This work presents a novel machine learning and signal processing framework designed to consistently detect, localize, and rate facial anomalies such as cleft lip deformity. The goal of this research is to establish a universal and objective measure of facial abnormalities, capable of sensitively identifying both subtle and significant deformities. The proposed model utilizes an enhanced two-phase automatic inpainting method for face normalization, effectively removing anomalies from the image and replacing them with normal facial content. The framework leverages an efficient knowledge distillation model to estimate the initial heatmap that highlights potential facial anomalies. This heatmap is subsequently converted into a mask for inpainting, which is applied to normalize the original face. A deep convolutional neural network (CNN)-based feature extraction method is then employed to compare the anomalous facial image with its normalized counterpart, enabling robust detection and evaluation of various facial anomalies. This is achieved by obtaining a noise-reduced final heatmap that more accurately scores the level of normality in the face. The normalization protocol delivers results comparable to state-of-the-art methods, while being significantly faster, taking less than one second from image upload to obtaining the face rating. This makes it highly feasible for deployment in mobile applications. Additionally, the proposed method does not require anomalous data for model training, while efficiently detecting and assessing various facial anomalies. We demonstrate that this unique computerized image appraisal system generates facial normality/abnormality scores that closely correlate with human intuition, exhibiting 92% correlation with human scores.

As a sustainable participant in the modernization of transportation systems, electric vehicles (EVs) call for a well-planned charging infrastructure. To meet the ever-increasing charging demands of EVs, an efficient dynamic spatio-temporal allocation strategy of charging stations (CSs) is necessary. With newly allocated CSs, additional distributed generators (DGs) are required to compensate for the load increase. Given a budget to be allocated over a certain time horizon, we formulate the joint spatio-temporal CSs and DGs planning problem as a multi-objective optimization problem. During each planning period, the allocation strategy aims at minimizing the total power generation costs and CSs/DGs installation costs while satisfying budgetary and power constraints and ensuring a minimum level for the charging requests satisfaction rate. In this regard, we first predict the future power demand of EVs using a graph convolutional neural network (GCNN). Then, using the power demand forecast, we obtain the optimal number and locations of CSs and DGs at each time stage using reinforcement learning. A case study of the proposed allocation strategy over 6 time stages for the 2000-bus power grid of Texas coupled with 720 initially existing CSs is presented to illustrate the performance of the planning strategy.

Cardiac arrhythmia is one of the prime reasons for death globally. Early diagnosis of heart arrhythmia is crucial to provide timely medical treatment. Heart arrhythmias are diagnosed by analyzing the electrocardiogram (ECG) of patients. Manual analysis of ECG is time-consuming and challenging. Hence, effective automated detection of heart arrhythmias is important to produce reliable results. Different deep-learning techniques to detect heart arrhythmias such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Transformer, and Hybrid CNN-LSTM were proposed. However, these techniques, when used individually, are not sufficient to effectively learn multiple features from the ECG signal. The fusion of CNN and LSTM overcomes the limitations of CNN in the existing studies as CNN-LSTM hybrids can extract spatiotemporal features. However, LSTMs suffer from long-range dependency issues due to which certain features may be ignored. Hence, to compensate for the drawbacks of the existing models, this paper proposes a more comprehensive feature fusion technique by merging CNN, LSTM, and Transformer models. The fusion of these models facilitates learning spatial, temporal, and long-range dependency features, hence, helping to capture different attributes of the ECG signal. These features are subsequently passed to a majority voting classifier equipped with three traditional base learners. The traditional learners are enriched with deep features instead of handcrafted features. Experiments are performed on the MIT-BIH arrhythmias database and the model performance is compared with that of the state-of-art models. Results reveal that the proposed model performs better than the existing models yielding an accuracy of 99.56%.

This study investigates the performance of visible light communications (VLCs) in the presence of blockages. An indoor office scenario with a single VLC access point serving the user nodes in the presence of human blockages is examined. System performance is assessed through closed-form expressions for outage probability and symbol error rate for binary phase shift keying and quadrature amplitude modulation. A deep neural network for symbol detection is deployed at the receiver. Performance metrics illustrate that the blockages cause significant impact on signal detection. Computer simulations corroborate the correctness of the obtained analytical expressions.

False data injection attacks (FDIAs) on smart power grids’ measurement data present a threat to system stability. When malicious entities launch cyberattacks to manipulate the measurement data, different grid components will be affected, which leads to failures. For effective attack mitigation, two tasks are required: determining the status of the system (normal operation/under attack) and localizing the attacked bus/power substation. Existing mitigation techniques carry out these tasks separately and offer limited detection performance. In this paper, we propose a multi-task learning-based approach that performs both tasks simultaneously using a graph neural network (GNN) with stacked convolutional Chebyshev graph layers. Our results show that the proposed model presents superior system status identification and attack localization abilities with detection rates of 98.5−100% and 99 − 100%, respectively, presenting improvements of 5 − 30% compared to benchmarks.

Photovoltaic (PV) power is progressively being subsumed into power grids. Consequently, reliable PV power forecasting (PVPF) has become essential to avoid ramp events that can adversely affect the operations of integrated power systems. This article presents a deep-learning-based algorithm for PVPF. The gated recurrent units (GRU) network was implemented to predict the non-linear spatiotemporal correlations of the weather data, leading to higher reliability of the PV stations. Experimental results obtained from actual testing demonstrate the validity of the GRU networks for accurate PVPF, contributing to the efficient operation and management of smart grids and renewable energy systems. The conducted case study shows that the proposed model outperforms bidirectional long short term memory (BiLSTM) and long short term memory (LSTM) models in terms of computation power, root-mean-square error, and mean absolute error metrics.

Twelve lead electrocardiogram signals capture unique fingerprints about the body's biological processes and electrical activity of heart muscles. Machine learning and deep learning-based models can learn the embedded patterns in the electrocardiogram to estimate complex metrics such as age and gender that depend on multiple aspects of human physiology. ECG estimated age with respect to the chronological age reflects the overall well-being of the cardiovascular system, with significant positive deviations indicating an aged cardiovascular system and a higher likelihood of cardiovascular mortality. Several conventional, machine learning, and deep learning-based methods have been proposed to estimate age from electronic health records, health surveys, and ECG data. This manuscript comprehensively reviews the methodologies proposed for ECG-based age and gender estimation over the last decade. Specifically, the review highlights that elevated ECG age is associated with atherosclerotic cardiovascular disease, abnormal peripheral endothelial dysfunction, and high mortality, among many other cardiovascular disorders. Furthermore, the survey presents overarching observations and insights across methods for age and gender estimation. This paper also presents several essential methodological improvements and clinical applications of ECG-estimated age and gender to encourage further improvements of the state-of-the-art methodologies.

This article proposes using communication pipelining to enhance the convergence speed of federated learning in mobile edge computing applications. Due to limited wireless subchannels, a subset of the total clients is scheduled in each iteration of federated learning algorithms. On the other hand, the scheduled clients wait for the slowest client to finish its computation. We propose to first cluster the clients based on the time they need per iteration to compute the local gradients of the federated learning model. Then, we schedule a mixture of clients from all clusters to send their local updates in a pipelined manner. In this way, instead of just waiting for the slower clients to finish their computations, more clients can participate in each iteration. While the time duration of a single iteration does not change, the proposed method can significantly reduce the number of required iterations to achieve a target accuracy. We provide a generic formulation for optimal client clustering under different settings, and we analytically derive an efficient algorithm for obtaining the optimal solution. We also provide numerical results to demonstrate the gains of the proposed method for different data sets and deep learning architectures.

Electricity theft cyber-attacks pose significant threats to smart power grids. In these attacks, malicious customers hack into their smart meters and manipulate the integrity of their energy consumption readings to reduce their electricity bills. Recently, machine learning techniques have been successfully employed to detect such cyber-attacks. However, the developed detectors have been tested against simple attacks. In this paper, we investigate the performance of electricity theft detectors against evasion attacks that are designed to reduce the reported value of the energy consumption and at the same time fool the machine learning-based detector model via adversarial samples. Furthermore, we propose a strong evasion attack that significantly degrades the performance of a set of benchmark detectors. Our results reveal that evasion attacks can deteriorate the detection rate (DR) and false alarm (FA) rate by ~ 20%. To address such evasion attacks, we propose an ensemble learning-based detector that integrates auto-encoder with attention (AEA), long-short-term-memory (LSTM), and feed forward deep neural networks. The developed detector maintains a stable detection performance against evasion attacks with a deterioration in performance by only 1 − 5% in DR and FA.

Optical wireless networks, especially those relying on visible light communications, suffer from severe deterioration in signal's quality when the line-of-sight (LOS) link is absent due to user's mobility. In order to enable efficient resource management within such networks, reliable prediction of LOS link outage is essential. Towards this objective, this article proposes a data-driven approach based on deep machine learning techniques to predict the outage events in an LOS link. First, we present a framework to generate sufficient data representing the channel gain in mobile optical wireless networks that consist of visible light communications in the downlink and infrared communications in the uplink. Using the developed dataset, we propose a channel predictor that forecasts the burst outages or signal recoveries in the upcoming frames using a deep recurrent neural network that implements long-short-term-memory (LSTM) units. To achieve this goal, we propose a low-complexity approach to reduce the data sparsity due to the user's mobility by abstracting and densifying the channel state sequence. For a one second prediction interval, the proposed prediction framework achieves an event hit rate of 91.55% for abrupt outages with an average event timing error of 79 ms, and 83.19% for recoveries from outages with 145 ms timing error. This timing error is on the same order of magnitude with the coherence time of the optical wireless channel. Therefore, this predictor is very useful in developing efficient resource management strategies in such optical networks.

Smart grids are faced with the challenge of meeting the ever increasing load demands of electric vehicles (EVs). To provide acceptable charging services, operators need to be equipped with an efficient charging stations (CSs) planning strategy. Unfortunately, existing planning solutions are quite limited. They normally rely on standard IEEE bus systems or power grids that are specific to certain cities. In this paper, using stochastic geometry, we formulate the CSs planning on a stochastic geometry-based power grid model, that we previously showed to mimic real-world power grids. We study the effect of the density of EVs and the technical charging constraints on the minimum number of CSs to install.

The tight interaction between power grids and communication networks supports the advanced functionalities of smart grids and enables a more efficient utilization of the assets within the power system. Towards this objective, it is of utmost importance to develop a model that reflects such an interaction. Unfortunately, existing cyber-physical interdependent models are conceptual or are specific to certain regions and do not consider the spatial information and correlation of electrical elements. To address these limitations, in this paper, we propose an interdependent communication-power network model using tools from stochastic geometry, which takes into consideration the spatial locations of electrical buses and the required service quality within the communication network. A step-by-step process on building a communication network on top of the power grid while satisfying the average queuing delay and queuing stability constraints is described. A case study for a realization of this generative interdependent model is presented.

This article investigates a heterogeneous network (HetNet) consisting of an overlapped coverage of radio frequency (RF) and optical wireless communication (OWC) to support human connectivity to the Internet of Things (IoT) in the fifth generation and beyond (5G+) mobile networks. Such a HetNet-based IoT benefits from the high throughput of the OWC and the high reliability of the RF communications. To ensure a reliable link with Quality-of-Service (QoS) guarantee in terms of network delay and throughput, vertical handovers are triggered within the HetNet. A cross-layer data-driven approach is adopted to reach optimal handover decisions and tackle the challenges associated with the mobility and reliability of IoT. As mobile time-varying wireless channel gains in optical links are not publicly available, we first present a realistic model for the mobile optical channel that reflects the nature of human mobility, and hence, a useful model that features the spatial-temporal patterns of indoor mobile channels is proposed. Using the created data set, we then present a data-driven algorithm that predicts abrupt outages in Line-of-Sight (LOS) optical links and evaluates the optical channel quality through deep learning. Given the resulting LOS link outage prediction in OWC, a reinforcement-learning-based approach is proposed to implement optimal vertical handover decisions with the QoS guarantee. The proposed handover decision algorithm learns to make a tradeoff between the outage risk and the cost of excessive handovers. The numerical results demonstrate considerable improvement in overall latency and handover rate under indoor mobility for bidirectional links.

