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This letter proposes novel approximate 4:2 compressors developed using input reordering circuits and input combination probabilities. The input reordering circuit is used to reduce the hardware complexity of the proposed designs. This letter proposes two designs of approximate 4:2 compressors. This compressor is used in designing an approximate multiplier. The proposed multiplier designs utilize less energy than the already published ones due to acceptable inaccurate output/precision, which are best suitable for image processing applications. The proposed multiplier designs MUL1, MUL2, MUL3, and MUL4 saves 22.75%, 21.95%, 11.57%, and 8.95% energy than the best of the existing design (Kong and Li, 2021). [ABSTRACT FROM AUTHOR]

In competitive electricity markets, prices are determined by the collective behaviour of suppliers and consumers. Hence, these systems rely on the balance between supply and demand, and sudden changes in the underlying conditions can lead to significant price fluctuations. In the face of the recent transformations in Italian electricity markets, which are driven by an increasing share of non-programmable renewables, energy crises, and geopolitical tensions, our study focuses on effective forecasting methodologies. We compare kernel and linear regression for predicting market equilibrium prices, both in point and probabilistic sense. We showcase the potential of both linear and non-linear models when carefully engineering the problem of interest, which involves properly selecting data and variables. The noteworthy outcome is that, while linear and non-linear models may differ in nature, their performance converges closely, attesting to the robustness of our approach in achieving reliable forecasts. We describe data sources and assumptions in exploratory univariate analyses, and the performance of the final multivariate model is evaluated over a test period on September 2023.

This tutorial aims to provide an intuitive introduction to Gaussian process regression (GPR). GPR models have been widely used in machine learning applications due to their representation flexibility and inherent capability to quantify uncertainty over predictions. The tutorial starts with explaining the basic concepts that a Gaussian process is built on, including multivariate normal distribution, kernels, nonparametric models, and joint and conditional probability. It then provides a concise description of GPR and an implementation of a standard GPR algorithm. In addition, the tutorial reviews packages for implementing state-of-the-art Gaussian process algorithms. This tutorial is accessible to a broad audience, including those new to machine learning, ensuring a clear understanding of GPR fundamentals. [ABSTRACT FROM AUTHOR]

The frequency of drought events has increased with climate change, making it vital to monitor and predict the response to drought. In particular, the relationship among meteorological, agricultural, and groundwater droughts needs to be characterized under different drought conditions. In this study, a probabilistic framework was developed for analyzing the spatio-temporal propagation of droughts and applied to South Korea. Three drought indices were calculated using satellite data and a deep learning model to determine the spatial and temporal extents of drought. The average propagation times were calculated. The time from meteorological to agricultural drought (MD-to-AD) was 2.83 months, and that from meteorological to groundwater drought (MD-to-GD) was 4.34 months. Next, the joint distribution among three drought types based on the best-fit copula functions was constructed. The conditional probabilities of drought occurrence were calculated on temporal and spatial scales. For instance, the probabilities of MD-to-GD propagation under light, moderate, severe, and extreme meteorological drought conditions were 38%, 43%, 48%, and 53%, respectively. The propagated drought occurrence probability was confirmed to be the highest under extreme antecedent drought conditions. The results of this study provide insight into the spatio-temporal drought propagation process from a probabilistic viewpoint. The use of satellite data and a deep learning model is expected to increase the efficiency of drought management practices such as vulnerability assessment and early warning system development.

Although the GPS-based positioning is ubiquitous for its high precision, the high power consumption brought by the high sampling frequency and the poor GPS signal penetration limits its availability in locating low-power mobile devices (especially mobile phones). As a promising complement, the cellular-based positioning has attracted great attention since it consumes much less power as well as its higher availability. However, the sparsity of cellular-based data (due to lower sampling rate) and large localization errors make the measurement accuracy becomes the main challenge of the cellular-based positioning. hidden Markov model can well solve the problem of positioning error of GPS data, but it is less accurate when applied to map matching of cellular-base data. Therefore, to improve accuracy, in this article, we propose a novel algorithm called the tailored hidden Markov model (THMM) that is optimized for the cellular-based data. Specifically, the geometric, the topological, and the probabilistic characteristics have been considered and fully exploited in the THMM design. Our proposed schemes are evaluated using real-world motor vehicle movement trajectories collected in Tianjin and the experimental results are encouraging compared with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAV) can perform high-speed and reliable transmissions with the properties in air superiority, high agility, and fast deployment. They have shown advantages in flexibility and reliability when the ground communication facilities cannot provide satisfactory services. In this article, a multi-UAV-based air–ground integrated network (AGIN) model is established with a novel joint optimal multi-UAV deployment framework, where UAVs serve as aerial base stations (BSs) or relays. To improve the communication performance of the AGIN, the following deployment schemes are proposed: 1) a static multi-UAV BS deployment to maximize the number of covered users by user clustering; 2) a mobile multi-UAV BS deployment to maximize the transmission performance of users, which is achieved by jointly optimizing UAV trajectories and user scheduling; 3) an optimized UAV multihop relay deployment to minimize the number of UAV relays and the communication outage probability. Moreover, UAV formation relay is deployed as a virtual multiantenna array to improve the relaying performance by beamforming and orthogonal space-time block coding, and some further open researches and challenges are also discussed. [ABSTRACT FROM AUTHOR]

The identification of faults for gearbox plays a significant role in maintaining high reliability of industrial equipment. Multivariate invertible deep probabilistic learning (MIDPL), as a generative model, assesses the distribution characteristics of the hidden condition using observations. It is a high-dimensional-to-high-dimensional transformation and is limited by poor classification performance. In this article, semisupervised MIDPL (S-MIDPL) fault identification method is proposed to overcome the drawback of MIDPL. First, the dimension of the observation is reduced. Second, supervised learning is performed based on the reduced dimension to improve the classification performance. Fault identification validations on a gearbox test bench and wind turbine gearbox are conducted to investigate the effectiveness of the proposed method. Comparisons with other state-of-the-art classification methods have also been conducted. The results indicate that S-MIDPL has a significant advantage over MIDPL and a variational inference-based semisupervised approach. [ABSTRACT FROM AUTHOR]

