5G) wireless communications to overcome the latency limitations of cloud-RAN (C-RAN). We consider the network slicing problem of allocating the limited resources at the network edge (fog nodes) to vehicular and smart city users with heterogeneous latency and computing demands in dynamic environments. We develop a network slicing model based on a cluster of fog nodes (FNs) coordinated with an edge controller (EC) to efficiently utilize the limited resources at the network edge. For each service request in a cluster, the EC decides which FN to execute the task, i.e., locally serve the request at the edge, or to reject the task and refer it to the cloud. We formulate the problem as infinite-horizon Markov decision process (MDP) and propose a deep reinforcement learning (DRL) solution to adaptively learn the optimal slicing policy. The performance of the proposed DRL-based slicing method is evaluated by comparing it with other slicing approaches in dynamic environments and for different scenarios of design objectives. Comprehensive simulation results corroborate that the proposed DRL-based EC quickly learns the optimal policy through interaction with the environment, which enables adaptive and automated network slicing for efficient resource allocation in dynamic vehicular and smart city environments.

We study online privacy-preserving anomaly detection in a setting in which the data are distributed over a network and locally sensitive to each node, and a probabilistic data model is unknown. We design and analyze a data-driven solution scheme where each node observes a high-dimensional data stream for which it computes a local outlierness score. This score is then perturbed, encrypted, and sent to a network operator. The network operator then decrypts an aggregate statistic over the network and performs online network anomaly detection via the proposed generalized cumulative sum (CUSUM) algorithm. We derive an asymptotic lower bound and an asymptotic approximation for the average false alarm period of the proposed algorithm. Additionally, we derive an asymptotic upper bound and asymptotic approximation for the average detection delay of the proposed algorithm under a certain anomaly. We show the analytical tradeoff between the anomaly detection performance and the differential privacy level, controlled via the local perturbation noise. Experiments illustrate that the proposed algorithm offers a good tradeoff between privacy and quick anomaly detection against the UDP flooding and spam attacks in a real Internet of Things (IoT) network.

Sea-level rise (SLR) problem, which is a major outcome of climate change, has been well documented and studied. Although it is globally observed due to climate change, local projections are needed to plan SLR adaptation strategies accurately. Since SLR is a community-wide multistakeholder problem at the local level, adaptation strategies can be more successful if the main stakeholders, e.g., government, residents, and businesses, collaborate in shaping them. Simulating the local socioeconomic system around SLR, including the interactions between essential stakeholders and nature, can be an effective way of evaluating different adaptation strategies and planning the best strategy for the local community. This work presents how such an SLR socioeconomic system can be modeled as a Markov decision process (MDP) and simulated using multiagent reinforcement learning (RL). The proposed multiagent RL framework serves two purposes. It provides a general scenario planning tool to investigate the cost–benefit analysis of natural events (e.g., flooding and hurricane) and agents’ investments (e.g., infrastructure improvement). It also shows how much the total cost due to SLR can be reduced over time by optimizing the adaptation strategies. We demonstrate the proposed scenario planning tool using available economic data and sea-level projections for Pinellas County, Florida, in a case study.

Latest technological improvements increased the quality of transportation. New data-driven approaches bring out a new research direction for all control-based systems, e.g., in transportation, robotics, IoT and power systems. Combining data-driven applications with transportation systems plays a key role in recent transportation applications. In this paper, the latest deep reinforcement learning (RL) based traffic control applications are surveyed. Specifically, traffic signal control (TSC) applications based on (deep) RL, which have been studied extensively in the literature, are discussed in detail. Different problem formulations, RL parameters, and simulation environments for TSC are discussed comprehensively. In the literature, there are also several autonomous driving applications studied with deep RL models. Our survey extensively summarizes existing works in this field by categorizing them with respect to application types, control models and studied algorithms. In the end, we discuss the challenges and open questions regarding deep RL-based transportation applications.

The stochastic nature of hospital bed demands and population growth rate in high migration areas poses significant challenges for the authorities to devise an appropriate hospital augmentation scheme. In this study, we propose a deep reinforcement learning (DRL) based model that can identify an appropriate hospital expansion plan for a particular geographical region of interest. Our proposed model analyzes the cost-benefit over a range of geographic regions and recommends the best capacity expansion area. We consider hospital bed numbers as a capacity determiner and population demographics for analyzing future demands economics in our approach. We divide a concerned geographic region into several sub-regions based on the local administrative body to recommend a sub-region where augmentation is necessary. The RL agent then works based on the age group, population growth, and current bed capacity utilizing the Advantage Actor-Critic (A2C) algorithm to minimize the cumulative cost. We also implemented our proposed approach for a case study in the Tampa Bay region, Florida, USA, to identify a hospital augmentation plan. The results from the case study verify this approach's superiority over traditional per capita-based and complaint-based policies.

