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Motivated by applications in unmanned aerial based ground penetratingradar for detecting buried landmines, we consider the problem of imagingsmall point like scatterers situated in a lossy medium below a randomrough surface. Both the random rough surface and the absorption in thelossy medium significantly impede the target detection and imagingprocess. Using principal component analysis we effectively remove thereflection from the air-soil interface. We then use a modification ofthe classical synthetic aperture radar imaging functional to image thetargets. This imaging method introduces a user-defined parameter, δ,which scales the resolution by √δ allowing for target localization withsub wavelength accuracy. Numerical results in two dimensionsillustrateWe study imaging methods for identifying point targets in alossy medium below a random rough surface. the robustness of theapproach for imaging multiple targets. However, the depth at whichtargets are detectable is limited due to the absorption in the lossymedium.

We introduce a dispersive point target model based on scattering by aparticle in the far-field. The synthetic aperture imaging problem is thenexpanded to identify these targets and recover their locations and frequencydependent reflectivities. We show that Kirchhoff migration (KM) is able toidentify dispersive point targets in an imaging region. However, KM predictstarget locations that are shifted in range from their true locations. We derivean estimate for this range shift for a single target. We also show that becauseof this range shift we cannot recover the complex-valued frequency dependentreflectivity, but we can recover its absolute value and hence the radarcross-section (RCS) of the target. Simulation results show that we can detect,recover the approximate location, and recover the RCS for dispersive pointtargets thereby opening opportunities to classifying important differencesbetween multiple targets such as their sizes or material compositions.

This article analyzes the effects of approximate multiplication when performing inferences on deep convolutional neural networks (CNNs). The approximate multiplication can reduce the cost of the underlying circuits so that CNN inferences can be performed more efficiently in hardware accelerators. The study identifies the critical factors in the convolution, fully-connected, and batch normalization layers that allow more accurate CNN predictions despite the errors from approximate multiplication. The same factors also provide an arithmetic explanation of why bfloat16 multiplication performs well on CNNs. The experiments are performed with recognized network architectures to show that the approximate multipliers can produce predictions that are nearly as accurate as the FP32 references, without additional training. For example, the ResNet and Inception-v4 models with Mitch-ww6 multiplication produces Top-5 errors that are within 0.2 percent compared to the FP32 references. A brief cost comparison of Mitch-ww6 against bfloat16 is presented where a MAC operation saves up to 80 percent of energy compared to the bfloat16 arithmetic. The most far-reaching contribution of this article is the analytical justification that multiplications can be approximated while additions need to be exact in CNN MAC operations.

The use of one-bit analog-to-digital converters (ADCs) is a practical solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. However, the distortion caused by one-bit ADCs makes the data detection task much more challenging. In this paper, we propose a two-stage detection method for massive MIMO systems with one-bit ADCs. In the first stage, we present several linear receivers based on the Bussgang decomposition that show significant performance gains over conventional linear receivers. Next, we reformulate the maximum-likelihood (ML) detection problem to address its non-robustness. Based on the reformulated ML detection problem, we propose a model-driven deep neural network-based detector, namely OBMNet, whose performance is comparable with an existing support vector machine-based receiver, albeit with a much lower computational complexity. A nearest-neighbor search method is then proposed for the second stage to refine the first stage solution. Unlike existing search methods that typically perform the search over a large candidate set, the proposed search method generates a limited number of most likely candidates and thus limits the search complexity. Numerical results confirm the low complexity, efficiency, and robustness of the proposed two-stage detection method.

This paper investigates a symbiotic unmanned aerial vehicle (UAV)-assisted intelligent reflecting surface (IRS) radio system, where the UAV is leveraged to help the IRS reflect its own signals to the base station, and meanwhile enhance the UAV transmission by passive beamforming at the IRS. First, we consider the weighted sum bit error rate (BER) minimization problem among all IRSs by jointly optimizing the UAV trajectory, IRS phase shift matrix, and IRS scheduling, subject to the minimum primary rate requirements. To tackle this complicated problem, a relaxation-based algorithm is proposed. We prove that the converged relaxation scheduling variables are binary, which means that no reconstruct strategy is needed, and thus the UAV rate constraints are automatically satisfied. Second, we consider the fairness BER optimization problem. We find that the relaxation-based method cannot solve this fairness BER problem since the minimum primary rate requirements may not be satisfied by the binary reconstruction operation. To address this issue, we first transform the binary constraints into a series of equivalent equality constraints. Then, a penalty-based algorithm is proposed to obtain a suboptimal solution. Numerical results are provided to evaluate the performance of the proposed designs under different setups, as compared with benchmarks.