Covert communications is critical in many application scenarios for ensuring transmission security and privacy. Two-way protocols are widely adopted in intelligent reflecting surface (IRS) assisted relaying networks to enable covert communications and prevent wireless signals from being overheard. To quantify the performance of covert communications in this special scenario, we derive the closed-form expression of outage probability and its asymptote in this paper. Considering the worst-case of covert communications, the optimal normalized power threshold of warden's detector is analyzed under a complex Gaussian distribution approximation. To meet the requirement of the detection error probability, the ratio of the transmission power between the covert nodes is investigated. Simulation results are provided to validate the theoretical expressions as well as the asymptotic analysis with a small number of reflecting elements. [ABSTRACT FROM AUTHOR]

In this work, we consider a hybrid aerial full-duplex (FD) relaying protocol consisting of a reconfigurable intelligent surface (RIS) mounted over an FD unmanned aerial vehicle (UAV) relay operating in the decode and forward mode to assist the information transfer between the base station and multiple users. For better spectral efficiency, we investigate the use of non-orthogonal multiple access (NOMA) in such networks and focus on both the performance analysis and design optimization of the considered RIS-NOMA network under imperfect channel state information (CSI) and successive interference cancellation (SIC) at each user, and residual-self interference (RSI) at UAV. We first formulate the sum rate maximization problem and adopt the block coordinate descent method to deal with the non-convex nature of the problem. Thereafter, we propose an algorithm based on the Riemannian conjugate gradient method to get the optimal phase shifts at the RIS, an iterative algorithm to obtain the optimal UAV/RIS position and the exhaustive method to obtain the optimum power allocation coefficients. Next, with obtained optimal position, phase shift and power coefficients, we further analyze the performance of the network and derive the closed-form expressions of outage probability, achievable throughput and ergodic capacity. We present Monte Carlo simulation-based results to validate the accuracy of the proposed algorithms and derived expressions and demonstrate the superiority of NOMA over OMA. [ABSTRACT FROM AUTHOR]

This paper studies the unmanned aerial vehicle (UAV) based multiple intelligent reflecting surface (IRS) network, where the hovering UAV acts as a base station, and the IRS enhances signal transmission to across obstacle between users and UAV. To achieve the maximum sum rate of proposed communication scenario, a non-convex problem considering IRS association results, hovering altitude of UAV, and the phase shift design of multi-IRS is formulated. From the IRS association problem, we can find that the IRS association results are coupled to the decoding order of non-orthogonal multiple access (NOMA). To tackle this, a mathematical interference expansion scheme is developed to decouple it and transform it to convex by binary relaxation method. The non-convexity of hovering altitude optimization problem is solved by logarithm operation, approximation, and auxiliary matrices. For the phase shift optimization problem of multi-IRS, we propose a gradient approximation based initial scheme and develop a univariate optimization based approach on the basis to achieve the users sum rate improvement in multi-IRS. In the end, we compare the proposed scheme with baseline scheme to present the superiority of this work under various network settings. The internal reasons for the variation of simulation results are also analyzed. [ABSTRACT FROM AUTHOR]

The relay positions or hop distances in a multi-hop relaying scheme is an important parameter that can be optimized in order to mitigate the angle-of-arrival variance or pointing error in a free-space optical (FSO) backhaul link. In this study, we have optimized the relay positions for amplify-and-forward and decode-and-forward relays in a multi-hop unmanned aerial vehicle-based relaying scheme for FSO. Particularly, we have shown that a significant performance improvement may be achieved by optimizing the outage probability as a function of the hop distance for amplify-and-forward relays when the relays are constrained by a finite power gain. Additionally, we have discovered that for a low signal-to-noise ratio channel, the optimal hop distance of a particular hop for decode-and-forward relays is inversely proportional to angle-of-arrival variance in that hop. [ABSTRACT FROM AUTHOR]

In this paper, we investigate physical-layer security of the uplink millimeter wave communications for a cellular vehicle-to-everything (C-V2X) network comprised of a large number of base stations (BSs) and different categories of V2X nodes, including vehicles, pedestrians, and road side units. Considering the dynamic change and randomness of the topology of the C-V2X network, we model the roadways, the V2X nodes on each roadway, and the BSs by a Poisson line process, a 1D Poisson point process (PPP), and a 2D PPP, respectively. We propose two uplink association schemes for a typical vehicle, namely, the smallest-distance association (SDA) scheme and the largest-power association (LPA) scheme, and we establish a tractable analytical framework to comprehensively assess the security performance of the uplink transmission, by leveraging the stochastic geometry theory. Specifically, for each association scheme, we first obtain new expressions for the association probability of the typical vehicle, and then derive the overall connection outage probability and secrecy outage probability by calculating the Laplace transform of the aggregate interference power. Numerical results are presented to validate our theoretical analysis, and we also provide interesting insights into how the security performance is influenced by various system parameters, including the densities of V2X nodes and BSs. Moreover, we show that the LPA scheme outperforms the SDA scheme in terms of secrecy throughput. [ABSTRACT FROM AUTHOR]

Underlay device-to-device (D2D) communication network is becoming a promising solution for the fifth generation (5G) and beyond wireless technology. It exploits the proximity of the D2D pairs and improves the overall network’s latency, capacity, and spectral efficiency by sharing/reusing the existing cellular resources. However, due to the frequency-sharing/reusing, the security of the device users (DUs) and the cellular users (CUs) becomes vulnerable. This paper presents a novel physical-layer security (PLS) scheme for the underlay multiple-input multiple-output (MIMO) D2D communications in the presence of multiple eavesdroppers. The proposed new PLS scheme can significantly reduce the information leakage for both CUs and DUs by adopting a null steering scheme at the transmitter. A signal alignment technique is also employed to eradicate the stringent requirement of a larger number of transmitter antennas than that of the receiver antennas. A generalized nonlinear optimization problem has been formulated to improve the PLS performance for MIMO-D2D communications. A closed-form and generalized analytical expression of the secrecy outage probability for CUs and DUs is derived over the imperfect Nakagami- $m$ and Norton fading channels. Theoretical and simulation results of our proposed new PLS scheme have shown significant improvement in the secrecy capacity and secrecy outage probability for both CUs and DUs in comparison with the existing methods. [ABSTRACT FROM AUTHOR]