Timely detection of abrupt anomalies is crucial for real-time monitoring and security of modern systems producing high-dimensional data. With this goal, we propose effective and scalable algorithms. Proposed algorithms are nonparametric as both the nominal and anomalous multivariate data distributions are assumed unknown. We extract useful univariate summary statistics and perform anomaly detection in a single-dimensional space. We model anomalies as persistent outliers and propose to detect them via a cumulative sum-like algorithm. In case the observed data have a low intrinsic dimensionality, we find a submanifold in which the nominal data are embedded and evaluate whether the sequentially acquired data persistently deviate from the nominal submanifold. Further, in the general case, we determine an acceptance region for nominal data via Geometric Entropy Minimization and evaluate whether the sequentially observed data persistently fall outside the acceptance region. We provide an asymptotic lower bound and an asymptotic approximation for the average false alarm period of the proposed algorithm. Moreover, we provide a sufficient condition to asymptotically guarantee that the decision statistic of the proposed algorithm does not diverge in the absence of anomalies. Experiments illustrate the effectiveness of the proposed schemes in quick and accurate anomaly detection in high-dimensional settings.

Internet of Things (IoT) networks consist of sensors, actuators, mobile and wearable devices that can connect to the Internet. With billions of such devices already in the market which have significant vulnerabilities, there is a dangerous threat to the Internet services and also some cyber-physical systems that are also connected to the Internet. Specifically, due to their existing vulnerabilities IoT devices are susceptible to being compromised and being part of a new type of stealthy Distributed Denial of Service (DDoS) attack, called Mongolian DDoS, which is characterized by its widely distributed nature and small attack size from each source. This article proposes a novel anomaly-based Intrusion Detection System (IDS) that is capable of timely detecting and mitigating this emerging type of DDoS attacks. The proposed IDS’s capability of detecting and mitigating stealthy DDoS attacks with even very low attack size per source is demonstrated through numerical and testbed experiments.

Recommender systems are widely used in electronic commerce, social media and online streaming services to provide personalized recommendations to the users by exploiting past ratings and interactions. This paper considers the security aspect with quick and accurate detection of attacks by observing the newly created profiles sequentially to prevent the damage which may be incurred by the injection of new profiles with dishonest ratings. The proposed framework consists of a latent variable model, which is trained by a variational EM algorithm, followed by a sequential detection algorithm. The latent variable model generates homogeneous representations of the users given their rating history and mixed data-type attributes such as age and gender. The representations are then exploited to generate univariate statistics to be efficiently used in a CUSUM-like sequential detection algorithm that can quickly detect persistent attacks while maintaining low false alarm rates. We apply our proposed framework to three different real-world datasets and exhibit superior performance in comparison to the existing baseline algorithms for both attack profile and sequential detection. Furthermore, we demonstrate robustness to different attack strategies and configurations.

Road damage detection is critical for the maintenance of a road, which traditionally has been performed using expensive high-performance sensors. With the recent advances in technology, especially in computer vision, it is now possible to detect and categorize different types of road damages, which can facilitate efficient maintenance and resource management. In this work, an ensemble model for efficient detection and classification of road damages, which has been submitted to the IEEE BigData Cup Challenge 2020. The solution utilizes a state-of-the-art object detector known as You Only Look Once (YOLO-v4), which is trained on images of various types of road damages from Czech, Japan and India. The ensemble approach was extensively tested with several different model versions and it was able to achieve an F1 score of 0.628 on the test 1 dataset and 0.6358 on the test 2 dataset.

Coastal communities are at high risk of natural hazards due to unremitting global warming and sea level rise. Both the catastrophic impacts, e.g., tidal flooding and storm surges, and the long-term impacts, e.g., beach erosion, inundation of low lying areas, and saltwater intrusion into aquifers, cause economic, social, and ecological losses. Creating policies through appropriate modeling of the responses of stakeholders, such as government, businesses, and residents, to climate change and sea level rise scenarios can help to reduce these losses. In this work, we propose a Markov decision process (MDP) formulation for an agent (government) which interacts with the environment (nature and residents) to deal with the impacts of climate change, in particular sea level rise. Through theoretical analysis we show that a reasonable government’s policy on infrastructure development ought to be proactive and based on detected sea levels in order to minimize the expected total cost, as opposed to a straightforward government that reacts to observed costs from nature. We also provide a deep reinforcement learning-based scenario planning tool considering different government and resident types in terms of cooperation, and different sea level rise projections by the National Oceanic and Atmospheric Administration (NOAA).