Intelligent reflecting surfaces (IRS) have been proposed as a revolutionary technology owing to its capability of adaptively reconfiguring the propagation environment in a cost-effective and hardware-efficient fashion. While the application of IRS as a passive reflector to enhance the performance of wireless communications has been widely investigated in the literature, using IRS as a passive transmitter recently is emerging as a new concept and attracting steadily growing interest. In this paper, we propose two novel IRS-based passive information transmission systems using advanced symbol-level precoding. One is a standalone passive information transmission system, where the IRS operates as a passive transmitter serving multiple receivers by adjusting its elements to reflect unmodulated carrier signals. The other is a joint passive reflection and information transmission system, where the IRS not only enhances transmissions for multiple primary information receivers (PIRs) by passive reflection, but also simultaneously delivers additional information to a secondary information receiver (SIR) by embedding its information into the primary signals at the symbol level. Two typical optimization problems, i.e., power minimization and quality-of-service (QoS) balancing, are investigated for the proposed IRS-based passive information transmission systems. Simulation results demonstrate the feasibility of IRS-based passive information transmission and the effectiveness of our proposed algorithms, as compared to other benchmark schemes.

The plug-and-play priors (PnP) and regularization by denoising (RED) methodshave become widely used for solving inverse problems by leveraging pre-traineddeep denoisers as image priors. While the empirical imaging performance and thetheoretical convergence properties of these algorithms have been widelyinvestigated, their recovery properties have not previously been theoreticallyanalyzed. We address this gap by showing how to establish theoretical recoveryguarantees for PnP/RED by assuming that the solution of these methods lies nearthe fixed-points of a deep neural network. We also present numerical resultscomparing the recovery performance of PnP/RED in compressive sensing againstthat of recent compressive sensing algorithms based on generative models. Ournumerical results suggest that PnP with a pre-trained artifact removal networkprovides significantly better results compared to the existing state-of-the-artmethods.

We introduce a set of novel multiscale basis transforms for signals on graphs that utilize their “dual” domains by incorporating the “natural” distances between graph Laplacian eigenvectors, rather than simply using the eigenvalue ordering. These basis dictionaries can be seen as generalizations of the classical Shannon wavelet packet dictionary to arbitrary graphs, and do not rely on the frequency interpretation of Laplacian eigenvalues. We describe the algorithms (involving either vector rotations or orthogonalizations) to construct these basis dictionaries, use them to efficiently approximate graph signals through the best basis search, and demonstrate the strengths of these basis dictionaries for graph signals measured on sunflower graphs and street networks.

The deployment of large-scale antenna arrays for cellular base stations (BSs), called massive MIMO, has been a key enabler for meeting the ever increasing capacity requirement for 5G communication systems and beyond. Despite their promising performance, fully digital massive MIMO systems require a large number of hardware components including radio frequency chains, power amplifiers, digital-to-analog converters (DACs), and so on, resulting in a huge increase in terms of the total power consumption and hardware costs for cellular BSs. Toward both spectrally-efficient and energy-efficient massive MIMO deployment, a number of hardware limited architectures have been proposed, including hybrid analog-digital structures, constant-envelope transmission, and the use of low-resolution DACs. In this article, we overview the recent advances in improving the error rate performance of massive MIMO systems with 1-bit DACs through precoding at the symbol level. This line of research goes beyond traditional interference suppression or cancellation techniques by managing interference on a symbol-by-symbol basis. This provides unique opportunities for interference-aware precoding tailored for practical massive MIMO systems. In this article, we first explain the concept of constructive interference (CI) and elaborate on how CI can benefit the 1-bit signal design by exploiting the traditionally undesired multi-user interference as well as the interference from imperfect hardware components. We then overview several solutions for 1-bit signal design to illustrate the gains achievable by exploiting CI. Finally, we identify some challenges and future research directions for 1-bit massive MIMO systems that have yet to be explored.