The study of recurrent copy number variations (CNVs) plays an important role in understanding the onset and evolution of complex diseases such as cancer. Array-based comparative genomic hybridization (aCGH) is a widely used microarray based technology for identifying CNVs. However, due to high noise levels and inter-sample variability, detecting recurrent CNVs from aCGH data remains a challenging topic. This paper proposes a novel method for identification of the recurrent CNVs. In the proposed method, the noisy aCGH data is modeled as the superposition of three matrices: a full-rank matrix of weighted piece-wise generating signals accounting for the clean aCGH data, a Gaussian noise matrix to model the inherent experimentation errors and other sources of error, and a sparse matrix to capture the sparse inter-sample (sample-specific) variations. We demonstrated the ability of our method to separate accurately recurrent CNVs from sample-specific variations and noise in both simulated (artificial) data and real data. The proposed method produced more accurate results than current state-of-the-art methods used in recurrent CNV detection and exhibited robustness to noise and sample-specific variations.

Link instability induced by users' mobility is one of the challenges of optical wireless communications (OWC) inherited from the propagation nature of light. Hence, good understanding of the optical channel characteristics in dynamic environments plays a vital role in developing robust resource management strategies in OWC networks. Unfortunately, it is quite difficult to collect accurate indoor optical channel data in dynamic environments. In addition, indoor trajectory dataset is not publicly available. To overcome such limitations, this paper proposes a mobile terminal-centric analytical framework that captures the propagation channel characteristics in a mobile OWC network whose downlink is based on visible light and uplink is based on infrared light. We abstract the nature of human behavior by integrating both macro and micro mobility patterns. These patterns are then used to realize the spatio-temporal characteristics of optical wireless channels under long-term environment-confined mobility. The statistics derived from the developed framework indicate that the mobile line-of-sight (LOS) channel gain follows space-time-dependent multiple-peak Nakagami distributions, whereas the non-line-of-sight (NLOS) channel gain adheres to various space-time-dependent single peak distributions under different indoor layouts. The overall distribution of NLOS bandwidth follows space-time-dependent multiple-peak log-logistic distributions in downlinks and space-time-dependent generalized log-logistic distributions in uplinks. Our investigation demonstrates that the indoor layout and the user's environment-confined mobility pattern significantly impact the LOS dynamics but present limited impact on NLOS components. Motivated by the need for better channel models for mobile OWC, the proposed framework fills up an important gap in literature and help the research community to understand better the indoor optical wireless channel characteristics.

Wavelength-domain multiplexing enables near parallel transmission for visible light communications (VLC). In this paper, we investigate an optimal bias allocation problem for the tri-color VLC channel when there is no color cross-talk (CoC), and propose a joint precoding and bias allocation algorithm when CoC exists. The optimization problems are formulated considering both lighting and communication requirements. The color constraint is depicted using the MacAdam ellipses on the chromaticity diagram, as an effective relaxation from a fixed point restriction. Also, for practical concerns, the addressed system is affected by signal-dependent noise (SDN). The bias allocation problem under SDN and related constraints is shown to be non-convex, thus a convex-concave procedure is utilized in this paper for convexification. The joint design problem is also non-convex, and an iterative optimization procedure is adopted, where the decoder is of Wiener filter form. Simulation results are given to show the impacts of SDN, CoC, and steps of MacAdam ellipses on system performance.

This paper focuses on the development of energy efficient visible light communication (VLC) networks. More specifically, since the quality-of-service and energy cost are key parameters in designing energy efficient networks, this paper optimizes the VLC base station (BS) density to minimize the area power consumption (APC) under an outage probability constraint. Using stochastic geometry, approximations of the outage probability of VLC networks are first introduced. The derived approximations are applicable to an arbitrary field-of-view at photodiodes and present low computational complexities. Leveraging the derived analytical results, a low complexity algorithm to find the VLC BS density that minimizes the APC of VLC networks is then proposed. The numerical simulations corroborate the tightness of the approximations on the outage probability and confirm that the proposed algorithm exhibits almost identical performance as the algorithm that exhaustively searches the optimal BS density.

In this paper, we propose privacy-preserving fine-grained data retrieval schemes for mobile social networks (MSNs). The schemes enable users to retrieve data from other users who are interested in some topics related to a subject of interest. We define a subject to be a broad term that can cover many fine-grained topics, e.g., History can be a subject and World War I can be a topic. We consider centralized and decentralized network models. Our centralized scheme allows users to securely outsource data to a server such that the server matches the users who are interested in same topic(s) and have defined social attributes with privacy preservation. Searchable encryption scheme and a proposed cryptography construct are used to enable the server to match the topics and attributes without knowing any private information. By using the social attributes, users can prescribe the other users who can be connected to. We also propose a decentralized scheme that can be used when there is no connection to the server, i.e, shortage of Internet connectivity. The scheme leverages friends-of-friends relationship and transferable trust concept, where each user trusts his friends and the friends of friends. If a friend is not interested in the requested subject, he/she can link him/her to his/her friends without knowing the requested subject to preserve privacy. Our schemes use Bloom filters to store the topics of interest to reduce the storage and communication overhead. This is important because the number of fine-grained topics can be large. Different techniques to store the topics in the filter are proposed and investigated. Performance metrics are proposed and evaluated using real implementations. Our analysis and implementation results demonstrate that our schemes can preserve the privacy of the MSN users with high performance.

This paper focuses on the development of energy efficient hybrid networks consisting of radio frequency (RF) base stations (BSs) and visible light communication (VLC) BSs. More specifically, since the quality-of-service and energy cost are key parameters in designing energy efficient networks, this paper optimizes the RF BS and VLC BS intensities to minimize the area power consumption (APC) under an outage probability constraint. Using stochastic geometry, approximations of the outage probability of VLC networks, which are applicable to an arbitrary field-of-view at photodiodes and present low computational complexities, are first introduced. Leveraging the derived analytical results, a low complexity algorithm to find the VLC BS intensity that minimizes the APC of VLC networks is then proposed. Furthermore, algorithms to identify the intensities of RF BSs and VLC BSs for energy efficient hybrid RF/VLC networks via one-dimensional search methods are also developed. The numerical simulations corroborate the tightness of the approximations on the outage probability and confirm that the proposed algorithms exhibit almost identical performances as the algorithms that exhaustively search the optimal BS intensities. Finally, it is shown that the hybrid RF/VLC networks achieve a lower outage probability with a reduced APC compared to the RF-only networks and VLC-only networks.

In this work, the joint optimization of sensing threshold and power allocation strategy in orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) networks is studied. The aim is to maximize the total capacity of secondary users (SUs) while limiting the interference caused to the primary users (PUs). Taking into account the sensing errors, the total capacity of SUs is expressed as a function of the sensing threshold and SUs' transmit power. The resulting problem represents a nonconvex mixed integer non-linear programming (MINLP) optimization. Due to the high computational complexity of the MINLP problem, an equivalent simplified problem is formulated without resorting to integer variables. A computationally efficient suboptimal algorithm is proposed to address the joint optimization problem. Using the suboptimal solution as the initial point, an alternating optimization method is then employed to enhance the solution accuracy. Computer simulation results confirm the effectiveness of the proposed algorithm.

Copy number variations (CNVs) play a role in the development of several diseases, including cancer. The detection or recurrent CNVs enables us to study the regions in which they occur and understand their contribution to the formation of disease. Microarray technologies, and Array-based comparative genomic hybridization (a C GH) in particular, have been widely used in the detection of CNVs. However, due to inter-sample variability and high noise levels, simple pattern detection methods experience significant challenges in recovering the recurrent CNV regions. In this work, we propose a new method for detecting recurrent CNV regions. To achieve this goal, we propose a matrix decomposition method in which the observed aCGH probe values are estimated using two elements: i) we use a full-rank matrix of weighted piece-wise generator signals to recover the recurrent CNVs. ii) We use a Gaussian matrix combined with a sparse matrix to capture the different types of noise and outlier values. We then evaluate the ability of our method to detect recurrent CNVs from several noisy simulated and real datasets. The results showed that our method is able to detect recurrent CNVs more accurately than current methods. Our method returned clean signals, exhibiting robustness to noise and outlier probe values.

Modern smart grids rely on advanced metering infrastructure (AMI) networks for monitoring and billing purposes. However, such an approach suffers from electricity theft cyberattacks. Different from the existing research that utilizes shallow, static, and customer-specific-based electricity theft detectors, this paper proposes a generalized deep recurrent neural network (RNN)-based electricity theft detector that can effectively thwart these cyberattacks. The proposed model exploits the time series nature of the customers' electricity consumption to implement a gated recurrent unit (GRU)-RNN, hence, improving the detection performance. In addition, the proposed RNN-based detector adopts a random search analysis in its learning stage to appropriately fine-tune its hyper-parameters. Extensive test studies are carried out to investigate the detector's performance using publicly available real data of 107,200 energy consumption days from 200 customers. Simulation results demonstrate the superior performance of the proposed detector compared with state-of-the-art electricity theft detectors.

Advanced metering infrastructure (AMI) networks are vulnerable against electricity theft cyber attacks. Different from the existing research that exploits shallow machine learning architectures for electricity theft detection, this paper proposes a deep neural network (DNN)-based customer-specific detector that can efficiently thwart such cyber attacks. The proposed DNN-based detector implements a sequential grid search analysis in its learning stage to appropriately fine tune its hyper-parameters, hence, improving the detection performance. Extensive test studies are carried out based on publicly available real energy consumption data of 5000 customers and the detector's performance is investigated against a mixture of different types of electricity theft cyber attacks. Simulation results demonstrate a significant performance improvement compared with state-of-the-art shallow detectors.

BACKGROUND: Analyzing Variance heterogeneity in genome wide association studies (vGWAS) is an emerging approach for detecting genetic loci involved in gene-gene and gene-environment interactions. vGWAS analysis detects variability in phenotype values across genotypes, as opposed to typical GWAS analysis, which detects variations in the mean phenotype value. RESULTS: A handful of vGWAS analysis methods have been recently introduced in the literature. However, very little work has been done for evaluating these methods. To enable the development of better vGWAS analysis methods, this work presents the first quantitative vGWAS simulation procedure. To that end, we describe the mathematical framework and algorithm for generating quantitative vGWAS phenotype data from genotype profiles. Our simulation model accounts for both haploid and diploid genotypes under different modes of dominance. Our model is also able to simulate any number of genetic loci causing mean and variance heterogeneity. CONCLUSIONS: We demonstrate the utility of our simulation procedure through generating a variety of genetic loci types to evaluate common GWAS and vGWAS analysis methods. The results of this evaluation highlight the challenges current tools face in detecting GWAS and vGWAS loci.

Network component analysis (NCA) is an important method for inferring transcriptional regulatory networks (TRNs) and recovering transcription factor activities (TFAs) using gene expression data, and the prior information about the connectivity matrix. The algorithms currently available crucially depend on the completeness of this prior information. However, inaccuracies in the measurement process may render incompleteness in the available knowledge about the connectivity matrix. Hence, computationally efficient algorithms are needed to overcome the possible incompleteness in the available data. We present a sparse network component analysis algorithm (sparseNCA), which incorporates the effect of incompleteness in the estimation of TRNs by imposing an additional sparsity constraint using the norm, which results in a greater estimation accuracy. In order to improve the computational efficiency, an iterative re-weighted method is proposed for the NCA problem which not only promotes sparsity but is hundreds of times faster than the norm based solution. The performance of sparseNCA is rigorously compared to that of FastNCA and NINCA using synthetic data as well as real data. It is shown that sparseNCA outperforms the existing state-of-the-art algorithms both in terms of estimation accuracy and consistency with the added advantage of low computational complexity. The performance of sparseNCA compared to its predecessors is particularly pronounced in case of incomplete prior information about the sparsity of the network. Subnetwork analysis is performed on the E.coli data which reiterates the superior consistency of the proposed algorithm.