This paper investigates the power coverage probability in cooperative wireless powered communication networks, where multiple access points collaborate to meet the energy demands of low-power devices. Based on the theory of Poisson-Delaunay triangulation, the probability density functions (PDF) of the Euclidean distance between the access points of the Poisson-Delaunay triangular cell and typical devices are derived. By using the theory of stochastic geometry and the obtained PDFs, the closed-form expressions of the wireless power coverage probability are obtained for three typical locations of the devices. As the wireless power coverage probability expressions involve the extended generalized multivariate MeijerG function (EGMMGF), a new implementation enabling numerical calculation of the EGMMGF is also proposed. The impacts of the main network parameters on the performance of the proposed framework are analyzed. In particular, comparative results show the significant gains that can be achieved in the wireless power coverage when multiple access points participate in the wireless power transfer or when the density of the network’s access points is increased, as compared to the non-cooperative scheme. [ABSTRACT FROM AUTHOR]

A connection between the general linear model (GLM) with frequentist statistical testing and machine learning (MLE) inference is derived and illustrated. Initially, the estimation of GLM parameters is expressed as a Linear Regression Model (LRM) of an indicator matrix; that is, in terms of the inverse problem of regressing the observations. Both approaches, i.e. GLM and LRM, apply to different domains, the observation and the label domains, and are linked by a normalization value in the least-squares solution. Subsequently, we derive a more refined predictive statistical test: the linear Support Vector Machine (SVM), that maximizes the class margin of separation within a permutation analysis. This MLE-based inference employs a residual score and associated upper bound to compute a better estimation of the actual (real) error. Experimental results demonstrate how parameter estimations derived from each model result in different classification performance in the equivalent inverse problem. Moreover, using real data, the MLE-based inference including model-free estimators demonstrates an efficient trade-off between type I errors and statistical power. [ABSTRACT FROM AUTHOR]

This letter characterizes the contact angle distribution based on the condition that the relay low earth orbit (LEO) satellite is in the communication range of both the ground transmitter and the ground receiver. As one of the core distributions in stochastic geometry-based routing analysis, the analytical expression of the Cumulative Distribution Function (CDF) of the conditional contact angle is derived. Furthermore, the conditional contact angle is applied to analyze the inaccessibility of common satellites between the ground transmitter and receiver. Finally, with the help of the conditional contact angle, coverage probability and achievable data rate in LEO satellite-relayed transmission are studied. [ABSTRACT FROM AUTHOR]

This article addresses the problem of threshold region characterization of the maximum likelihood (ML) sinusoid frequency estimation. We first study on the exact analytical expression of the probability of detection for the ML mean square error for all the signal-to-noise ranges. Then, we propose a simple asymptotic expression to this ML detection probability and propose a model for the characterization of the variance of the frequency estimation. We also provide asymptotic closed-form expressions to the threshold and the no-information signal-to-noise ratio breakdowns for the ML frequency estimation, respectively. Outcomes of extensive numerical simulations verify our proposed theoretical derivations. [ABSTRACT FROM AUTHOR]

With the explosion of GPS-enabled smartphones and social media platforms, geo-social networks are increasing as tools for businesses to promote their products or services. Influence maximization, which aims to maximize the expected spread of influence in the networks, has drawn increasing attention. However, most recent work tries to study influence maximization by only considering geographic distance, while ignoring the influence of users’ spatio-temporal behavior on information propagation or location promotion, which can often lead to poor results. To relieve this problem, we propose a Similarity-aware Influence Maximization (SIM) model to efficiently maximize the influence spread by taking the effect of users’ spatio-temporal behavior into account, which is more reasonable to describe the real information propagation. We first calculate the similarity between users according to their historical check-ins, and then we propose a Propagation to Consumption (PTC) model to capture both online and offline behaviors of users. Finally, we propose two greedy algorithms to efficiently maximize the influence spread. The extensive experiments over real datasets demonstrate the efficiency and effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Faced with massive data, subsampling is a commonly used technique to improve computational efficiency, and using nonuniform subsampling probabilities is an effective approach to improve estimation efficiency. For computational efficiency, subsampling is often implemented with replacement or through Poisson subsampling. However, no rigorous investigation has been performed to study the difference between the two subsampling procedures such as their estimation efficiency and computational convenience. This paper performs a comparative study on these two different sampling procedures. In the context of maximizing a general target function, we first derive asymptotic distributions for estimators obtained from the two sampling procedures. The results show that the Poisson subsampling may have a higher estimation efficiency. Based on the asymptotic distributions for both subsampling with replacement and Poisson subsampling, we derive optimal subsampling probabilities that minimize the variance functions of the subsampling estimators. These subsampling probabilities further reveal the similarities and differences between subsampling with replacement and Poisson subsampling. The theoretical characterizations and comparisons on the two subsampling procedures provide guidance to select a more appropriate subsampling approach in practice. Furthermore, practically implementable algorithms are proposed based on the optimal structural results, which are evaluated through both theoretical and empirical analyses. [ABSTRACT FROM AUTHOR]

Data-driven fault diagnosis primarily involves the identification of different fault locations and fault severities. Focusing on a challenging task for which the target fault severities do not exist in the training samples, this article proposes a generalized cross-severity bearing fault diagnosis scheme based on a novel hierarchical cross-category inference framework. The proposed method uses an outlier detection scheme based on unsupervised feature mapping and local outlier probability calculation to identify the unseen samples. A neural network embedded with a tree-structured decision layer acts as a backbone to execute fault diagnosis at different hierarchies for different sample types, seen or unseen. Additionally, the metric learning method is used to support the approximate severity inference of the unseen samples after the fault locations are identified in the hierarchical model. Experiments performed on an aeronautical bearing test rig revealed that the proposed scheme is both feasible and superior to existing methods. [ABSTRACT FROM AUTHOR]