Intelligent reflecting surfaces (IRSs) have emerged as a revolutionary solution to enhance wireless communications by changing propagation environment in a cost-effective and hardware-efficient fashion. In addition, symbol-level precoding (SLP) has attracted considerable attention recently due to its advantages in converting multiuser interference (MUI) into useful signal energy. Therefore, it is of interest to investigate the employment of IRS in symbol-level precoding systems to exploit MUI in a more effective way by manipulating the multiuser channels. In this article, we focus on joint symbol-level precoding and reflecting designs in IRS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both power minimization and quality-of-service (QoS) balancing problems are considered. In order to solve the joint optimization problems, we develop an efficient iterative algorithm to decompose them into separate symbol-level precoding and block-level reflecting design problems. An efficient gradient-projection-based algorithm is utilized to design the symbol-level precoding and a Riemannian conjugate gradient (RCG)-based algorithm is employed to solve the reflecting design problem. Simulation results demonstrate the significant performance improvement introduced by the IRS and illustrate the effectiveness of our proposed algorithms.

The use of low-resolution Analog-to-Digital Converters (ADCs) is a practical solution for reducing cost and power consumption for massive Multiple-Input-Multiple-Output (MIMO) systems. However, the severe nonlinearity of low-resolution ADCs causes significant distortions in the received signals and makes the channel estimation and data detection tasks much more challenging. In this paper, we show how Support Vector Machine (SVM), a well-known supervised-learning technique in machine learning, can be exploited to provide efficient and robust channel estimation and data detection in massive MIMO systems with one-bit ADCs. First, the problem of channel estimation for uncorrelated channels is formulated as a conventional SVM problem. The objective function of this SVM problem is then modified for estimating spatially correlated channels. Next, a two-stage detection algorithm is proposed where SVM is further exploited in the first stage. The performance of the proposed data detection method is very close to that of Maximum-Likelihood (ML) data detection when the channel is perfectly known. We also propose an SVM-based joint Channel Estimation and Data Detection (CE-DD) method, which makes use of both the to-be-decoded data vectors and the pilot data vectors to improve the estimation and detection performance. Finally, an extension of the proposed methods to OFDM systems with frequency-selective fading channels is presented. Simulation results show that the proposed methods are efficient and robust, and also outperform existing ones.

We study closed-form interference-exploitation precoding for multi-level modulations in the downlink of multi-user multiple-input single-output (MU-MISO) systems. We consider two distinct cases: first, when the number of served users is not larger than the number of transmit antennas at the base station (BS), we mathematically derive the optimal precoding structure based on the Karush-Kuhn-Tucker (KKT) conditions. By formulating the dual problem, the precoding problem is transformed into a pre-scaling operation using quadratic programming (QP) optimization. We further consider the case where the number of served users is larger than the number of transmit antennas at the BS. By employing the pseudo inverse, we show that the optimal solution of the pre-scaling vector is equivalent to a linear combination of the right singular vectors corresponding to zero singular values, and derive the equivalent QP formulation. We also present the condition under which multiplexing more streams than the number of transmit antennas is achievable. For both considered scenarios, we propose a modified iterative algorithm to obtain the optimal precoding matrix, as well as a sub-optimal closed-form precoder. Numerical results validate our derivations on the optimal precoding structures for multi-level modulations, and demonstrate the superiority of interference-exploitation precoding for both scenarios.

Low-cost passive intelligent reflecting surfaces (IRSs) have recently been envisioned as a revolutionary technology capable of reconfiguring the wireless propagation environment through carefully tuning reflection elements. This paper proposes deploying an IRS to cover the dead zone of cellular multiuser full-duplex (FD) two-way communication links while suppressing user-side self-interference (SI) and co-channel interference (CI). This approach, allowing the base station (BS) and all users to exchange information simultaneously, can potentially double the spectral efficiency. To ensure network fairness, we jointly optimize the precoding matrix of the BS and the reflection coefficients of the IRS to maximize the weighted minimum rate (WMR) of all users, subject to maximum transmit power and unit-modulus constraints. We reformulate this non-convex problem and decouple it into two subproblems. Then the optimization variables in the equivalent problem are alternately optimized by adopting the block coordinate descent (BCD) algorithm. In order to further reduce the computational complexity, we propose the minorization-maximization (MM) algorithm for optimizing the precoding matrix and the reflection coefficient vector by defining minorizing functions in the surrogate problems. Finally, simulation results confirm the convergence and efficiency of our proposed algorithm, and validate the advantages of introducing IRS to improve coverage in blind areas.