In this paper, the joint spectrum sensing and resource allocation problem is investigated in a multi-band-multiuser cognitive radio (CR) network. Assuming imperfect spectrum sensing information, our goal is to jointly optimize the sensing threshold and power allocation strategy such that the average total throughput of secondary users (SUs) is maximized. In Addition, the power of SUs is constrained to keep the interference introduced to primary users under certain limit, which gives rise to a nonconvex mixed integer non-linear programming (MINLP) optimization problem. Our contribution in this paper is threefold. First, it is illustrated that the dimension of the nonconvex MINLP problem can be significantly reduced, which helps to re-formulate the optimization problem without resorting to integer variables. Second, it is demonstrated that the simplified formulation admits the canonical form of a monotonic optimization, and an E-optimal solution can be achieved using the polyblock outer approximation algorithm. Third, a practical low-complexity spectrum sensing and resource allocation algorithm is developed to reduce the computational cost. Finally, the effectiveness of proposed algorithms is verified by simulations.

In this paper, we study the downlink resource allocation (RA) problem in orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) networks. Our goal is to maximize the aggregated capacity of secondary users (SUs). In addition, the power of SUs is controlled to keep the interference introduced to primary users (PUs) under certain limits, which gives rise to a non-convex mixed integer non-linear programming (MINLP) optimization problem. In this paper, it is illustrated that the non-convex MINLP formulation admits a special structure and the optimal solution can be always achieved using standard convex optimization techniques under a general and practical assumption. In particular, the subgradient method is adopted to address the problem in the dual domain. The effectiveness of the proposed algorithms is verified by simulations.

BACKGROUND: Biomarker detection presents itself as a major means of translating biological data into clinical applications. Due to the recent advances in high throughput sequencing technologies, an increased number of metagenomics studies have suggested the dysbiosis in microbial communities as potential biomarker for certain diseases. The reproducibility of the results drawn from metagenomic data is crucial for clinical applications and to prevent incorrect biological conclusions. The variability in the sample size and the subjects participating in the experiments induce diversity, which may drastically change the outcome of biomarker detection algorithms. Therefore, a robust biomarker detection algorithm that ensures the consistency of the results irrespective of the natural diversity present in the samples is needed. RESULTS: Toward this end, this paper proposes a novel Regularized Low Rank-Sparse Decomposition (RegLRSD) algorithm. RegLRSD models the bacterial abundance data as a superposition between a sparse matrix and a low-rank matrix, which account for the differentially and non-differentially abundant microbes, respectively. Hence, the biomarker detection problem is cast as a matrix decomposition problem. In order to yield more consistent and solid biological conclusions, RegLRSD incorporates the prior knowledge that the irrelevant microbes do not exhibit significant variation between samples belonging to different phenotypes. Moreover, an efficient algorithm to extract the sparse matrix is proposed. Comprehensive comparisons of RegLRSD with the state-of-the-art algorithms on three realistic datasets are presented. The obtained results demonstrate that RegLRSD consistently outperforms the other algorithms in terms of reproducibility performance and provides a marker list with high classification accuracy. CONCLUSIONS: The proposed RegLRSD algorithm for biomarker detection provides high reproducibility and classification accuracy performance regardless of the dataset complexity and the number of selected biomarkers. This renders RegLRSD as a reliable and powerful tool for identifying potential metagenomic biomarkers.

In vivo wireless body area networks and their associated technologies are shaping the future of healthcare by providing continuous health monitoring and noninvasive surgical capabilities, in addition to remote diagnostic and treatment of diseases. To fully exploit the potential of such devices, it is necessary to characterize the communication channel, which will help to build reliable and high-performance communication systems. This paper presents an in vivo wireless communication channel characterization for male torso both numerically and experimentally (on a human cadaver) considering various organs at 915 MHz and 2.4 GHz. A statistical path loss (PL) model is introduced, and the anatomical region-specific parameters are provided. It is found that the mean PL in decibel scale exhibits a linear decaying characteristic rather than an exponential decaying profile inside the body, and the power decay rate is approximately twice at 2.4 GHz as compared to 915 MHz. Moreover, the variance of shadowing increases significantly as the in vivo antenna is placed deeper inside the body since the main scatterers are present in the vicinity of the antenna. Multipath propagation characteristics are also investigated to facilitate proper waveform designs in the future wireless healthcare systems, and a root-mean-square delay spread of 2.76 ns is observed at 5 cm depth. Results show that the in vivo channel exhibit different characteristics than the classical communication channels, and location dependence is very critical for accurate, reliable, and energy-efficient link budget calculations.

In this paper, a 5G energy-constrained network is considered, where a non-orthogonal multiple access (NOMA) scheme is applied at the multiple users. The outage probability and the ergodic rate are studied as two benchmarks to evaluate the system performance. First, a closed-form expression for the exact outage probability is derived. A lower bound of the outage probability is obtained and shown to predict well the system performance. In addition, the outage probability in the high signal-to-interference-plus-noise ratio (SINR) regime is studied to assess its upper limit while achieving the diversity order. Second, a closed-form expression for the upper bound of the ergodic rate is obtained. Then the ergodic rate in the high SINR regime is also analyzed. Finally, numerous computer simulations are performed to corroborate the superior performance of NOMA relative to orthogonal multiple access schemes and the validity of the derived analytical results.

In literature, dynamic planning (base station (BS) on-off switching) is proposed as an efficient approach for energy saving at a low call traffic load condition. All related research efforts aim to employ dynamic planning to save energy for the network operators while satisfying the quality-of-service (QoS) for downlink mobile users. On the other hand, although switching off a BS can save energy for network operators and satisfy the downlink QoS, it may lead to associating uplink mobile users with a faraway BS, and hence, degrade the uplink QoS. Such an impact of dynamic planning on uplink service quality is not well investigated in literature. In this paper, we aim to quantify this impact by considering two QoS metrics, namely, user required throughput and call dropping probability due to mobile terminal battery depletion for uplink data calls. Simulation results demonstrate that BS switching off decisions that do not account for the uplink QoS requirements can result in severe service quality degradation.

In this paper, a novel devices-to-device (Ds2D) communication paradigm is proposed to enable green (energy efficient) wireless networks. Different from the conventional D2D communications, the sink device establishes simultaneous associations with multiple source devices for file download using its multiple radio interfaces and the multi-homing technique. In such a networking setting, we propose a network-controlled algorithm for optimal selection of source devices and their respective radio interfaces to support green Ds2D communications. Simulation results demonstrate that the proposed Ds2D communication paradigm under optimal selection of source devices and radio interfaces presents an improved energy efficiency performance compared with the conventional D2D communications, and leads to a lower energy consumption per source device.

In this paper, we propose a privacy-preserving power charging coordination scheme. Each energy storage unit (ESU) should send a charging request to an aggregator. The request does not reveal any private information to the aggregator. The aggregator forwards the requests to a charging controller that can know enough data to run a charging coordination scheme, but it cannot link the data to particular ESUs. Temporal charging coordination scheme is then proposed based on a modified knapsack problem formulation. The goal is to maximize the amount of power delivered to the ESUs before the charging requests expire without exceeding the available maximum charging capacity. Our simulation results demonstrate that both the optimal charging coordination and the privacy-aware charging coordination exhibit an improved performance compared with a first-come-first-serve charging coordination. More importantly, the privacy-aware scheme offers an attractive trade-off between the charging coordination performance and privacy preservation.

In this paper, we propose a trust-based and privacy-preserving fine-grained data retrieval scheme for mobile social networks (MSNs). The scheme enables users to create a log of trusted users who store (or are interested in) some topics related to a subject of interest. A subject is a broad term that can cover many fine-grained topics. In creating logs, we leverage friends-of-friends relationships and transferrable trust concept. Each user trusts its friends and the friends of friends. If a friend is not interested in a subject, he can help his friend in creating the log by linking the friend to his friends without knowing the subject to preserve privacy. In order to reduce the storage and computation overhead, we use Bloom filters to store the topics. A distinctive feature in our scheme is that it can query users who possess a fine-grained topic, rather than querying users who are interested in the broad subject but they may not have the specific topic of interest. We analyze the security and privacy of our scheme and evaluate the communication and computation overhead.

Developing energy efficient wireless communication networks has become crucial due to the associated environmental and financial benefits. Visible light communication (VLC) has emerged as a promising candidate for achieving energy efficient wireless communications. Integrating VLC with radio frequency (RF)-based wireless networks has improved the achievable data rates of mobile users. In this paper, we investigate the energy efficiency benefits of integrating VLC with RF-based networks in a heterogeneous wireless environment. We formulate and solve the problem of power and bandwidth allocation for energy efficiency maximization of a heterogeneous network composed of a VLC system and an RF communication system. Then, we investigate the impact of the system parameters on the energy efficiency of the mixed RF/VLC heterogeneous network. Numerical results are conducted to corroborate the superiority in performance of the proposed hybrid system. The impact of hybrid system parameters on the overall energy efficiency is also quantified.

Dynamic planning has been used by network operators for energy saving by switching ON-OFF base stations (BSs) according to the traffic load condition while providing quality-of-service (QoS) guarantee for mobile users. In the literature, research efforts focus mainly on satisfying the QoS of downlink mobile users. However, the impact of dynamic planning on the QoS of uplink mobile users is overlooked. A dynamic planning approach that relies only on the downlink performance of mobile users to determine the BS switching decision can result in deactivating nearby small cells and associating uplink mobile users to a faraway macro BS. Consequently, uplink mobile users may suffer from QoS degradation due to the longer transmission distance. Hence, while saving energy for the network operators and satisfying the downlink QoS, the uplink QoS could be violated. In this paper, we propose a balanced dynamic planning approach that accounts for QoS requirements both in the uplink and downlink to specify the BS switching decision. The switching decision is formulated as a two-timescale average reward Markov decision process with finite horizon. Due to computational complexity, sub-optimal algorithms are proposed. Simulation results demonstrate the trade-off between energy saving and QoS guarantee.

Several corrections are necessary for the above-named work are presented.

In vivo wireless medical devices have a critical role in healthcare technologies due to their continuous health monitoring and noninvasive surgery capabilities. In order to fully exploit the potential of such devices, it is necessary to characterize the in vivo wireless communication channel which will help to build reliable and high-performance communication systems. This paper presents preliminary results of experimental characterization for this fascinating communications medium on a human cadaver and compares the results with numerical studies.

This paper investigates energy efficient in vivo communication with multi-source nodes. In such a network, the in vivo sensor nodes transmit their sensing information to an on-body destination node. Due to the associated high path loss with implant devices, on-body relay nodes can be used to convey the information bits from the in vivo source nodes to the on-body destination node in an energy efficient manner. In this context, the paper objective is to select the optimal on-body relay locations that result in the minimum per bit average energy consumption for the in vivo networks using the minimum number of on-body relay nodes. The problem is formulated as an integer program that can be efficiently solved using commercial optimization solvers. Numerical results demonstrate the significant improvement in energy consumption and quality-of-service (QoS) support, when on-body relays are optimally located.

In this paper, energy efficient uplink communications are investigated for battery-constrained mobile terminals (MTs) with service quality requirements and multi-homing capabilities. A heterogeneous wireless medium is considered, where MTs communicate with base stations (BSs) and access points (APs) of different networks with overlapped coverage. Different from the existing works, we develop a quality of service (QoS)-based optimization framework for joint uplink bandwidth and power allocation to maximize energy efficiency for a set of MTs with multi-homing capabilities. The proposed framework is implemented in a decentralized architecture, through coordination among BSs/APs of different networks and MTs, which is a desirable feature when different networks are operated by different service providers. A suboptimal framework is presented with a reduced computational complexity as compared with the optimal framework. Simulation results demonstrate the improved performance of both the optimal and suboptimal frameworks over a state-of-the-art benchmark.