As one of the two fundamental types of multiple access, random access has been widely adopted in various communication networks, and expected to play an increasingly central role owing to the rising popularity of Machine-to-Machine (M2M) communications. Despite decades of successful applications, the theory of random access has long been underdeveloped, with key fundamental issues unresolved. Among them, stability of random-access networks is the most long-standing one that has received continuous attention for almost half a century. The challenge lies in establishing an analytical framework where the coupled service processes of nodes’ queues can be characterized. In this paper, by extending our previously proposed analytical framework from the symmetric scenario to the general one, we tackle three open questions: 1) How to characterize the coupled service rates of nodes? 2) How to determine the stability region of input rates, only within which the network can be stabilized? 3) For given input rates within the stability region, how to tune the transmission probabilities of nodes to stabilize the network? We demonstrate that the key to characterizing the coupled service rates lies in properly establishing and solving the fixed-point equations of steady-state probabilities of successful transmission of Head-of-Line (HOL) packets of nodes. For the stability region of input rates, which closely depends on the definition of stability, two types of stability, i.e., queue-stability and throughput-stability, are considered, and both stability regions are shown to be determined by the sufficient and necessary condition of the existence of positive real roots of the fixed-point equations. To characterize the operating regions of transmission probabilities, constraints need to be further developed to ensure that the network operates at the specific steady-state point. The analysis shows that to stabilize the network, the transmission probabilities of nodes can be tuned only based on their long-term traffic input rates. Although the main results are illustrated based on Aloha with Constant Backoff, discussions on how to incorporate a general backoff function and other features of random access are also presented. [ABSTRACT FROM AUTHOR]

Unmanned-aerial-vehicle (UAV)-based communications are expected to play an important role in the future generation wireless communication networks. It is a promising solution to enhance the wireless connectivity of devices without infrastructure. As compared to the terrestrial communications, UAVs offer various advantages such as line-of-sight (LoS) connectivity, dynamic deployment and flexible reconfiguration. Further, free space optics (FSO) communication is considered as a possible solution to enable UAV-based communication links due to its cost effective and high bandwidth nature. However, despite offering large bandwidth, UAV-based FSO links are marred by atmospheric path loss, atmospheric turbulence, non-zero boresight pointing errors, and angle-of-arrival (AoA) fluctuations. In this paper, the performance of a hovering UAV-based FSO communication system is investigated. The closed-form expressions for outage probability, average symbol error rate (SER), and ergodic capacity are derived taking into account both heterodyne (HD) and direct detection (DD) techniques for ground-to-UAV (G2U), UAV-to-UAV (U2U), and UAV-to-ground (U2G) links over the generalized Málaga distribution. Moreover, the asymptotic expressions for the above performance metrics are also derived to get the diversity gain of the system. [ABSTRACT FROM AUTHOR]

In this paper, we model the multi-layer vertical underwater link as a cascaded channel and unify the performance analysis for the underwater optical communication (UWOC) system using generalized Gamma (GG), exponential GG (EGG), exponentiated Weibull (EW), and Gamma-Gamma ($\Gamma \Gamma$) oceanic turbulence models. We derive unified analytical expressions for probability density function (PDF) and cumulative distribution function (CDF) for the signal-to-noise ratios (SNR) considering independent and non-identical (i.ni.d.) turbulent models and zero bore-sight model for pointing errors. We develop performance metrics of the considered UWOC system using outage probability, average bit error rate (BER), and ergodic capacity with asymptotic expressions for outage probability and average BER. We develop the diversity order of the proposed system to provide a better insight into the system performance at a high SNR. We also integrate a terrestrial OWC (TOWC) subjected to the combined effect of generalized Malága atmospheric turbulence, fog-induced random path gain, and pointing errors to communicate with the UWOC link using the fixed-gain amplify-and-forward (AF) relaying. We analyze the performance of the mixed TWOC and multi-layer UWOC system by deriving PDF, CDF, outage probability, and average BER using the bivariate Fox H-function. We use Monte-Carlo simulation results to validate our exact and asymptotic expressions and demonstrate the performance of the considered underwater UWOC system using measurement-based parametric data available for turbulent oceanic channels. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) is regarded as a promising technology to assist indoor millimeter wave (mm-Wave) communication due to its ability to manipulate electromagnetic waves directionally. Due to the complexity of indoor environment, different RIS deployment strategies significantly affect the system performance. However, the existing work investigating RIS indoor deployment ignored the influence of human mobility to indoor signal coverage, therefore a practical indoor deployment strategy remains unknown. In this letter, based on the consideration of human blockage to mm-Wave signal and probability distribution of human location, we propose an indoor RIS deployment strategy with human mobility model to minimize the outage probability, which is more applicable to the realistic indoor environment. An algorithm is presented to minimize the outage probability at a subspace scale of the room geometry and calculate the corresponding RIS location for single RIS deployment and multiple RISs cooperative deployment. Numerical results are provided to display how our deployment strategy works, which indicate that deploying RIS correctly can decrease the indoor communication outage probability significantly. [ABSTRACT FROM AUTHOR]

This work addresses the issue of spectrum sensing with random arrival and departure of primary signals. We first design a convolutional neural network (CNN) with outputs as the posterior probabilities of the arrival and departure of primary signals, leading to a CNN-based detector with the ratio of the posterior probabilities (i.e., the outputs of the CNN) as a test statistic. To further enhance the attention of the network on the switch feature of channel states, we design a switch attention module (SAM) that adaptively weights the received signals. Replacing the convolution plus maximum pooling block in the CNN detector with the SAM block leads to an SAM-CNN detector. Simulations show that the proposed CNN detector significantly outperforms existing detectors, and further improvement of detection probability by 19% is achieved by the SAM-CNN detector. [ABSTRACT FROM AUTHOR]

In this letter, we investigate the problem of buffer-aided relaying in multi-hop systems that comprise a source, destination and $K$ relays equipped with buffers of size $L$. We propose three relaying strategies and compare their performances. The three strategies account for the channel and buffer states at every time slot. The first strategy considers only the state of the previous relay buffer, the second accounts for the next relay whereas the third scheme accounts for the previous and next relays’ states. We evaluate the asymptotic performance of the three schemes and we derive closed-form expressions of the diversity order (DO) and average packet delay (APD) for all values of $K$ and $L$. Results show the ability of the three schemes to achieve multiple levels of tradeoff between the performance metrics. [ABSTRACT FROM AUTHOR]