The use of low-resolution Analog-to-Digital Converters (ADCs) is a practical solution for reducing cost and power consumption for massive Multiple-Input-Multiple-Output (MIMO) systems. However, the severe nonlinearity of low-resolution ADCs causes significant distortions in the received signals and makes the channel estimation and data detection tasks much more challenging. In this paper, we show how Support Vector Machine (SVM), a well-known supervised-learning technique in machine learning, can be exploited to provide efficient and robust channel estimation and data detection in massive MIMO systems with one-bit ADCs. First, the problem of channel estimation for uncorrelated channels is formulated as a conventional SVM problem. The objective function of this SVM problem is then modified for estimating spatially correlated channels. Next, a two-stage detection algorithm is proposed where SVM is further exploited in the first stage. The performance of the proposed data detection method is very close to that of Maximum-Likelihood (ML) data detection when the channel is perfectly known. We also propose an SVM-based joint Channel Estimation and Data Detection (CE-DD) method, which makes use of both the to-be-decoded data vectors and the pilot data vectors to improve the estimation and detection performance. Finally, an extension of the proposed methods to OFDM systems with frequency-selective fading channels is presented. Simulation results show that the proposed methods are efficient and robust, and also outperform existing ones.

This paper considers the secrecy performance of several schemes formulti-antenna transmission to single-antenna users with full-duplex (FD)capability against randomly distributed single-antenna eavesdroppers (EDs).These schemes and related scenarios include transmit antenna selection (TAS),transmit antenna beamforming (TAB), artificial noise (AN) from the transmitter,user selection based their distances to the transmitter, and colluding andnon-colluding EDs. The locations of randomly distributed EDs and users areassumed to be distributed as Poisson Point Process (PPP). We derive closed formexpressions for the secrecy outage probabilities (SOP) of all these schemes andscenarios. The derived expressions are useful to reveal the impacts of variousenvironmental parameters and user's choices on the SOP, and hence useful fornetwork design purposes. Examples of such numerical results are discussed.

Hybrid acoustic-RF systems offer excellent ranging accuracy, yet they typically come at a power consumption that is too high to meet the energy constraints of mobile IoT nodes. We combine pulse compression and synchronized wake-ups to achieve a ranging solution that limits the active time of the nodes to 1 ms. Hence, an ultra low-power consumption of 9.015 µW for a single measurement is achieved. The operation time is estimated on 8.5 years on a CR2032 coin cell battery at a 1 Hz update rate, which is over 250 times larger than state-of-the-art RF-based positioning systems. Measurements based on a proof-of-concept hardware platform show median distance error values below 10 cm. Both simulations and measurements demonstrate that the accuracy is reduced at low signal-to-noise ratios and when reflections occur. We introduce three methods that enhance the distance measurements at a low extra processing power cost. Hence, we validate in realistic environments that the centimeter accuracy can be obtained within the energy budget of mobile devices and IoT nodes. The proposed hybrid signal ranging system can be extended to perform accurate, low-power indoor positioning.

Reconfigurable intelligent surfaces (RISs) or intelligent reflecting surfaces(IRSs), are regarded as one of the most promising and revolutionizingtechniques for enhancing the spectrum and/or energy efficiency of wirelesssystems. These devices are capable of reconfiguring the wireless propagationenvironment by carefully tuning the phase shifts of a large number of low-costpassive reflecting elements. In this article, we aim for answering fourfundmental questions: 1) Why do we need RISs? 2) What is an RIS? 3) What areRIS's applications? 4) What are the relevant challenges and future researchdirections? In response, eight promising research directions are pointed out.