Abstract This paper focuses on the development of multiuser access schemes for spectrum sharing systems whereby secondary users are allowed to share the spectrum with primary users under the condition that the interference observed at the primary receiver is below a predetermined threshold. In particular, two scheduling schemes are proposed for selecting a user among those that satisfy the interference constraint and achieve an acceptable signal‐to‐noise ratio level. The first scheme focuses on optimizing the average spectral efficiency by selecting the user that reports the best channel quality. In order to alleviate the relatively high feedback required by the first scheme, a second scheme based on the concept of switched diversity is proposed, where the base station (BS) scans the secondary users in a sequential manner until a user whose channel quality is above an acceptable predetermined threshold is found. We develop expressions for the statistics of the signal‐to‐interference and noise ratio as well as the average spectral efficiency, average feedback load, and the delay at the secondary BS. We then present numerical results for the effect of the number of users and the interference constraint on the optimal switching threshold and the system performance and show that our analysis results are in perfect agreement with the numerical results. Copyright © 2014 John Wiley & Sons, Ltd.

A vehicle-to-vehicle (V2V) (dis)charging strategy can provide charging plans for gridable electric vehicles (GEVs), aiming to offload the heavy power loads from the electric power system. However, designing an efficient online V2V (dis)charging strategy to achieve optimal energy utilization is still an open issue. In this paper, we propose a semi-distributed online V2V (dis)charging strategy at a swapping station based on price control. Specifically, based on the electricity price control strategy, GEVs are motivated to contribute to a V2V energy transaction due to expected high revenue for discharging GEVs and low cost for charging GEVs. The Oligopoly game and Lagrange duality optimization techniques are exploited to address the associated optimal V2V (dis)charging strategies. Simulation results are presented to demonstrate the performance of the proposed V2V (dis)charging strategy.

The paper presents the improvement offered by spatial diversity to reduce fading in ultra wideband (UWB) body-centric wireless communication channel in context of second order statistics for five different measured links, i.e., waist-to-chest, waist-to-head, waist-to-wrist, waist-to-ankle and waist-to-back. Average fade duration (AFD) and level crossing rate (LCR) values are presented and compared for diversity combined signal with respect to branch signals. In addition AFD and LCR of diversity combined signals are also compared for five different on-body channels and for three different locations in an indoor environment. Result and analysis of second order channel parameters shows the importance of considering these parameters when designing an enhanced body-area network system.

Heterogeneous networks (HetNets) represent a promising solution for the next generation wireless networks (NGWNs), where many low power, low cost small-cells (e.g., fem-tocells) are planned to support the existing macrocell networks to reduce the over the air signaling and uplink power consumption, and thereby enhance the spectral efficiency compared to the macro-only network. In this context, the massive deployment of many lightly loaded small-cells is anticipated to increase the downlink power consumption of the HetNets. This paper investigates the end-to-end downlink power consumption of the HetNets, which consists of the power consumed by the macrocell and small-cell base stations and the backhaul to carry the traffic from the access to the core network. The downlink power consumption depends probabilistically on the population of the active mobile users in both the macrocell and small-cell networks such that the regulating factor is referred to as active user population factor (AUPF). A mathematical framework is presented to derive AUPF by assuming that the total population of active mobile users is a random variable and has a Binomial probability distribution. The number of active users and small-cells are calculated by the proposed probabilistic traffic model which assures the reduction in downlink power consumption since it now consists of the power consumption due to the base stations and backhaul for only the active population of mobile users. This model helps to evaluate the power consumption of HetNets. The simulations results indicate that AUPF and traffic load have significant impact on the downlink power consumption of HetNets.

With the cell coverage area of current and future mobile networks becoming smaller, heterogeneous small-cell networks (HetSNets), where multiple low-power, low-cost base stations (BSs) complement the existing macrocell infrastructure, are considered constitutive elements of future mobile networks. In this paper, we propose a K-tier HetSNet, where multiple tiers of small-cells are padded between macrocells which in turn expand the network coverage and significantly increase in capacity without compromising the frequency reuse factor. In this context, we derive analytical capacity bounds of the K-tier HetSNets based on the distance of the desired user from its serving BS and all other interfering BSs. It was observed that the upper bound of the capacity becomes tighter as the number of small-cell tiers increases due to the increase in the number of small-cells. Simulation results show the performance of the proposed K-tier HetSNets against the macro-only network in terms of frequency reuse factor, capacity and area spectral efficiency.

The problems of node localization and clock synchronization in wireless sensor networks are naturally tied from a statistical signal processing perspective. In this work, we consider the joint estimation of an unknown node's location and clock parameters by incorporating the effect of imperfections in node oscillators, which render a time varying nature to the clock parameters. In order to alleviate the computational complexity associated with the optimal maximum a-posteriori estimator, a simpler approach based on the Expectation-Maximization (EM) algorithm is proposed which iteratively estimates the clock parameters using a Kalman smoother in the E-step, and the location of the unknown node in the M-step. The convergence and the mean square error (MSE) performance of the proposed algorithm are evaluated using simulation studies which demonstrate the high fidelity of the proposed joint estimation approach.

This paper proposes a novel variational approach for proving the extremal entropy inequality (EEI) [1]. Unlike previous proofs [1], [2], the proposed variational approach is simpler and it does not require neither the classical entropy power inequality (EPI) [1], [2] nor the channel enhancement technique [1]. The proposed approach is versatile and can be easily adapted to numerous other applications such as proving or extending other fundamental information theoretic inequalities such as the EPI, worst additive noise lemma, and Cramér-Rao inequality.

Heterogeneous networks (HetNets) are considered as a standard part of the future generation of wireless networks where masses of low power, low cost smallcells (e.g., femtocells) are anticipated to support the existing macrocell networks. While HetNets are increasing the spectral efficiency and decreasing the over-the-air signaling and uplink power consumption compared to macro networks, the large scale deployment of many lightly loaded smallcells is expected to increase the downlink power consumption of the HetNets. This paper studies the impact of smallcell population on the downlink power consumption of the HetNets. In this context, we propose that the population of smallcells is strictly depending on the traffic load due to active mobile users, which is a random variable and time-varying. We derive the mathematical framework to calculate the required population of smallcells based on the probabilistic traffic models where the number of total mobile users and number of active mobile users have different probabilistic distributions such as different combinations of Binomial and Poisson distributions. The proposed method guarantees the reduction in downlink power consumption of HetNets by forcing the smallcells to turn on the sleeping mode under low and medium traffic load conditions. Several simulation results are included to illustrate the impact of traffic load dependent population of smallcells on the downlink power consumption of HetNets. Moreover, it is shown that the mathematical and simulation results are in perfect agreement.

In this paper, we consider the performance of a two-way amplify-and-forward relaying network (AF TWRN) in the presence of unequal power co-channel interferers (CCI). Specifically, we consider AF TWRN with an interference-limited relay and two noisy-nodes with channel estimation error and CCI. We derive the approximate signal-to-interference plus noise ratio expressions and then use these expressions to evaluate the outage probability and error probability. Numerical results show that the approximate closed-form expressions are very close to the exact ones.

MOTIVATION: Network component analysis (NCA) is an efficient method of reconstructing the transcription factor activity (TFA), which makes use of the gene expression data and prior information available about transcription factor (TF)-gene regulations. Most of the contemporary algorithms either exhibit the drawback of inconsistency and poor reliability, or suffer from prohibitive computational complexity. In addition, the existing algorithms do not possess the ability to counteract the presence of outliers in the microarray data. Hence, robust and computationally efficient algorithms are needed to enable practical applications. RESULTS: We propose ROBust Network Component Analysis (ROBNCA), a novel iterative algorithm that explicitly models the possible outliers in the microarray data. An attractive feature of the ROBNCA algorithm is the derivation of a closed form solution for estimating the connectivity matrix, which was not available in prior contributions. The ROBNCA algorithm is compared with FastNCA and the non-iterative NCA (NI-NCA). ROBNCA estimates the TF activity profiles as well as the TF-gene control strength matrix with a much higher degree of accuracy than FastNCA and NI-NCA, irrespective of varying noise, correlation and/or amount of outliers in case of synthetic data. The ROBNCA algorithm is also tested on Saccharomyces cerevisiae data and Escherichia coli data, and it is observed to outperform the existing algorithms. The run time of the ROBNCA algorithm is comparable with that of FastNCA, and is hundreds of times faster than NI-NCA. AVAILABILITY: The ROBNCA software is available at http://people.tamu.edu/∼amina/ROBNCA

In cognitive radio (CR) systems, one of the main implementation issues is spectrum sensing because of the uncertainties in propagation channel, hidden primary user (PU) problem, sensing duration and security issues. This paper considers an orthogonal frequency-division multiplexing (OFDM)-based CR spectrum sharing system that assumes random access of primary network subcarriers by secondary users (SUs) and absence of the PU's spectrum utilization information, i.e., no spectrum sensing is employed to acquire information about the PU's activity or availability of free subcarriers. In the absence of information about the PU's activity, the SUs randomly access (utilize) the subcarriers of the primary network and collide with the PU's subcarriers with a certain probability. In addition, inter-cell collisions among the subcarriers of SUs (belonging to different cells) can occur due to the inherent nature of random access scheme. This paper conducts a stochastic analysis of the number of subcarrier collisions between the SUs' and PU's subcarriers assuming fixed and random number of subcarriers requirements for each user. The performance of the random scheme in terms of capacity and capacity (rate) loss caused by the subcarrier collisions is investigated by assuming an interference power constraint at PUs to protect their operation.

In this paper, we consider the performance of a two-way amplify-and-forward relaying network (AF TWRN) in the presence of unequal power co-channel interferers (CCI). Specifically, we first consider AF TWRN with an interference-limited relay and two noisy-nodes with channel estimation errors and CCI. We derive the approximate signal-to-interference plus noise ratio expressions and then use them to evaluate the outage probability, error probability, and achievable rate. Subsequently, to investigate the joint effects of the channel estimation error and CCI on the system performance, we extend our analysis to a multiple-relay network and derive several asymptotic performance expressions. For comparison purposes, we also provide the analysis for the relay selection scheme under the total power constraint at the relays. For AF TWRN with channel estimation error and CCI, numerical results show that the performance of the relay selection scheme is not always better than that of the all-relay participating case. In particular, the relay selection scheme can improve the system performance in the case of high power levels at the sources and small powers at the relays.

Clock synchronization has become an indispensable requirement in wireless sensor networks due to its central importance in vital network operations such as data fusion and duty cycling, and has attracted considerable research interest recently. Assuming exponentially distributed random delays in a two-way message exchange mechanism, this work proposes a network-wide clock synchronization algorithm using a factor graph representation of the network. Message passing using the max-product algorithm is adopted to derive the update rules for the proposed iterative procedure. A closed form solution is obtained for each node's belief about its clock offset at each iteration. Simulation results show that the application of the proposed message passing-based network-wide clock synchronization algorithm provides convergent estimates for both regular cycle-free and random topologies. Moreover, the mean square error (MSE) performance of the proposed algorithm is also compared with the Cramer-Rao bound (CRB) for small example networks, which further highlights the effectiveness of the proposed algorithm.

This paper proposes a unifying variational approach for proving and extending some fundamental information theoretic inequalities. Fundamental information theory results such as maximization of differential entropy, minimization of Fisher information (Cramér-Rao inequality), worst additive noise lemma, entropy power inequality, and extremal entropy inequality are interpreted as functional problems and proved within the framework of calculus of variations. Several applications and possible extensions of the proposed results are briefly mentioned.