This letter studies a UAV-assisted communication design where a rotary-wing UAV is launched to provide continuous coverage to mobile nodes. We aim to maximize the service time duration of the UAV by optimizing its three-dimensional (3D) trajectory while simultaneously ensuring adequate coverage to mobile nodes. Considering the limited energy available with the UAV, we frame an equivalent energy consumption minimization problem. Though the formulated problem is non-convex, it is shown that it is pseudoconvex under horizontal and vertical UAV coordinates. Based on this observation, we use alternating optimization to solve the problem. Numerical results provide trajectory insight and compare the service duration to the mobile nodes with different schemes. [ABSTRACT FROM AUTHOR]

Process mining is the discipline of analyzing and improving processes which are known as an event log. The real-life event log contains noise, infrequent behaviors, and numerous concurrency, in effect the generated process model through process discovery algorithms will be inefficient and complex. Shortcomings in an event log result in current process discovery algorithms failing to pre-process data and describe real-life phenomena. Existing process mining algorithms are limited based on the algorithm’s filtering, parameters, and pre-defined features. It is critical to use a high-quality event log to generate a robust process model. However, pre-processing of the event log is mostly cumbersome and is a challenging procedure. In this paper, we propose a novel pre-processing step aimed to obtain superior quality event log from a set of raw data, consequently a better performing process model. The proposed approach concatenates events which hold concurrent relations based on a probability algorithm, producing simpler and accurate process models. This proposed pre-processing step is based on the probability of the frequency of concurrent events. The performance of the pre-processing approach is evaluated on 18 real-life benchmark datasets that are publicly available. We show that the proposed pre-processing framework significantly reduces the complexity of the process model and improves the model’s F-Measure.

As an efficient and interference-resistant coding technique, multiple description coding (MDC) is proposed with the aim to solve the transmission unreliability problem caused by packet errors, loss or blocking delays. The integration of MDC with non-orthogonal multiple access (NOMA) scheme (MDC-NOMA) can effectively boost the robustness and throughput of the underlying system. Additionally, space-time block coding (STBC) can further enhance the system reliability with increased diversity gain, as well as reduced decoding complexity. In this paper, we propose a novel framework referred to as MDC-NOMA-STBC, in which we apply Alamouti STBC integrated with MDC and NOMA to implement more reliable transmission. Specifically, NOMA signals are constructed by superimposing the descriptions from different users, then transmitted by the base station (BS) using Alamouti STBC. In order to substantiate the performance of the proposed framework, first, we derive closed-form expressions of both the outage probability and ergodic rate for each user in a two-antenna BS scenario. Second, for multi-antenna BS, we investigate different antenna selection strategies to further enhance the outage performance under the considered framework, whose analytical expression is provided wherever possible. Third, Monte Carlo simulation results are presented to validate our theoretical framework and to corroborate the superiority of the proposed MDC-NOMA-STBC over state-of-the-art MDC-NOMA scheme. Moreover, under realistic contexts with image transmission, it is confirmed that MDC-NOMA-STBC outperforms its counterpart MDC-NOMA in terms of the peak signal-to-noise ratio and the bit error rate. [ABSTRACT FROM AUTHOR]

We consider multiple unmanned aerial vehicles (UAVs) serving a density of ground terminals (GTs) as base stations. The objective is to minimize the outage probability of GT-to-UAV transmissions. Optimal placement of UAVs under different UAV altitude constraints and GT densities is studied. A practical variant of the Rician fading model, which has been developed by Azari et al. (2018) specifically for UAV systems, is used to model the communication channels. First, the structure of optimal deployments is determined when the common altitude constraint is large. For a wide class of GT densities, it is shown that all UAVs should be placed to the same location in an optimal deployment. A design implication is that one can use a single multi-antenna UAV as opposed to multiple single-antenna UAVs without loss of optimality. Next, using a random deployment argument, a general upper bound on the optimal outage probability is found for any density of GTs and any number of UAVs. Further, for any arbitrary user density, centralized and distributed numerical algorithms are designed using particle swarm optimization and modified gradient descent algorithms, respectively. Simulations show that our distributed algorithm provides almost optimal results even with considerably reduced communication and sensing ranges at the UAVs. [ABSTRACT FROM AUTHOR]

This article establishes an analytical framework for the upper bound on the average Age-of-Information (AoI) in first-come-first-served (FCFS) wireless communications where a certain level of outage probability is unavoidable. To begin with, we analyze the average AoI and derive a general upper bound for G/G/1 systems with a certain outage probability. Subsequently, for an M/M/1 system with the FCFS scheme, we obtain a concise closed-form expression of the upper bound, and further refine the upper bound after analyzing the relative error. Interestingly, it is found by the analysis that the relative error is independent of the service rate, and the upper bound becomes tighter as the outage probability increases. Based on the refined upper bound, we minimize the average AoI for the communications suffering from block Rayleigh fading. We derive a closed-form expression of the outage probability over a fading channel, and then prove that the refined upper bound is a convex function with respect to the average update generating rate. Consequently, we optimize the AoI performance by solving a convex optimization problem formulated utilizing the refined upper bound expression. The numerical results indicate that the minimum average AoI can be reduced by either increasing the service rate or the transmission power. [ABSTRACT FROM AUTHOR]