Low-resolution analog-to-digital converters (ADCs) have been considered as apractical and promising solution for reducing cost and power consumption inmassive Multiple-Input-Multiple-Output (MIMO) systems. Unfortunately,low-resolution ADCs significantly distort the received signals, and thus makedata detection much more challenging. In this paper, we develop a new deepneural network (DNN) framework for efficient and low-complexity data detectionin low-resolution massive MIMO systems. Based on reformulated maximumlikelihood detection problems, we propose two model-driven DNN-based detectors,namely OBMNet and FBMNet, for one-bit and few-bit massive MIMO systems,respectively. The proposed OBMNet and FBMNet detectors have unique and simplestructures designed for low-resolution MIMO receivers and thus can beefficiently trained and implemented. Numerical results also show that OBMNetand FBMNet significantly outperform existing detection methods.

Graph signal processing (GSP) is an important methodology for studying data residing on irregular structures. As acquired data is increasingly taking the form of multi-way tensors, new signal processing tools are needed to maximally utilize the multi-way structure within the data. In this paper, we review modern signal processing frameworks generalizing GSP to multi-way data, starting from graph signals coupled to familiar regular axes such as time in sensor networks, and then extending to general graphs across all tensor modes. This widely applicable paradigm motivates reformulating and improving upon classical problems and approaches to creatively address the challenges in tensor-based data. We synthesize common themes arising from current efforts to combine GSP with tensor analysis and highlight future directions in extending GSP to the multi-way paradigm.

A neural network (NN)-based approach for indoor localization via cellularlong-term evolution (LTE) signals is proposed. The approach estimates, from thechannel impulse response (CIR), the range between an LTE eNodeB and a receiver.A software-defined radio (SDR) extracts the CIR, which is fed to a longshort-term memory model (LSTM) recurrent neural network (RNN) to estimate therange. Experimental results are presented comparing the proposed approachagainst a baseline RNN without LSTM. The results show a receiver navigating for100 m in an indoor environment, while receiving signals from one LTE eNodeB.The ranging root-mean squared error (RMSE) and ranging maximum error along thereceiver's trajectory were reduced from 13.11 m and 55.68 m, respectively, inthe baseline RNN to 9.02 m and 27.40 m, respectively, with the proposedRNN-LSTM.

In this paper, we consider the multi-UAV deployment problem for a two-hoprelaying system. For a better network performance, UAVs carry directionalantennas that are modeled by a realistic radiation pattern. The goal is tomaximize the minimum user rates, and therefore achieve fairness in the network.We propose an iterative algorithm to optimize the TDMA scheduling in both hops,UAV trajectories, antenna beamwidths, and transmit power of the base stationand relays. Simulation results show the throughput improvement as a result ofoptimizing the directional antenna radiation patterns. In addition, we derivethe optimal power allocation, which combined with the beamwidth optimizationyields to a much better performance.

We examine the uplink spectral efficiency of a massive MIMO base station employing a one-bit Sigma-Delta ( \Sigma \Delta ) sampling scheme implemented in the spatial rather than the temporal domain. Using spatial rather than temporal oversampling, and feedback of the quantization error between adjacent antennas, the method shapes the spatial spectrum of the quantization noise away from an angular sector where the signals of interest are assumed to lie. It is shown that, while a direct Bussgang analysis of the \Sigma \Delta approach is not suitable, an alternative equivalent linear model can be formulated to facilitate an analysis of the system performance. The theoretical properties of the spatial quantization noise power spectrum are derived for the \Sigma \Delta array, as well as an expression for the spectral efficiency of maximum ratio combining (MRC). Simulations verify the theoretical results and illustrate the significant performance gains offered by the \Sigma \Delta approach for both MRC and zero-forcing receivers.

In this paper, we investigate the unmanned aerial vehicle (UAV)-Aided simultaneous uplink and downlink transmission networks, where one UAV acting as a disseminator is connected to multiple access points (AP), and the other UAV acting as a base station (BS) collects data from numerous sensor nodes (SNs). The goal of this paper is to maximize the system throughput by jointly optimizing the 3D UAV trajectory, communication scheduling, and UAV-AP/SN transmit power. We first consider a special case where the UAV-BS and UAV-AP trajectories are pre-determined. Although the resulting problem is an integer and non-convex optimization problem, a globally optimal solution is obtained by applying the polyblock outer approximation (POA) method based on the problem's hidden monotonic structure. Subsequently, for the general case considering the 3D UAV trajectory optimization, an efficient iterative algorithm is proposed to alternately optimize the divided sub-problems based on the successive convex approximation (SCA) technique. Numerical results demonstrate that the proposed design is able to achieve significant system throughput gain over the benchmarks. In addition, the SCA-based method can achieve nearly the same performance as the POA-based method with much lower computational complexity.