The problems of node localization and clock synchronization in wireless sensor networks are naturally tied from a statistical signal processing perspective. In this work, we consider the joint estimation of an unknown node's location and clock parameters by incorporating the effect of imperfections in node oscillators, which render a time varying nature to the clock parameters. The data exchange mechanism is based on a two-way message exchange with anchor nodes. In order to alleviate the computational complexity associated with the optimal maximum a-posteriori estimator, two iterative approaches are proposed as simpler alternatives. The first approach utilizes an Expectation-Maximization (EM) based algorithm which iteratively estimates the clock parameters and the location of the unknown node. The EM algorithm is further simplified by a non-linear processing of the data to obtain a closed form solution of the location estimation problem using least squares (LS). The performance of the estimation algorithms is benchmarked by deriving the Hybrid Cramer-Rao lower bound (HCRB) on the mean square error (MSE) of the estimators. The theoretical findings are corroborated by simulation studies which reveal that the LS estimator closely matches the performance of the EM algorithm for small time of arrival measurement noise, and is well suited for implementation in low cost sensor networks.

This paper reviews the information theoretic methods used for inferring gene regulatory networks. Mutual information has been widely used as a dependency measure to estimate the undirected interactions between genes using steady state data. However, employing time-series data results in a directed graph. Since two genes may be interacting with each other via an intermediate gene, their mutual information may show a direct dependency. To resolve this issue, data processing inequality and conditional mutual information have been employed. Mutual information, being a symmetric measure, is unable to predict directed edges using the steady-state data alone, while algorithms using time-series data can be computationally complex as more data is involved. Therefore, non-symmetric measures such as φ mixing coefficients have recently been proposed in the literature. The algorithms using these techniques are also discussed in this article. Estimation of information-theoretic metrics is explained which is a core component of all the methods. Performance metrics that are frequently used to test the robustness and accuracy of the algorithms are also described and some avenues of future research are proposed.

In this paper, we develop multiuser access schemes for spectrum sharing systems whereby secondary users are allowed to share the spectrum with primary users under the condition that the interference observed at the primary receiver is below a predetermined threshold. In particular, we devise two schemes for selecting a user among those that satisfy the interference constraint and achieve an acceptable signal-to-noise ratio level. The first scheme selects the user that reports the best channel quality. In order to alleviate the high feedback load associated with the first scheme, we develop a second scheme based on the concept of switched diversity where the base station scans the users in a sequential manner until an acceptable user is found. In addition to these two selection schemes, we consider two power adaptive settings at the secondary users based on the amount of interference available at the secondary transmitter. In the On/Off power setting, users are allowed to transmit based on whether the interference constraint is met or not, while in the full power adaptive setting, the users are allowed to vary their transmission power to satisfy the interference constraint. Finally, we present numerical results for our proposed algorithms where we show the trade-off between the average spectral efficiency and average feedback load for both schemes.

The problem of clock offset estimation in a two-way timing exchange regime is considered when the likelihood function of the observation time stamps is exponentially distributed. In order to capture the imperfections in node oscillators, which render a time-varying nature to the clock offset, a novel Bayesian approach to the clock offset estimation is proposed using a factor graph representation of the posterior density. Message passing using the max-product algorithm yields a closed form expression for the Bayesian inference problem.

This paper considers the problem of inferring gene regulatory networks using time series data. A nonlinear model is assumed for the gene expression profiles, whereas the microarray data follows a linear Gaussian model. A particle filter based approach is proposed to estimate the gene expression profiles and the parameters are estimated online using Kalman filter. In order to capture the inherent sparsity of the gene networks, LASSO based least square optimization is performed. The performance of the proposed algorithm is compared with the extended Kalman filter (EKF) algorithm using Mean Square Error (MSE) as the fidelity criterion. The simulations are performed using the synthetic as well as real data and the proposed algorithm is observed to outperform the EKF in the scenarios considered.

This paper illustrates the equivalence between two fundamental results: Stein identity, originally proposed in the statistical estimation realm, and de Bruijn identity, considered for the first time in the information theory field. Two distinctive extensions of de Bruijn identity are presented as well. For arbitrary but fixed input and noise distributions, the first-order derivative of differential entropy is expressed by means of a function of the posterior mean, while the second-order derivative of differential entropy is manifested in terms of a function of Fisher information. Several applications exemplify the utility of the proposed results.

This paper focuses on developing an alternative proof for an extremal entropy inequality, originally presented in [1]. The proposed alternative proof is simply based on the classical entropy power inequality and the data processing inequality. Compared with the proofs in [1], the proposed alternative proof is simpler, more direct, and information theoretic, and presents the advantage of providing the structure of the optimal solution covariance matrix. Also, the proposed proof might also be used as a novel method to address applications such as calculation of the vector Gaussian broadcast channel capacity, establishing a lower bound for the achievable rate of distributed source coding with a single quadratic distortion constraint, and the secrecy capacity of the Gaussian wire-tap channel.

Multi-user downlink MIMO communication has the potential to increase throughput using efficient beamforming. However, the precoding criteria assume perfect knowledge of the beamforming matrices at the base station (BS). This requires a user to transmit its complete channel information to the BS, a task that entails significant overhead. Limited feedback precoding, a part of LTE standardization now, needs only limited information be fed back to the BS. This paper studies the capacity loss incurred by using LTE codebooks, under different power allocation schemes with varying degrees of optimality. The sub-optimal power allocation schemes also ensure a better throughput-fairness trade-off. It is observed that using LTE codebooks results in a significant loss and that there is a need to develop codebooks which offer better adaptation to channel variations.

This paper considers the problem of learning the structure of gene regulatory networks from gene expression time series data. A more realistic scenario when the state space model representing a gene network evolves nonlinearly is considered while a linear model is assumed for the microarray data. To capture the nonlinearity, a particle filter-based state estimation algorithm is considered instead of the contemporary linear approximation-based approaches. The parameters characterizing the regulatory relations among various genes are estimated online using a Kalman filter. Since a particular gene interacts with a few other genes only, the parameter vector is expected to be sparse. The state estimates delivered by the particle filter and the observed microarray data are then subjected to a LASSO-based least squares regression operation which yields a parsimonious and efficient description of the regulatory network by setting the irrelevant coefficients to zero. The performance of the aforementioned algorithm is compared with the extended Kalman filter (EKF) and Unscented Kalman Filter (UKF) employing the Mean Square Error (MSE) as the fidelity criterion in recovering the parameters of gene regulatory networks from synthetic data and real biological data. Extensive computer simulations illustrate that the proposed particle filter-based network inference algorithm outperforms EKF and UKF, and therefore, it can serve as a natural framework for modeling gene regulatory networks with nonlinear and sparse structure.

The problem of clock offset estimation in a two-way timing message exchange regime is considered when the likelihood function of the observation time stamps is Gaussian, exponential, or log-normally distributed. A parameterized solution to the maximum likelihood (ML) estimation of clock offset is analytically obtained, which differs from the earlier approaches where the likelihood function is maximized graphically. In order to capture the imperfections in node oscillators, which may render a time-varying nature to the clock offset, a novel Bayesian approach to the clock offset estimation is proposed by using a factor graph representation of the posterior density. Message passing using the max-product algorithm yields an exact expression for the Bayesian inference problem. Several lower bounds on the variance of an estimator are derived for arbitrary exponential family distributed likelihood functions which, while serving as stepping stones to benchmark the performance of the proposed clock offset estimators, can be useful in their own right in classical as well Bayesian parameter estimation theory. To corroborate the theoretical findings, extensive simulation results are discussed for classical as well as Bayesian estimators in various scenarios. It is observed that the performance of the proposed estimators is fairly close to the fundamental limits established by the lower bounds.

This paper focuses on illustrating 1) the equivalence between Stein's identity and De Bruijn's identity, and 2) two extensions of De Bruijn's identity. First, it is shown that Stein's identity is equivalent to De Bruijn's identity under additive noise channels with specific conditions. Second, for arbitrary but fixed input and noise distributions under additive noise channels, the first derivative of the differential entropy is expressed by a function of the posterior mean, and the second derivative of the differential entropy is expressed in terms of a function of Fisher information. Several applications over a number of fields, such as signal processing and information theory, are presented to support the usefulness of the developed results in this paper.

ABSTRACT Cognitive radio (CR) with spectrum‐sharing feature is a promising technique to address the spectrum under‐utilization problem in dynamically changing environments. In this paper, the achievable capacity gain of spectrum‐sharing systems over dynamic fading environments is studied. To perform a general analysis, a theoretical fading model called hyper‐fading model that is suitable to the dynamic nature of CR channel is proposed. Closed‐form expressions of probability density function (PDF) and cumulative density function (CDF) of the signal‐to‐noise ratio (SNR) for secondary users (SUs) in spectrum‐sharing systems are derived. In addition, the capacity gains achievable with spectrum‐sharing systems in high and low power regions are obtained. The effects of different fading figures, average fading powers, interference temperatures, peak powers of secondary transmitters, and numbers of SUs on the achievable capacity are investigated. The analytical and simulation results show that the fading figure of the channel between SUs and primary base‐station (PBS), which describes the diversity of the channel, does not contribute significantly to the system performance gain. Copyright © 2011 John Wiley & Sons, Ltd.

This paper proposes an efficient blind deterministic carrier frequency offset (CFO) estimation method for orthogonal frequency division multiplexing (OFDM) systems. In the proposed method, two OFDM symbols with time difference are generated by exploiting both the oversampled OFDM signal and the cyclic prefix, and a cost function is introduced for CFO estimation. It is shown that the cost function can be expressed as a cosine function. Using a property of the cosine function, a formula for deterministically estimating the CFO is derived. The proposed method is very suitable for real wireless environments since it requires only one OFDM symbol for blind reliable estimation of CFO. The proposed method is very efficient in computational complexity since no searching operation for the minimum cost value is required. The computer simulations show that the performance of the proposed method is superior to those of the MUSIC method and the ML method.

Recently, the maximum likelihood estimator (MLE) and Cramer-Rao Lower Bound (CRLB) were proposed with the goal of maximizing and assessing the synchronization accuracy in wireless sensor networks (WSNs). Because the network delays may assume any distribution and the performance of MLE is quite sensitive to the distribution of network delays, designing clock synchronization algorithms that are robust to unknown network delay distributions appears as an important problem. By adopting a Bayesian framework, this paper proposes a novel clock synchronization algorithm, called Iterative Gaussian Mixture Kalman Particle Filter (IGMKPF), which is shown to achieve good and robust performance in the presence of unknown net work delay distributions. The Posterior Cramer-Rao Bound (PCRB) and the Mean-Square Error (MSE) of IGMKPF are evaluated and shown to exhibit improved performance and robustness relative to MLE.

In this study, the behavior of interference is investigated for cellular mobile radio networks. More explicitly, this work focuses on examining how interference evolves with respect to time under long and short term fading together for the uplink of an FDD system. The results show that decorrelation distance of shadowing plays a crucial role in predicting future interference conditions. Given the initial interference measurement, it is shown that the future interference levels are highly dependent on maintaining the decorrelation distance through the observation interval. On the one hand, if the observation interval is kept too short in a mobile environment, then a drastic deviation in the new interference level from the previous one is not expected. On the other hand, if the observation interval is kept too long, then spatial correlation of shadowing loses its significance and drastic deviations are more likely in the new interference levels; furthermore, path loss might dominate the interference status due to longer displacements.

This paper proposes an efficient blind deterministic carrier frequency offset (CFO) estimation method for orthogonal frequency division multiplexing (OFDM) systems. In the proposed method, two OFDM symbols with time difference are generated by exploiting both the oversampled OFDM signal and the cyclic prefix, and a cost function is introduced for CFO estimation. It is shown that the cost function can be expressed as a cosine function. Using a property of the cosine function, a formula for estimating the CFO is derived. The estimator of the CFO requires three independent cost function values calculated at three different frequency offsets. Using the formula, the CFO can be estimated without searching all the frequency offset range. The proposed method is very suitable for real wireless environments since it requires only one OFDM symbol for blind reliable estimation of CFO. The computer simulations show that the performance of the proposed method is superior to those of the MUSIC method and the oversampling method . Unlike the conventional methods such as MUSIC method and the oversampling method, the accuracy of the proposed method is independent of the searching step.