In this paper, we propose a robust secrecy transmission scheme for intelligent reflecting surface (IRS) aided communication systems. Different from all the existing works where IRS has already been deployed at a fixed location, we take the location of IRS as a variable to maximize the secrecy rate (SR) under the outage probability constraint by jointly optimizing the location of IRS, transmit beamformer and IRS phase shifts with imperfect channel state information (CSI) of Eve, where we consider two cases: a) the location of Eve is known; b) only a suspicious area of Eve is available. We show a critical observation that CSI models are different before and after IRS deployment, thus the optimization problem could be decomposed and solved via a two-stage framework. For case a), in the first stage, universal upper bounds of outage probabilities only related to the location of IRS are derived which can be optimized via successive convex approximation (SCA) method. In the second stage, we develop an alternative optimization (AO) algorithm to optimize beamformer and phase shifts iteratively. For case b), we propose a Max-Min SR scheme based on two-stage framework, where the location of IRS is optimized based on the worst location of Eve. Simulation results indicate the importance of the location of IRS optimization. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the physical layer security for a cognitive multiuser network which is composed of multiple cognitive sources, a cognitive destination and an eavesdropper under the joint impact of hardware impairments (HIs) and channel estimation errors (CEEs). We consider a practical scenario where mutual interference exists between the primary users and cognitive users. To achieve high physical layer security with low implementation complexity, we propose three pure user scheduling schemes, namely, selection combining (SC) scheme, threshold-based switched diversity (tSD) scheme and switch-and-examine combining with post-selection (SECps) scheme. To further improve physical layer security, we present an extension of our SC framework to a jammer aided multiuser network and propose a jammer aided SC (JSC) scheme. We derive the closed-form intercept probability (IP), outage probability (OP) and effective secrecy throughput (EST) expressions for SC, tSD, SECps and JSC schemes over Nakagami- $m$ channels to analyze the system performance. Numerical results show that among the three pure multiuser scheduling schemes, the SC scheme achieves the best secrecy performance with the highest complexity, the SECps scheme obtains the worst secrecy performance with the lowest complexity. In addition, the secrecy performance of JSC scheme is better than that of SC scheme in the high SNR region. [ABSTRACT FROM AUTHOR]

The reconfigurable intelligent surface (RIS) is a promising technology that is anticipated to enable high spectrum and energy efficiencies in future wireless communication networks. This paper investigates optimum location-based RIS selection policies in RIS-aided wireless networks to maximize the end-to-end signal-to-noise ratio for product-scaling and sum-scaling path-loss models where the received power scales with the product and sum of the transmitter-to-RIS and RIS-to-receiver distances, respectively. These scaling laws cover the important cases of end-to-end path-loss models in RIS-aided wireless systems. The random locations of all available RISs are modeled as a Poisson point process. To quantify the network performance, the outage probabilities and average rates attained by the proposed RIS selection policies are evaluated by deriving the distance distribution of the chosen RIS node as per the selection policies for both product-scaling and sum-scaling path-loss models. We also propose a limited-feedback RIS selection framework to achieve distributed network operation. The outage probabilities and average rates obtained by the limited-feedback RIS selection policies are derived for both path-loss models as well. The numerical results show notable performance gains obtained by the proposed RIS selection policies. [ABSTRACT FROM AUTHOR]

The combination of reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) has been recognized as a critical method to improve the sixth generation networks performance. In this paper, a distributed RIS-aided downlink multiple-input multiple-output (MIMO) NOMA systems with discrete phase shifts are studied, where the channel directions from base station to paired users can be manipulated with the assistance of RISs by employing the concept of signal alignment. In order to ensure base station can flexibly serve some users with NOMA and others users with spatial division multiple access, a unified precoder and decoder are provide to cancel inter-cluster interference. Subsequently, the channel statistics over Nakagami- $m$ fading channels are derived for near and far users. In particular, considering the cascade channel gain may exists two different cases, Beaulieu series is further adopted to characterize their corresponding cumulative distribution function. In what follows, the outage probability and ergodic rate for three situations within one cluster are derived by utilizing the obtained channel statistics, respectively. Based on the derived results, we also analyze the diversity order and high signal-to-noise ratio slope to provide essential insights into the considered systems. Finally, simulation results are presented to reveal that: 1) selecting the setting of 3-bits resolution can realize a near-aligned performance for our proposed systems; 2) the cascade channel statistic caused by RIS can be evaluated with any number of RIS element and any channel gain via Beaulieu series. [ABSTRACT FROM AUTHOR]

In the past few years, many companies are considering “social recommendation” for their businesses, e.g., firms are offering rewards to customers who recommend the firms’ products/services in online social networks (OSNs). However, the pros and cons of such social recommendation scheme are still unclear. Thus, it is difficult for firms to design rewarding schemes, and for OSN platforms to design regulating policies. By analyzing real data from Weixin and Yelp, we first identify key factors that affect the spreading of products/services in OSNs. These findings enable us to develop an accurate (i.e., with a high validation accuracy) mathematical model on social recommendations. Our model captures how users decide whether to recommend an item, which is a key factor but often ignored by previous social recommendation models such as the “Independent Cascade model”. We also design algorithms to infer model parameters. Using our model, we uncover conditions when social recommendation improves a firm’s profit and users’ utilities, as well as when it cannot improve the profit or hurts users’ utilities. These conditions help the design of both rewarding schemes and regulating policies. Moreover, we extend our model to a dynamic setting, so that a firm can improve its profit by dynamically optimizing its rewarding schemes. [ABSTRACT FROM AUTHOR]

In ICISC-05, and in the ePrint 2010/287, Patarin claimed a lower bound on the number of $2 q$ tuples of $n$ -bit strings $(P_{1}, \ldots, P_{2q}) \in ({\{0,1\}}^{n})^{2q}$ satisfying $P_{2i - 1} \oplus P_{2i} = \lambda _{i}$ for $1 \leq i \leq q$ such that $P_{1}, P_{2}, \ldots $ , $P_{2q}$ are distinct and $\lambda _{i} \in {\{0,1\}} ^{n} \setminus \{0^{n}\}$. This result is known as Mirror theory and widely used in cryptography. It stands as a powerful tool to provide a high-security guarantee for many block cipher-(or even ideal permutation-) based designs. In particular, Mirror theory has a direct application in the security of XOR of block ciphers. Unfortunately, the proof of Mirror theory contains some unverifiable gaps and several mistakes. This paper provides a simple and verifiable proof of Mirror theory. [ABSTRACT FROM AUTHOR]