Smart grid is a large complex network with a myriad of vulnerabilities, usually operated in adversarial settings and regulated based on estimated system states. In this study, we propose a novel highly secure distributed dynamic state estimation mechanism for wide-area (multi-area) smart grids, composed of geographically separated subregions, each supervised by a local control center. We first propose a distributed state estimator assuming regular system operation that achieves near-optimal performance based on the local Kalman filters and with the exchange of necessary information between local centers. To enhance the security, we further propose to 1) protect the network database and the network communication channels against attacks and data manipulations via a blockchain (BC)-based system design, where the BC operates on the peer-to-peer network of local centers, 2) locally detect the measurement anomalies in real-time to eliminate their effects on the state estimation process, and 3) detect misbehaving (hacked/faulty) local centers in real-time via a distributed trust management scheme over the network. We provide theoretical guarantees regarding the false alarm rates of the proposed detection schemes, where the false alarms can be easily controlled. Numerical studies illustrate that the proposed mechanism offers reliable state estimation under regular system operation, timely and accurate detection of anomalies, and good state recovery performance in case of anomalies.

We propose a method for simultaneously detecting shared and unshared communities in heterogeneous multilayer weighted and undirected networks. The multilayer network is assumed to follow a generative probabilistic model that takes into account the similarities and dissimilarities between the communities. We make use of a variational Bayes approach for jointly inferring the shared and unshared hidden communities from multilayer network observations. We show that our approach outperforms state-of-the-art algorithms in detecting disparate (shared and private) communities on synthetic data as well as on real genome-wide fibroblast proliferation dataset.

Fog radio access network (F-RAN) has been recently proposed to satisfy the low-latency communication requirements of Internet of Things (IoT) applications. We consider the problem of sequentially allocating the limited resources of a fog node to a heterogeneous population of IoT applications with varying latency requirements. Specifically, for each service request, the fog node needs to decide whether to serve that user locally to provide it with low-latency communication service or to refer it to the cloud control center to keep the limited fog resources available for future users. We formulate the problem as a Markov Decision Process (MDP), for which we present the optimal decision policy through Reinforcement Learning (RL). The proposed resource allocation method learns from the IoT environment how to strike the right balance between two conflicting objectives, maximizing the total served utility and minimizing the idle time of the fog node. Extensive simulation results for various IoT environments corroborate the theoretical underpinnings of the proposed RL-based resource allocation method.

Malicious profiles have been a credible threat to collaborative recommender systems. Attackers provide fake item ratings to systematically manipulate the platform. Attack detection algorithms can identify and remove such users by observing rating distributions. In this study, we aim to use the user attributes as an additional information source to improve the accuracy and speed of attack detection. We propose a probabilistic factorization model which can embed mixed data type user attributes and observed ratings into a latent space to generate anomaly statistics for new users. To identify the persistent outliers in the system, we also propose a sequential attack detection algorithm to enable quick and accurate detection based on the probabilistic model learned from genuine users. The proposed model demonstrates significant improvements in both accuracy and speed when compared to baseline algorithms on a popular benchmark dataset.

As we enter the Internet of Things (IoT) era in which the communication network is becoming increasingly dynamic, heterogeneous, and complex, it is desirable to have cognitive communication systems and networks that possess multiple interacting capabilities for situation assessment, resource management, online/distributed learning, big-data processing, and intelligent decision making. AI techniques, such as deep learning, probabilistic graph model, and reinforcement learning, aided with big data and IoT, provide a wide variety of tools and solutions to many new problems encountered in the design, operation, and optimization of cognitive communication systems and networking, including resource management, situation assessment, channel identification, anomaly detection, root cause analysis, and online/distributed learning.

For a safe and reliable operation of the smart grid, timely detection of cyber-attacks is of critical importance. Moreover, considering smarter and more capable attackers, robust detection mechanisms are needed against a diverse range of cyber-attacks. With these purposes, we propose a robust online detection algorithm for (possibly combined) false data injection and jamming attacks, that also provides online estimates of the unknown and time-varying attack parameters and recovered state estimates. Further, considering smarter attackers that are capable of designing stealthy attacks to prevent the detection or to increase the detection delay of the proposed algorithm, we propose additional countermeasures. Numerical studies illustrate the quick and reliable response of the proposed detection mechanisms against hybrid and stealthy cyber-attacks.