Cognitive radio is regarded as a novel approach for improving the utilization of precious radio spectrum resource. The detection of very low signal-to-noise ratio (SNR) signals with relaxed a priori information on the signal parameters is of high importance to such radios. This paper proposes a detection algorithm based on the first-order cyclostationarity for amplitude modulated (AM) signals that only requires rough information on the signal bandwidth and carrier frequency. A theoretical asymptotic analysis is performed. Simulation results show that the proposed algorithm performs well at low SNRs.

Cognitive radio (CR) represents a possible solution to the paradoxical problem of simultaneous scarcity and underutilization of the electromagnetic spectrum. Spectrum awareness, a key task of such a radio, encompasses the recognition of the received signal type and parameters. This paper investigates the cyclostationarity approach for the recognition of cyclically prefixed single carrier linearly digitally modulated (CP-SCLD) signals versus SCLD and orthogonal frequency division multiplexing (OFDM) signals under practical conditions, including time-dispersive channels, additive Gaussian noise, and phase, frequency and timing offsets. Analytical closed-form expressions are derived for the cyclic autocorrelation function (CAF) and the set of cycle frequencies (CFs) of CP-SCLD signals. These results are the basis of the proposed signal recognition algorithm. This algorithm has the advantage of avoiding requirements for the recovery of carrier, waveform, and symbol timing information, and the estimation of signal and noise powers.

We consider the problem of Maximum Likelihood (ML) estimation of clock parameters in a two-way timing exchange scenario where the random delays assume a Weibull distribution, which represents a more generalized model. The ML estimate of the clock offset for the case of exponential distribution was obtained earlier. Moreover, it was reported that when the fixed delay is known, MLE is not unique. We determine the uniformly minimum variance unbiased (UMVU) estimators for exponential distribution under such a scenario and produce biased estimators having lower MSE than UMVU for all values of clock offset. We then consider the case when shape parameter is greater than one and reduce the corresponding optimization problems to their equivalent convex forms, thus guaranteeing convergence to a global minimum.

A sender-receiver paradigm, in which a master and slave node exchange timing packets to estimate the clock offsets of the slave node and other nodes located in the common broadcast region of master and slave nodes, is adopted herein for synchronizing the clocks of individual nodes in a wireless sensor network (WSN). The maximum likelihood estimate of the clock offset of the listening node hearing the broadcasts from both the master and slave nodes was derived, assuming symmetric exponential link delays. This paper advances those results in two directions. First, some improved estimators, each being optimal in its own class, are derived for the clock offset of the listening node and mean link delays. Second, the results are generalized by addressing the more realistic problem of clock offset estimation under asymmetric exponential delays. The results presented in this paper are important for time synchronization of WSNs, where these techniques can be utilized to achieve accurate clock estimates with reduced power consumption.

For a meaningful processing of the information sensed by a wireless sensor network (WSN), the clocks of the individual nodes need to be matched through some well-defined procedures. Extending the idea of having silent nodes in a WSN overhear the two-way timing message communication between two active (master and slave) nodes, this paper derives the maximum-likelihood estimator (MLE) for the clock offsets of the listening nodes located within the communication range of the active nodes by assuming an exponential link delay modeling, hence synchronizing with the reference node at a very low cost. A vital advantage for adopting such an approach is that the performance of sender-receiver protocols can be compared with receiver-receiver protocols on equal footings, because their main critical aspect was associated with the high-communication overhead induced by the point-to-point nature of communication links relative to broadcast communications. The MLE is also shown to be the minimum variance unbiased estimator (MVUE) of the clock offset when the mean of exponential link delays is known. Since it is attractive to know in advance the extent to which an estimator can perform through its lower bound, the Chapman-Robbins bound and the Barankin bound for the clock offset estimator are also derived. It is shown that for an exponential link delay model, the mean square error of the clock offset estimator is inversely proportional to the square of the number of observations, and hence its performance is on a similar scale, albeit slightly lesser, as compared to the usual sender-receiver clock offset estimator. In addition, a novel method referred to as the Gaussian mixture Kalman particle filter (GMKPF) is proposed herein to estimate the clock offsets of the listening nodes in a WSN. GMKPF represents a better and flexible alternative to the MLE for the clock offset estimation problem due to its improved performance and applicability in arbitrary and generalized non-Gaussian random delay models.

For many applications, distributed networks require the local clocks of the constituent nodes to run close to an agreed upon notion of time. Most of the widely used clock synchronization algorithms in such systems employ the sender-receiver protocol based on a two-way timing message exchange paradigm. Maximum likelihood estimator (MLE) of the clock offset based on the timing message exchanges between two clocks was derived in D. R. Jeske, On maximum likelihood estimation of clock offset[IEEE Trans. Commun., vol. 53, pp. 53-54, Jan. 2005], when the fixed delays are symmetric and the variable delays in each direction assume an exponential distribution with an unknown mean. Herein, the best linear unbiased estimate using order statistics (BLUE-OS) of the clock offset between two nodes is derived assuming both symmetric and asymmetric exponential network delays, respectively. The Rao-Blackwell-Lehmann-Scheffe¿ theorem is then exploited to obtain the minimum variance unbiased estimate (MVUE) for the clock offset which it is shown to coincide with the BLUE-OS. In addition, it is found that the MVUE of the clock offset in the presence of symmetric network delays also coincides with the MLE. Finally, in the presence of asymmetric network delays, although the MLE is biased, it is shown to achieve lesser mean-square error (MSE) than the MVUE in the region around the point where the bidirectional network link delays are symmetric and hence its merit as the most versatile estimator is fairly justified.

Cognitive radio with spectrum sharing feature is a promising technique to address the spectrum under-utilization problem in dynamically changing environments. In this paper, achievable capacity gain of spectrum sharing systems over dynamic fading environments is studied. For the analysis, a theoretical fading model called hyper fading model that is suitable to the dynamic nature of cognitive radio channel is proposed. Closed-form expression of probability density function (PDF) and cumulative density function (CDF) of the signal-to-noise ratio (SNR) for secondary users in spectrum sharing systems are derived. In addition, the capacity gains achievable with spectrum sharing systems in high and low power regions are obtained. Numerical simulations are performed to study the effects of different fading figures, average powers, interference temperature, and number of secondary users on the achievable capacity.

This paper targets the problem of clock synchronization for a set of receivers lying within the broadcast range of two nodes implementing a general sender-receiver protocol using a wireless channel. The maximum likelihood estimate of the clock offset of the inactive node (and mean link delay) hearing the broadcasts from both the master and slave nodes was derived in assuming symmetric exponential link delays. In this paper, the minimum variance unbiased estimate for the clock offset of such nodes is derived by applying the Rao-Blackwell-Lehmann-Scheffe theorem. The result is important in the realm of wireless sensor networks, where a tight network synchronization along with a conservative energy utilization plays a major role in network performance.

Clock synchronization is an important issue for the design of a network composed of small sensor nodes. Based on the two-way timing message exchange mechanism and assuming an exponential network delay distribution, many analytical results have been presented in the literature by applying the techniques from statistical signal processing. This paper derives the minimum variance unbiased estimator for the clock offset for both symmetric and asymmetric exponential delay cases. For the asymmetric delays, it is shown to be a function of both the minimum and the mean link delays. This result is a very significant contribution since only the minimum link delay observations have been used to estimate the clock offset in the past. For the symmetric case, it is shown to coincide with the maximum likelihood estimator. In addition, the result is also applicable to clock synchronization problem in general computer networks.

In timing synchronization, the Cramer Rao Bound has been used as performance bounds for timing estimation in AWGN channel. However, the instantaneous CRB for timing estimation in fading channels depends on the channel realizations and may fail to bound the mean square error (MSE) of the estimator because the equivalent signal-to-noise ratio (SNR) is too low. In this paper, we demonstrate that the conventional CRB for timing estimation is no longer valid in fading channels. Furthermore, a new performance bound called Weighted Bayesian CRB (WBCRB) is proposed for the estimation of both single and multiple timing offsets under fading channels. The relationship between the conventional CRB and WBCRB are discussed in details, where numerical results show that the WBCRB is a valid bound for all SNR even under fading channels.

Wireless Sensor Networks (WSN) are a key vehicle for driving many future applications, most of which require the logical clocks of the constituent nodes to by closely synchronized in time with each other. Applying the concepts from the field of statistical signal processing in this area can yield many fruitful results and advance the state of the art in this technology. This paper illustrates a summary of the main results achieved so far by working in this particular direction, which includes the derivation of Maximum Likelihood Estimators (MLE) of the clock parameters, namely the clock offset, skew and drift as well as the link delays, the lower bounds on their performance and two simplified schemes for power conservation in the context of WSNs.

We analyze the performance of the hard handoff algorithm used in the 1xEV-DO Rev. A systems. A theoretical approach is presented to calculate the slot error probability (SEP). The approach enables us to evaluate the effects of filtering, hysteresis as well as the system introduced delay of handoff execution. Unlike previous work the model used considers multiple base stations (BS) and accounts for correlation of shadow fading affecting different signal powers received from different BS's. The theoretical results are verified over ranges of parameters of practical interest using simulations which are also used to evaluate the packet error rate (PER) and the number of handoffs.

Clock synchronization plays a fundamental role in the operation of wireless sensor networks. This paper derives the Maximum Likelihood Estimator (MLE) of clock phase offset and skew between two nodes that assume a two-way timing message exchange mechanism to achieve synchronization. The MLE is derived under quite general conditions assuming an exponential distribution for the variable network delays and the presence of an unknown deterministic component due to the fixed network delays.

Due to experimental constraints, most microarray observations are obtained through irregular sampling. In this paper three popular spectral analyzing schemes, i.e., Lomb-Scargle, Capon and the missing data amplitude and phase estimation (MAPES), are compared in terms of their ability and efficiency to recover the periodically expressed genes. The in silico experiments based on microarray measurements of drosophila melanogaster not only verify half of the published cell-cycle genes, but also corroborate genes that behave periodically in human Hela time series experiments.

This letter proposes a new method to estimate the number of competing stations in IEEE 802.11 networks. Due to the nonlinear/non-Gaussian nature of measurement model, a nonlinear filtering algorithm, called the Gaussian mixture sigma point particle filter (GMSPPF), is proposed herein to estimate the number of competing stations. Since GMSPPF represents a better alternative to the conventional extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and unscented particle filter (UPF) for nonlinear/non-Gaussian (or Gaussian) tracking problems, we apply this filter for IEEE 802.11 WLANs. GMSPPF provides a more viable means for tracking in any conditions the number of competing stations in IEEE 802.11 WLANs relative to EKF, UKF, PF, and UPF. Further, GMSPPF presents both high accuracy as well as prompt reactivity to changes in the network occupancy status. For the more accurate method (GMSPPF), the combined access mode is shown to maximize the system throughput by switching between the basic access mode and the RTS/CTS access mode.

Recently, the concept of mutual information has been proposed for inferring the structure of genetic regulatory networks from gene expression profiling. After analyzing the limitations of mutual information in inferring the gene-to-gene interactions, this paper introduces the concept of conditional mutual information and based on it proposes two novel algorithms to infer the connectivity structure of genetic regulatory networks. One of the proposed algorithms exhibits a better accuracy while the other algorithm excels in simplicity and flexibility. By exploiting the mutual information and conditional mutual information, a practical metric is also proposed to assess the likeliness of direct connectivity between genes. This novel metric resolves a common limitation associated with the current inference algorithms, namely the situations where the gene connectivity is established in terms of the dichotomy of being either connected or disconnected. Based on the data sets generated by synthetic networks, the performance of the proposed algorithms is compared favorably relative to existing state-of-the-art schemes. The proposed algorithms are also applied on realistic biological measurements, such as the cutaneous melanoma data set, and biological meaningful results are inferred.