Hybrid automatic repeat request (HARQ) is a key enabler for ultrareliable communications. This paper optimizes transmit power for the initial transmission and the subsequent retransmissions of HARQ with either incremental redundancy or Chase combining, aiming to minimize the expected energy consumption given the target outage probability and the target latency. The main challenge is due to the fact that the outage probability is a complicated function of the power variables which are nested in successive convolutions. The existing works mostly use a classic upper bound to approximate the outage probability by assuming unbounded transmit power, then convert the original problem to a geometric programming (GP) problem. In contrast, we propose a novel and much tighter upper bound by taking the practical power limit into consideration. The new bound and the resulting new GP method are further extended to a broader group of channel models with various fading, multiple antennas, and multiple receivers. As shown in simulations, the GP method based on the new bound significantly outperforms the existing strategies that either fix transmit power or optimize power by the classic bounding technique. [ABSTRACT FROM AUTHOR]

Multiply and accumulate (MAC) composed of a set of multipliers and one reduction dominates the latency and power of convolutional neural network (CNN) accelerators. Existing approximate multipliers reduce latency and power at a tolerable drop in accuracy, without considering the data distribution (implicitly assuming that data are uniformly distributed). This letter discloses that practical CNNs’ activations and weights are usually Gaussian-like distributed, and the bits of quantized activations and weights are typically not with a probability of 0.5. Thus, we propose an approximate MAC unit design by taking into account the statistical features of input data, to achieve a balanced tradeoff among latency, power, and accuracy. The extensive experiments show that our proposed MAC unit design provides much higher accuracy than state-of-the-art approximate circuits, while the latency, area, and power are similar. [ABSTRACT FROM AUTHOR]

Energy-aware resource allocation to achieve desired throughput over cooperative power line communications (PLCs) has gained growing interest from past few years. In this article, we design an energy-efficient amplify-and-forward relay-assisted PLC, where outage probability based on energy-efficiency is minimized by jointly optimizing the relay location and power allocation (PA) in two scenarios: 1) absence and 2) presence of the end-to-end direct link. To reduce complexity, using a novel and nontrivial approach, we obtain a closed-form expression of the outage probabilities for both the scenarios. Further, an equivalent problem is formulated to make the optimization problem more tractable to obtain its solution. In the absence of direct link, a closed-form solution of the problem is obtained by the optimization schemes where underlying variables are optimized individually and jointly. While, in the presence of the direct link, a closed-form solution is obtained using a nontrivial tight approximation algorithm. Via numerical results, the outage expressions are validated, and various insights on optimization of PA and relay location are obtained. Finally, with respect to energy-efficiency, it is shown that the joint optimization gives an outage improvement of around 55% and 37% in the absence and presence of the direct link, respectively. [ABSTRACT FROM AUTHOR]

Self-energyrecycling (sER), which allows transmit energy reutilization, has emerged as a viable option for improving the energy efficiency (EE) in low-power Internet of Things networks. In this article, we investigate its benefits also in terms of reliability improvements and compare the performance of full-duplex (FD) and half-duplex (HD) schemes when using multiantenna techniques in a communication system. We analyze the tradeoffs when considering not only the energy spent on transmission but also the circuitry power consumption, thus making the analysis of much more practical interest. In addition to the well-known spectral efficiency improvements, results show that FD also outperforms HD in terms of reliability. We show that sER introduces not only benefits in EE matters but also some modifications on how to achieve maximum reliability fairness between uplink and downlink transmissions, which is the main goal in this article. In order to achieve this objective, we propose the use of a dynamic FD scheme where the small base station determines the optimal allocation of antennas for transmission and reception. We show the significant improvement gains of this strategy for the system outage probability when compared to the simple HD and FD schemes. [ABSTRACT FROM AUTHOR]

As public safety migrates from existing digital land mobile radio networks to fourth generation (4G) long-term evolution (LTE), a novel feature in the latter is the enablement of direct communications between devices. In this article, we examine the performance of the LTE device-to-device direct discovery process in out-of-coverage scenarios (Mode 2). We model individual discovery periods as a slotted random access protocol with half-duplex user equipment (UE). We outline a simulation framework using the open-source network simulator 3 (ns-3) and evaluate the discovery process’s performance as a function of the size of the resource pool, UE density, and the transmission probability. We establish that there exists an optimal transmission probability that minimizes discovery time for a given network configuration. However, because a UE cannot know the number of neighbors when it goes off-network, we develop a method to allow UEs to learn the optimal transmission probability during discovery. We compare our algorithm’s performance to the 3rd Generation Partnership Project standards prescribed method and others to demonstrate its superiority. [ABSTRACT FROM AUTHOR]

Low-activity signals, such as voice, electrocardiogram (ECG), and ultrasonic signals, in the Internet-of-Things applications have both posed unique challenges and offered special opportunities for modern analog-to-digital conversion. This article presents a new successive approximation register (SAR) analog-to-digital converter (ADC) search methodology, which is aimed at low-activity signals for reducing comparator activity and switching energy of digital-to-analog converter (DAC). By using statistical histogram information of the low-activity signals, two search solutions are proposed. The first solution is designed for some part of signal that has small difference between two adjacent samples, while the second solution is designed for that with large difference. To engage one suitable solution, the digital interval between two adjacent samples needs to be detected. In addition, a new DAC tactic is proposed to reduce the activity of DAC switches. Our simulated 10-bit SAR ADC for voice signals shows that by using our proposed method, the comparator activity is reduced by 62.09%, and the DAC switching energy is decreased by 85.90% compared to the monotonic method. In addition, the activity of DAC switches is further trimmed by 39.66% compared to the monotonic method. [ABSTRACT FROM AUTHOR]

This letter considers a full-duplex wireless communication system, where two users communicate to each other through an intelligent reflecting surface (IRS). Each user is assumed to be equipped with two antennas, one each for reception and transmission. The system suffers from loop-interference (LI) between the transmit and receive antennas, self-interference (SI) through the IRS, and external interferers (EI) available at the IRS and users. The fading channels are modelled by Weibull distribution as it is a generalized and accurate model for both indoor and outdoor communications. By taking into account the IRS random reflection coefficient (RRC), and by applying the Laguerre expansion approximation, we derive closed-form expressions for outage probability and ergodic capacity. Numerical and simulation results show that LI, SI, EI and RRC have sever impacts on the system performance. The results also show the robustness of the Laguerre expansion approximation compared to non-central Chi-squared distribution, in particular, for a small number of IRS elements. [ABSTRACT FROM AUTHOR]