In this paper, online detection of false data injection attacks and denial of service attacks in the smart grid is studied. The system is modeled as a discrete-time linear dynamic system and state estimation is performed using the Kalman filter. The generalized cumulative sum algorithm is employed for quickest detection of the cyber-attacks. Detectors are proposed in both centralized and distributed settings. The proposed detectors are robust to time-varying states, attacks, and set of attacked meters. Online estimates of the unknown attack variables are provided, that can be crucial for a quick system recovery. In the distributed setting, due to bandwidth constraints, local centers can only transmit quantized messages to the global center, and a novel event-based sampling scheme called level-crossing sampling with hysteresis is proposed that is shown to exhibit significant advantages compared with the conventional uniform-in-time sampling scheme. Moreover, a distributed dynamic state estimator is proposed based on information filters. Numerical examples illustrate the fast and accurate response of the proposed detectors in detecting both structured and random attacks and their advantages over existing methods.

The impact of cybersecurity attacks on the Smart Grid may cause cyber as well as physical damages, as clearly shown in the recent attacks on the power grid in Ukraine where consumers were left without power. A set of recent successful Distributed Denial-of-Service (DDoS) attacks on the Internet, facilitated by the proliferation of the Internet-of-Things powered botnets, shows that it is just a matter of time before the Smart Grid, as one of the most attractive critical infrastructure systems, becomes the target and likely victim of similar attacks, potentially leaving catastrophic disruption of power service to millions of people. It is in this context that we propose a scalable mitigation approach, referred to as Minimally Invasive Attack Mitigation via Detection Isolation and Localization (MIAMI-DIL), under a hierarchical data collection infrastructure. We provide a proofof- concept by means of simulations which show the efficacy and scalability of the proposed approach.

We consider the online and nonparametric detection of abrupt and persistent anomalies, such as a change in the regular system dynamics at a time instance due to an anomalous event (e.g., a failure, a malicious activity). Combining the simplicity of the nonparametric Geometric Entropy Minimization (GEM) method with the timely detection capability of the Cumulative Sum (CUSUM) algorithm we propose a computationally efficient online anomaly detection method that is applicable to high-dimensional datasets, and at the same time achieve a near-optimum average detection delay performance for a given false alarm constraint. We provide new insights to both GEM and CUSUM, including new asymptotic analysis for GEM, which enables soft decisions for outlier detection, and a novel interpretation of CUSUM in terms of the discrepancy theory, which helps us generalize it to the nonparametric GEM statistic. We numerically show, using both simulated and real datasets, that the proposed nonparametric algorithm attains a close performance to the clairvoyant parametric CUSUM test.

The small wavelength at millimeter wave (mmWave) allows to pack antenna arrays in a very small area enabling practical large-scale MIMO. However, wideband and high-precision analog-to-digital converters (ADCs) are very expensive and power-hungry. In this paper, we propose a mmWave MIMO communication system that employs simple one-bit ADCs at the receiver and MIMO precoding at the transmitter. One significant challenge associated with this system is efficient channel estimation based on one-bit quantized channel output. We propose a novel continuous-time channel estimator based on level-triggered sampling, a nonuniform sampling scheme that offers estimation performance similar to that of the estimator based on full-precision channel output. We also develop the transmitter precoding scheme that reduces the transmit peak-to-average power ratio (PAPR) and, at the same time, equalizes the antenna cross-talks so that receiver demodulation can be implemented simply by symbol-rate slicing. Extensive simulation results are provided to demonstrate the effectiveness of the proposed channel estimation and precoding techniques for mmWave systems.

Dynamic spectrum access under channel uncertainties is considered. With the goal of maximizing the secondary user (SU) throughput subject to constraints on the primary user (PU) outage probability we formulate a joint problem of spectrum sensing and channel state estimation. The problem is cast into a sequential framework since sensing time minimization is crucial for throughput maximization. In the optimum solution, the sensing decision rule is coupled with the channel estimator, making the separate treatment of the sensing and channel estimation strictly suboptimal. Using such a joint structure for spectrum sensing and channel estimation we propose a distributed (cooperative) dynamic spectrum access scheme under statistical channel state information (CSI). In the proposed scheme, the SUs report their sufficient statistics to a fusion center (FC) via level-triggered sampling, a nonuniform sampling technique that is known to be bandwidth-and-energy efficient. Then, the FC makes a sequential spectrum sensing decision using local statistics and channel estimates, and selects the SU with the best transmission opportunity. The selected SU, using the sensing decision and its channel estimates, computes the transmit power and starts data transmission. Simulation results demonstrate that the proposed scheme significantly outperforms its conventional counterparts, under the same PU outage constraints, in terms of the achievable SU throughput.