This letter proposes a novel Walsh coded training signal design and decoding method to estimate the channel response in MIMO-OFDM systems. The Walsh coded training signals, designed to be orthogonal in the time domain, facilitate the separation of the desired training signal from the received mixed signal and the estimation of the channel response. The proposed channel estimation method is directly applicable to practical MIMO-OFDM systems with null subcarriers and exhibits nearly the same performance as Li's original channel estimator [5] at a much reduced computational complexity.

Motivated by the necessity of having a good clock synchronization amongst the nodes of wireless ad-hoc sensor networks, the joint maximum likelihood (JML) estimator for clock phase offset and skew under exponential noise model for reference broadcast synchronization (RBS) protocol is formulated and found via a direct algorithm. The Gibbs sampler is also proposed for joint clock phase offset and skew estimation and shown to provide superior performance relative to JML- estimator. Lower and upper bounds for the mean-square errors (MSE) of JML-estimator and Gibbs Sampler are introduced in terms of the MSE of the uniform minimum variance unbiased (UMVU) estimator and the conventional best linear unbiased estimator (BLUE), respectively.

This letter proposes an energy-efficient clock synchronization scheme for Wireless Sensor Networks (WSNs) based on a novel time synchronization approach. Within the proposed synchronization approach, a subset of sensor nodes are synchronized by overhearing the timing message exchanges of a pair of sensor nodes. Therefore, a group of sensor nodes can be synchronized without sending any extra messages. This paper brings two main contributions: 1. Development of a novel synchronization approach which can be partially or fully applied for implementation of new synchronization protocols and for improving the performance of existing time synchronization protocols. 2. Design of a time synchronization scheme which significantly reduces the overall network-wide energy consumption without incurring any loss of synchronization accuracy compared to other well-known schemes.

Recent advances in high throughput microarray data have enabled the learning of the structure and operation of gene regulatory networks. This paper proposes a novel approach for reconstruction of gene regulatory networks based on the posterior probabilities of gene regulations. Built within the framework of Bayesian statistics and exploiting efficient computational Monte Carlo techniques, the proposed approach prevents the dichotomy of classifying gene interactions as either being connected or disconnected, and thereby it reduces significantly the inference errors. Simulation results corroborate the superior performance of the proposed approach relative to the existing state-of-the-art algorithms.

Clock synchronization represents a crucial element in the operation of Wireless Sensor Networks (WSN). For any general time synchronization protocol involving a two way message exchange mechanism (e.g., Timing Synch Protocol for Sensor Networks (TPSN) [1]), the Maximum Likelihood Estimate (MLE) for clock offset under the exponential delay model was derived in [2] assuming no clock skew between the nodes. Since all practical clocks are running at different rates with respect to each other; the skew correction becomes important for achieving long term synchronization which in turn results in reduction of message exchanges and hence minimal power utilization. The MLE for clock offset as well as skew under the exponential delay model for TPSN were derived in [6]. In this paper; due to complexity reasons for finding the MLE, a simple and efficient algorithm is presented as an alternative to the ML estimation which particularly suits the low power demanding regime of wireless sensor networks.

This paper proposes an energy-efficient clock synchronization scheme for Wireless Sensor Networks (WSNs) based on a novel time synchronization approach. Within the proposed synchronization approach, a subset of sensor nodes are synchronized by over-hearing the timing message exchanges of a pair of sensor nodes. Therefore, a group of sensor nodes can be synchronized without sending any extra messages. This paper brings two main contributions: 1. Development of a novel synchronization approach which can be partially or fully applied for implementation of new synchronization protocols and for improving the performance ofexisting time synchronization protocols. 2. Design of a time synchronization scheme which significantly reduces the overall network-wide energy consumption without incurring any loss of synchronization accuracy compared to other well-known schemes.

Recently, a few efficient timing synchronization protocols for wireless sensor networks (WSNs) have been proposed with the goal of maximizing the accuracy and minimizing the power utilization. This paper proposes novel clock skew estimators assuming different delay environments to achieve energy-efficient network-wide synchronization for WSNs. The proposed clock skew correction mechanism significantly increases the re-synchronization period, which is a critical factor in reducing the overall power consumption. The proposed synchronization scheme can be applied to the conventional protocols without additional overheads. Moreover, this paper derives the Cramer-Rao lower bounds and the maximum likelihood estimators under different delay models and assumptions. These analytical metrics serves as good benchmarks for the thus far reported experimental results

This paper analyzes the effects of the level of cooperation (LOC) on the performance of a practical cooperative coding scheme for frequency division half-duplex relay links in general wireless ad-hoc networks. The end-to-end bit error probability (BEP) and the corresponding optimum LOG minimizing the end-to-end BEP are investigated for specific realizations using a family of rate compatible punctured convolutional (RCPC) codes. This paper shows that the BEP of the cooperative coding scheme depends on the LOC and there exists an optimum LOC for every specific realization. Further, it is shown that more than a certain level of cooperation is required to achieve possible cooperative gains in practical signal-to-noise ratio (SNR) regions, and increasing LOC does not effect much on the BEP performance for sufficiently large LOCs

Recently, a few protocols for synchronizing the nodes of wireless sensor networks (WSNs) to a common time frame have been proposed with the goal of maximizing the accuracy and minimizing the power utilization. Thus far, the performance of the existing protocols for time synchronization is assessed only through computer simulations and experiments without using any rigorous analytical metrics. The goal of this paper is to fill up this gap by deriving the Cramer-Rao lower bound (CRLB) for the clock offset in one of the most popular synchronization algorithms, namely timing-sync protocol for sensor networks (TPSN), assuming commonly used exponential and Gaussian delay models, respectively. Furthermore, this paper proposes novel and practical clock skew estimators requiring no prior information of the fixed portion of delays, which makes the TPSN algorithm very suitable to synchronization in light of its power efficiency constraint.

MOTIVATION: A central question in reverse engineering of genetic networks consists in determining the dependencies and regulating relationships among genes. This paper addresses the problem of inferring genetic regulatory networks from time-series gene-expression profiles. By adopting a probabilistic modeling framework compatible with the family of models represented by dynamic Bayesian networks and probabilistic Boolean networks, this paper proposes a network inference algorithm to recover not only the direct gene connectivity but also the regulating orientations. RESULTS: Based on the minimum description length principle, a novel network inference algorithm is proposed that greatly shrinks the search space for graphical solutions and achieves a good trade-off between modeling complexity and data fitting. Simulation results show that the algorithm achieves good performance in the case of synthetic networks. Compared with existing state-of-the-art results in the literature, the proposed algorithm exceptionally excels in efficiency, accuracy, robustness and scalability. Given a time-series dataset for Drosophila melanogaster, the paper proposes a genetic regulatory network involved in Drosophila's muscle development. AVAILABILITY: Available from the authors upon request.

In this letter, all the previously proposed digital blind feedforward symbol timing estimators employing second-order statistics are casted into a unified framework. The finite sample mean-square error (MSE) expression for this class of estimators is established. Simulation results are also presented to corroborate the analytical results. It is found that the feedforward conditional maximum likelihood (CML) estimator and the square law nonlinearity (SLN) estimator with a properly designed prefilter perform the best and their performances coincide with the asymptotic conditional Cramer-Rao bound (CCRB), which is the performance lower bound for the class of estimators under consideration.

In this paper, all the previously proposed digital blind feedforward symbol timing estimators employing second-order statistics are cast into a unified framework. The finite sample mean-square error (MSE) expression for this class of estimators is established. Simulation results are also presented to corroborate the analytical results. It is found that the feedforward conditional maximum likelihood (CML) estimator and the square law nonlinearity (SLN) estimator with a properly designed prefilter perform the best and their performances coincide with the asymptotic conditional Cramer-Rao bound (CCRB), which is the performance lower bound for the class of estimators under consideration.

A new decision-directed (DD) synchronization scheme is proposed for joint estimation of carrier frequency offset (CFO) and sampling clock frequency offset (SFO) in orthogonal frequency-division multiplexing (OFDM) systems. By exploiting the hard decisions, we report accurate estimators of residual CFO and small SFO. The performance analysis and simulation results indicate that the proposed novel DD scheme achieves much better performance than the conventional pilot-based schemes in both additive white Gaussian noise and frequency-selective channels.

This comment corrects several errors found in the paper, "Class of Cyclic-Based Estimators for Frequency-Offset Estimation of OFDM Systems". In addition, we show that the minimum variance unbiased estimator for frequency offset derived in the above paper is the maximum-likelihood estimator when the timing delay is perfectly known.

Space-time coding has received considerable interest recently as a simple transmit diversity technique for improving the capacity and data rate of a channel without bandwidth expansion. Most research in space-time coding, however, assumes that the symbol timing at the receiver is perfectly known. In practice, this has to be estimated with high accuracy. In this paper, a new symbol-timing estimator for space-time coding systems is proposed. It improves the conventional algorithm of Naguib et al. such that accurate timing estimates can be obtained even if the oversampling ratio is small. Analytical mean-square error (MSE) expressions are derived for the proposed estimator. Simulation and analytical results show that for a modest oversampling ratio (such as Q equal to four), the MSE of the proposed estimator is significantly smaller than that of the conventional algorithm. The effects of the number of transmit and receive antennas, the oversampling ratio, and the length of training sequence on the MSE are also examined.

Based on the maximum-likelihood principle and the preamble structure of IEEE 802.11a wireless local area network (WLAN) standard, this paper proposes a new symbol synchronization algorithm for IEEE 802.11a WLANs over frequency-selective fading channels. In addition to the physical channel, the effects of filtering and unknown sampling phase offset are also considered. Loss in system performance due to synchronization error is used as a performance criterion. Computer simulations show that the proposed algorithm exhibits better performances than the simple correlation-based algorithms. When compared to the algorithm based on the generalized Akaike information criterion, the proposed algorithm presents comparable performance and exhibits reduced complexity.

In This work, the problem of training sequence design for symbol timing estimation in MIMO channels is addressed. In particular, we consider correlated fading between antennas. The optimal training sequences are derived by minimizing the modified Cramer-Rao bound (MCRB) with respect to the training data. It is found that when the transmit pulse is a root raised cosine pulse and there is no correlation among antennas, the optimal training sequences resemble the Walsh sequences. Furthermore, it is also found that the the impact of not knowing the antenna correlations when designing training sequences is very small.

A new decision-directed (DD) synchronization scheme is proposed for joint estimation of carrier frequency offset (CFO) and sampling clock frequency offset (SFO) in coded orthogonal frequency division multiplexing (OFDM) systems. By exploiting the decisions provided by a Viterbi decoder and the information available on all the modulated subcarriers, we report accurate estimators of residual CFO and small SFO without relying on pilots. The performance analysis and simulation results indicate that the proposed novel DD scheme achieves much better performance than the conventional pilot-based schemes in both AWGN and frequency-selective channels.

In this paper, the maximum likelihood (ML) symbol timing estimator in a MIMO correlated channel, based on training data, is derived. It is shown that the approximated ML algorithm in (A. F. Naguib et al, IEEE J Select. Areas in Commun., vol.16, p.1459-1478, 1998) and (Y. C. Wu et al, IEEE Trans. on Wireless Comm., 2003) is just a special case of the proposed algorithm. Furthermore, the modified Cramer-Rao bound (MCRB) is also derived for comparison. Simulation results under different operating conditions (e.g., number of antennas and correlation between antennas) are given to assess the performances of the ML estimator and it is found that the mean square errors (MSE)s of the ML estimator: i) are close to the MCRBs; ii) are approximately independent of the number of transmit antennas; iii) are inversely proportional to the number of receive antennas; and iv) correlation between antennas has no effect on the MSE performance.