We propose novel reconfigurable-intelligent-surface (RIS)-based energy harvesting (EH) systems with linear EH (L-EH) and non-linear EH (NL-EH) models where channels are exposed to Nakagami- $m$ fading. We also compare two RIS-based EH systems in terms of RIS locations, namely RIS-EH and RIS-information processing (RIS-IP). In the RIS-EH, RIS increases the amount of harvested energy, while in the RIS-IP, RIS is utilized to improve the information processing link. Closed-formed expressions of throughput, outage probability, and average harvested power are derived and confirmed via simulation. The L-EH model overestimates system performance, while the NL-EH model provides a realistic interpretation of EH system design. [ABSTRACT FROM AUTHOR]

This article introduces a probabilistic machine learning framework for the uncertainty quantification (UQ) of electronic circuits based on the Gaussian process regression (GPR). As opposed to classical surrogate modeling techniques, GPR inherently provides information on the model uncertainty. The main contribution of this work is twofold. First, it describes how, in a UQ scenario, the model uncertainty can be combined with the uncertainty of the input design parameters to provide confidence bounds for the statistical estimates of the system outputs, such as moments and probability distributions. These confidence bounds allow assessing the accuracy of the predicted statistics. Second, in order to deal with dynamic multioutput systems, principal component analysis (PCA) is effectively employed to compress the time-dependent output variables into a smaller set of components, for which the training of individual GPR models becomes feasible. The uncertainty on the principal components is then propagated back to the original output variables. Several application examples, ranging from a trivial RLC circuit to real-life designs, are used to illustrate and validate the advocated approach. [ABSTRACT FROM AUTHOR]

In this paper, we study the physical-layer security of a hybrid satellite-terrestrial network composed of a satellite, multiple terrestrial decode-and-forward (DF) relays and users in the presence of a terrestrial eavesdropper who attempts to tap legitimate information from both satellite and relays. We consider two different scenarios: I) the channel state information (CSI) of wiretap links is known, and II) the CSI of wiretap links is unknown. Accordingly, optimal and suboptimal relay-user pairing schemes are proposed for the aforementioned two scenarios, respectively. Specifically, a relay-user pair maximizing the secrecy rate is chosen as the optimal one to participate in the transmission, where all links’ CSI is used to perform relay-user pairing. However, suboptimal relay-user pairing scheme is designed only relying on the main links’ CSI without requiring wiretap links’ CSI, where a relay-user pair with the maximal transmission rate of main link is selected. Also, closed-form expressions of secrecy outage probability with both integer and rational fading severity parameter are derived in the case of satellite links undergo Shadowed-Rician fading and terrestrial links subject to Rayleigh fading. Furthermore, asymptotic secrecy outage probability analysis is conducted in the high signal-to-noise ratio region. Numerical results reveal that relay-user pairing is beneficial to further improve physical-layer security of the hybrid satellite-terrestrial network. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of an intelligent reflective surface (IRS) assisted non-orthogonal multiple access (NOMA) system, where the IRS is mounted on an unmanned aerial vehicle (UAV) to assist the transmissions from a base station (BS). The BS utilizes the UAV as a relay to serve multiple user equipments (UEs) on the ground. In addition to the IRS relaying links, we also incorporate direct non line-of-sight links between the BS and UEs. We derive the outage probability for this system configuration. Moreover, we obtain a bound of the ergodic spectral efficiency to extract various useful insights. Furthermore, we also compare the performance of the proposed system design against the baseline scheme. Finally, we present numerical results to highlight the benefits of the proposed IRS-aided UAV relaying system and the accuracy of the derived analytical results. [ABSTRACT FROM AUTHOR]

We study the phase synchronization problem with noisy measurements $Y=z^{*}z^{* { \mathrm {\scriptscriptstyle H} }}+\sigma W\in \mathbb {C}^{n\times n}$ , where $z^{*}$ is an $n$ -dimensional complex unit-modulus vector and $W$ is a complex-valued Gaussian random matrix. It is assumed that each entry $Y_{jk}$ is observed with probability $p$. We prove that an SDP relaxation of the MLE achieves the error bound $(1+o(1))\frac {\sigma ^{2}}{2np}$ under a normalized squared $\ell _{2}$ loss. This result matches the minimax lower bound of the problem, and even the leading constant is sharp. The analysis of the SDP is based on an equivalent non-convex programming whose solution can be characterized as a fixed point of the generalized power iteration lifted to a higher dimensional space. This viewpoint unifies the proofs of the statistical optimality of three different methods: MLE, SDP, and generalized power method. The technique is also applied to the analysis of the SDP for $\mathbb {Z}_{2}$ synchronization, and we achieve the minimax optimal error $\exp \left ({-(1-o(1))\frac {np}{2\sigma ^{2}}}\right)$ with a sharp constant in the exponent. [ABSTRACT FROM AUTHOR]

We study the problem of off-policy evaluation in the multi-armed bandit model with bounded rewards, and develop minimax rate-optimal procedures under three settings. First, when the behavior policy is known, we show that the Switch estimator, a method that alternates between the plug-in and importance sampling estimators, is minimax rate-optimal for all sample sizes. Second, when the behavior policy is unknown, we analyze performance in terms of the competitive ratio, thereby revealing a fundamental gap between the settings of known and unknown behavior policies. When the behavior policy is unknown, any estimator must have mean-squared error larger—relative to the oracle estimator equipped with the knowledge of the behavior policy— by a multiplicative factor proportional to the support size of the target policy. Moreover, we demonstrate that the plug-in approach achieves this worst-case competitive ratio up to a logarithmic factor. Third, we initiate the study of the partial knowledge setting in which it is assumed that the minimum probability taken by the behavior policy is known. We show that the plug-in estimator is optimal for relatively large values of the minimum probability, but is sub-optimal when the minimum probability is low. In order to remedy this gap, we propose a new estimator based on approximation by Chebyshev polynomials that provably achieves the optimal estimation error. Numerical experiments on both simulated and real data corroborate our theoretical findings. [ABSTRACT FROM AUTHOR]