We consider the problem of decentralized scalar parameter estimation using wireless sensor networks with Gaussian noise. Specifically, we propose a novel framework based on level-triggered sampling, a non-uniform sampling strategy, and sequential estimation. The proposed estimator can be used as an asymptotically optimal fixed-sample-size decentralized estimator when the observed Fisher information, i.e., Fisher information without expectation, is deterministic, as an alternative to the one-shot estimators commonly found in the literature. It can also be used as an asymptotically optimal sequential decentralized estimator when the observed Fisher information is random. We show that the optimal centralized estimator under Gaussian noise, which is the maximum likelihood estimator, is characterized by two processes, namely the observed Fisher information Ut and the observed correlation Vt. It is noted that Vt is always random even when Ut is not. In the proposed scheme, each sensor computes its local random processes, and sends a single bit to the fusion center (FC) whenever the local random processes passes certain predefined levels. The FC, upon receiving a bit from a sensor, updates its approximation to the corresponding global random process and, accordingly, its estimate. The sequential estimation process terminates when Ut (or the approximation to it) reaches a target value. We provide an asymptotic analysis for the proposed estimator and the one based on conventional uniform-in-time sampling under both deterministic and random Ut, and determine the conditions under which they are asymptotically optimal, consistent, and asymptotically unbiased. Analytical results, together with simulation results, demonstrate the superiority of the proposed estimator based on level-triggered sampling over the traditional decentralized estimator based on uniform sampling.

We develop optimal centralized sequential estimators under different formulations of the problem. Decentralized sequential estimation is also considered for wireless sensor networks. We propose an asymptotically optimal decentralized scheme based on level-triggered sampling, a non-uniform sampling technique. Performance of the proposed scheme is analyzed.

We consider decentralized detection for wireless sensor networks. A sequential scheme based on level-triggered sampling is proposed. In the proposed scheme, sensors compute log-likelihood ratio (LLR) of their local observations, sample local LLR using level-triggered sampling and transmit a single bit at each sampling time to a fusion center (FC). At each sampling time excess LLR over (below) sampling threshold is linearly encoded in time. The FC, upon receiving a bit from a sensor, decodes excess LLR and updates approximate global LLR, which it uses as test statistic. An SPRT-like test is used by the FC to reach a final decision. We show that the proposed scheme achieves order-2 asymptotic optimality by using only a single bit for each sample, thanks to time-encoding the overshoot.

We consider sequential nonlinear prediction of a bounded, real-valued and deterministic signal from its noise-corrupted past samples in a competitive algorithm framework. We introduce a randomized algorithm based on context-trees . The introduced algorithm asymptotically achieves the performance of the best piecewise affine model that can both select the best partition of the past observations space (from a doubly exponential number of possible partitions) and the affine model parameters based on the desired clean signal in hindsight. Although the performance measure including the loss function is defined with respect to the noise-free clean signal, the clean signal, its past samples or prediction errors are not available for training or constructing predictions. We demonstrate the performance of the introduced algorithm when applied to certain chaotic signals.

The Krylov proportionate normalized least mean square (KPNLMS) algorithm extended the use of proportional update idea of the PNLMS (proportionate normalized LMS) algorithm to the non-sparse (dispersive) systems. This paper deals with the mean fourth minimization of the error and proposes Krylov proportionate normalized least mean fourth algorithm (KPNLMF). First, the PNLMF (proportionate NLMF) algorithm is derived, then Krylov subspace projection technique is applied to the PNLMF algorithm to obtain the KPNLMF algorithm. While fully exploiting the fast convergence property of the PNLMF algorithm, the system to be identified does not need to be sparse in the KPNLMF algorithm due to the Krylov subspace projection technique. In our simulations, the KPNLMF algorithm converges faster than the KPNLMS algorithm when both algorithms converge to the same system mismatch value. The KPNLMF algorithm achieves this without any increase in the computational complexity. Further numerical examples comparing the KPNLMF with the NLMF and the KPNLMS algorithms support the fast convergence of the KPNLMF algorithm.