A new symbol-timing estimator for space-time coding systems is proposed. It improves the conventional algorithm of Naguib et al. such that accurate timing estimates can be obtained even if the oversampling ratio is small (such as oversampling ratio Q=4). The increase in implementation complexity with respect to that of the conventional algorithm is very small. The requirements and the design procedures for the training sequences are discussed. Analytical and simulation results show that the estimation mean square error of the proposed estimator is significantly smaller than that of the conventional algorithm.

This letter deals with the compensation of the self-noise (jitter) in a nondata-aided (blind) feedforward symbol-timing estimator using two samples per symbol for linearly modulated waveforms transmitted through additive white Gaussian noise channels. The main contribution of this letter is the derivation of an appropriate prefilter to achieve nearly jitter-free digital blind feedforward timing recovery. Computer simulations illustrate that the proposed prefiltering scheme improves significantly the performance at mid and high signal-to-noise ratios.

We propose a fast and reduced complexity frame and carrier acquisition scheme for orthogonal frequency-division multiplex systems which assumes either a continuous or a burst mode operation in additive white Gaussian noise and frequency-selective channels. By exploiting the repetitive structure of a training symbol, a robust frame synchronizer is obtained and shown to resume to finding the peak of a certain correlation metric that is obtained by invoking maximum likelihood principles. A modified carrier estimator that can correct frequency offsets up to two subcarrier spacings is also proposed. The efficiency of the proposed synchronization algorithms is illustrated by both theoretical performance analysis and computer simulations.

By exploiting a general cyclostationary (CS) statistics-based framework, this letter develops a rigorous and unified asymptotic (large sample) performance analysis setup for a class of blind feedforward timing epoch estimators for linear modulations transmitted through time nonselective flat-fading channels. Within the proposed CS framework, it is shown that several estimators proposed in the literature can be asymptotically interpreted as maximum likelihood (ML) estimators applied on a (sub)set of the second- (and/or higher) order statistics of the received signal. The asymptotic variance of these ML estimators is established in closed-form expression and compared with the modified Crame/spl acute/r-Rao bound. It is shown that the timing estimator proposed by Oerder and Meyr achieves asymptotically the best performance in the class of estimators which exploit all the second-order statistics of the received signal, and its performance is insensitive to oversampling rates P as long as P/spl ges/3. Further, an asymptotically best consistent estimator, which achieves the lowest asymptotic variance among all the possible estimators that can be derived by exploiting jointly the second- and fourth-order statistics of the received signal, is also proposed.

Abstract In this paper, the data aided (DA) and non‐data aided (NDA) maximum likelihood (ML) symbol timing estimators and their corresponding conditional Cramer–Rao bound (CCRB) and modified Cramer–Rao bound (MCRB) in multiple‐input‐multiple‐output (MIMO) correlated flat‐fading channels are derived. It is shown that the approximated ML algorithm in References [4,13] is just a special case of the DA ML estimator; while the extended squaring algorithm in Reference [14] is just a special case of the NDA ML estimator. For the DA case, the optimal orthogonal training sequences are also derived. It is found that the optimal orthogonal sequences resemble the Walsh sequences, but present different envelopes. Simulation results under different operating conditions (e.g. number of antennas and correlation between antennas) are given to assess and compare the performances of the DA and NDA ML estimators with respect to their corresponding CCRBs and MCRBs. It is found that (i) the mean square error (MSE) of the DA ML estimator is close to the CCRB and MCRB, (ii) the MSE of the NDA ML estimator is close to the CCRB but not to the MCRB, (iii) the MSEs of both DA and NDA ML estimators are approximately independent of the number of transmit antennas and are inversely proportional to the number of receive antennas, (iv) correlation between antennas has little effect on the MSEs of DA and NDA ML estimators and (v) DA ML estimator performs better than NDA ML estimator at the cost of lower transmission efficiency and higher implementation complexity. Copyright © 2004 John Wiley & Sons, Ltd.

This letter deals with the problem of nondata-aided carrier frequency offset estimation of noncircular modulations transmitted through unknown frequency-selective channels. By exploiting the unconjugated cyclostationary statistics induced by oversampling in the received waveform, an optimized carrier frequency offset estimator is proposed and its asymptotic (large sample) performance analyzed. In order for the proposed frequency estimator to achieve the minimum mean-square estimation error, it is shown that the oversampling rate need not be larger than twice the symbol rate. It is also shown that the proposed synchronizer is asymptotically jitter-free, admits a feedforward structure that may be implemented in digital form, and is suitable for burst transmissions.

Recently, S.J. Lee proposed a blind feedforward symbol timing estimator that exhibits low computational complexity and requires only two samples per symbol (see IEEE Commun. Lett., vol.6, p.205-7, 2002). We analyze Lee's estimator rigorously by exploiting efficiently the cyclostationary statistics present in the received oversampled signal; its asymptotic (large sample) bias and mean-square error (MSE) are derived in closed-form expression. A new blind feedforward timing estimator that requires only two samples per symbol and presents the same computational complexity as Lee's estimator is proposed. It is shown that the proposed new estimator is asymptotically unbiased and exhibits smaller MSE than Lee's estimator. Computer simulations are presented to illustrate the performance of the proposed new estimator with respect to Lee's estimator and existing conventional estimators.

The paper introduces and analyzes the asymptotic (large sample) performance of a family of blind feedforward nonlinear least-squares (NLS) estimators for joint estimation of carrier phase, frequency offset, and Doppler rate for burst-mode phase-shift keying transmissions. An optimal or "matched" nonlinear estimator that exhibits the smallest asymptotic variance within the family of envisaged blind NLS estimators is developed. The asymptotic variance of these estimators is established in closed-form expression and shown to approach the Cramer-Rao lower bound of an unmodulated carrier at medium and high signal-to-noise ratios (SNR). Monomial nonlinear estimators that do not depend on the SNR are also introduced and shown to perform similarly to the SNR-dependent matched nonlinear estimator. Computer simulations are presented to corroborate the theoretical performance analysis.

This paper introduces a family of blind feedforward nonlinear least-squares (NLS) estimators for joint estimation of the carrier phase and frequency offset of general quadrature amplitude modulated (QAM) transmissions. As an extension of the Viterbi and Viterbi (1983) estimator, a constellation-dependent optimal matched nonlinear estimator is derived such that its asymptotic (large sample) variance is minimized. A class of conventional monomial estimators is also proposed. The asymptotic performance of these estimators is established in closed-form expression and compared with the Cramer-Rao lower bound. A practical implementation of the optimal matched estimator, which is a computationally efficient approximation of the latter and exhibits negligible performance loss, is also derived. Finally, computer simulations are presented to corroborate the theoretical performance analysis and indicate that the proposed optimal matched nonlinear estimator improves significantly the performance of the classic fourth-power estimator.

This paper introduces a family of blind feedforward nonlinear estimators for joint estimation of carrier phase, frequency offset and Doppler rate for burst-mode phase shift keying (PSK) transmissions. An optimal or “matched” nonlinear estimator that exhibits the smallest asymptotic variance within the family of envisaged estimators is developed. The asymptotic performance of these estimators is established in closed-form expression and compared with the Cramèr-Rao lower bound for an unmodulated carrier. Finally, computer simulations are presented to corroborate the theoretical performance analysis.

This paper examines the asymptotic (large sample) performance of a family of non-data aided feedforward (NDA FF) nonlinear least-squares (NLS) type carrier frequency estimators for burst-mode phase shift keying (PSK) modulations transmitted through AWGN and flat Rician-fading channels. The asymptotic performance of these estimators is established in a closed-form expression and compared with the modified Cramer-Rao bound (MCRB). A best linear unbiased estimator (BLUE), which exhibits the lowest asymptotic variance within the family of NDA FF NLS-type estimators, is also proposed.

This letter proposes a class of blind feedforward carrier-phase estimators for higher order quadrature amplitude-modulated transmissions. As an extension of the Viterbi and Viterbi (V&V) estimator, a constellation-dependent optimal matched nonlinear estimator is derived such that its asymptotic variance is minimized. The asymptotic variances of the optimal matched and V&V estimators are established in closed-form expressions and compared. Computer simulations are presented to corroborate the theoretical performance analysis.

This paper considers the problem of blind symbol rate estimation of signals linearly modulated by a sequence of unknown symbols. Oversampling the received signal generates cyclostationary statistics that are exploited to devise symbol-rate estimators by maximizing in the cyclic domain a (possibly weighted) sum of modulus squares of cyclic correlation estimates. Although quite natural, the asymptotic (large sample) performance of this estimator has not been studied rigorously. The consistency and asymptotic normality of this symbol-rate estimator is established when the number of samples N converges to infinity. It is shown that this estimator exhibits a fast convergence rate (proportional to N/sup -3/2/), and it admits a simple closed-form expression for its asymptotic variance. This asymptotic expression enables performance analysis of the rate estimator as a function of the number of estimated cyclic correlation coefficients and the weighting matrix. A justification for the high performance of the unweighted estimator in high signal-to-noise scenarios is also provided.

This paper proposes to analyze the performance of a family of non-data aided open-loop carrier frequency offset (FO) estimators for a linearly modulated signal transmitted through an unknown flat-fading (possibly frequency-selective) channel. The exact asymptotic (large sample) performance of these estimators is established and analyzed as a function of the received signal sampling frequency, signal-to-noise ratio (SNR), timing delay, and number of samples (N). It is shown that in the presence of timing errors, the performance of the estimators can be improved by oversampling (fractionally sampling) the received signal and by taking into account the entire cyclostationary information that is present in the received sequence.

By exploiting the received signal's second-order cyclostationary statistics, blind algorithms have been previously proposed for joint estimation of the frequency offset and the symbol timing delay of a linearly modulated waveform transmitted through a flat-fading channel. The goal of this paper is to establish and analyze the asymptotic (large sample) performance of the Gini-Giannakis (1998) and Ghogho-Swami-Durrani (1999) estimators as a function of the pulse shape bandwidth and the oversampling factor. It is shown that the performance of these estimators improves as the pulse shape bandwidth increases, and the best performance is obtained by selecting small values for the oversampling factor.

We consider blind estimation of the carrier frequency offset of a linearly modulated non-circular transmission through an unknown frequency selective channel. A frequency estimator is developed based on the unique conjugate cyclic frequency of the received signal that equals twice the frequency offset. We establish consistency and asymptotic normality of the frequency estimator, and calculate its asymptotic variance in closed form. This expression enables performance analysis of the proposed frequency offset estimator as a function of the number of estimated cyclic correlation coefficients used. Numerical simulations show that estimation and compensation of the carrier frequency offset in the presence of an unknown frequency selective channel can be performed with no loss in performance relative to methods where the channel is pre-equalized first and the frequency offset is compensated afterwards.

Previous results have shown that blind channel estimators, which are robust to the location of channel zeros and channel order overestimation errors, can be derived for communication channels equipped with transmitted induced cyclostationarity (TIC) precoders. This paper addresses the problem of joint estimation of the unknown intersymbol interference (ISI) and carrier frequency offset using TIC-based set-ups. First, it is shown that the second-order cyclic statistics of the output allow recovery of the channel taps under a scaling factor ambiguity dependent on the unknown carrier offset frequency. Next, a carrier frequency estimator is proposed, and its asymptotic (large sample) performance is analyzed. It is shown that the asymptotic performance of the frequency estimator improves in the presence of a channel equalizer for high SNRs. Finally, numerical simulations are presented to collaborate the performance of proposed algorithms.

Periodic modulation precoders allow blind identification of SISO channels from output second-order cyclic statistics, irrespective of the location of channel zeros, color of additive stationary noise, or the channel order overestimation errors. The performance of blind channel estimators relying on periodic precoders is investigated. Criteria for optimal designs of periodic modulation precoders are also presented.