Wide area networking infrastructures (WANs), particularly science andresearch WANs, are the backbone for moving large volumes of scientific databetween experimental facilities and data centers. With demands growing atexponential rates, these networks are struggling to cope with large datavolumes, real-time responses, and overall network performance. Networkoperators are increasingly looking for innovative ways to manage the limitedunderlying network resources. Forecasting network traffic is a criticalcapability for proactive resource management, congestion mitigation, anddedicated transfer provisioning. To this end, we propose a nonautoregressivegraph-based neural network for multistep network traffic forecasting.Specifically, we develop a dynamic variant of diffusion convolutional recurrentneural networks to forecast traffic in research WANs. We evaluate the efficacyof our approach on real traffic from ESnet, the U.S. Department of Energy'sdedicated science network. Our results show that compared to classicalforecasting methods, our approach explicitly learns the dynamic nature ofspatiotemporal traffic patterns, showing significant improvements inforecasting accuracy. Our technique can surpass existing statistical and deeplearning approaches by achieving approximately 20% mean absolute percentageerror for multiple hours of forecasts despite dynamic network traffic settings.

Recently, analog compute-in-memory (CIM) architectures based on emerginganalog non-volatile memory (NVM) technologies have been explored for deepneural networks (DNN) to improve energy efficiency. Such architectures,however, leverage charge conservation, an operation with infinite resolution,and thus are susceptible to errors. The computations in DNN realized by analogNVM thus have high uncertainty due to the device stochasticity. Several reportshave demonstrated the use of analog NVM for CIM in a limited scale. It isunclear whether the uncertainties in computations will prohibit large-scaleDNNs. To explore this critical issue of scalability, this paper first presentsa simulation framework to evaluate the feasibility of large-scale DNNs based onCIM architecture and analog NVM. Simulation results show that DNNs trained forhigh-precision digital computing engines are not resilient against theuncertainty of the analog NVM devices. To avoid such catastrophic failures,this paper introduces the analog floating-point representation for the DNN, andthe Hessian-Aware Stochastic Gradient Descent (HA-SGD) training algorithm toenhance the inference accuracy of trained DNNs. As a result of suchenhancements, DNNs such as Wide ResNets for the CIFAR-100 image recognitionproblem are demonstrated to have significant performance improvements inaccuracy without adding cost to the inference hardware.

The charging load from Electric vehicles (EVs) is modeled as deferrable load, meaning that the power consumption can be shifted to different time windows to achieve various grid objectives. In local community scenarios, EVs are considered as controllable storage devices in a global optimization problem together with other microgrid components, such as the building load, renewable generations, and battery energy storage system, etc. Various objectives have been investigated before, such as following a given load profile, reducing load fluctuation and minimizing the total operational cost. However, the uncertainties inside the behaviors of individual drivers have tremendous impact on the cost effectiveness of microgrid operations, which have not been carefully studied. In this paper, we propose a predictive management strategy in a community microgrid, using datasets of system base load, solar generation and EV charging behaviors from real-world cases. Two stage operations are modeled for cost-effective EV management, i.e. wholesale market partition in the first stage and load profile following in the second stage. Predictive control strategies, including receding horizon control, are adapted to solve the energy allocation problem in a decentralized fashion. The experimental results indicate the proposed approach can considerably reduce the total energy cost and decrease the ramping index of total system load up to 56.3%.

The charging load from Electric vehicles (EVs) is modeled as deferrable load, meaning that the power consumption can be shifted to different time windows to achieve various grid objectives. In local community scenarios, EVs are considered as controllable storage devices in a global optimization problem together with other microgrid components, such as the building load, renewable generations, and battery energy storage system, etc. Various objectives have been investigated before, such as following a given load profile, reducing load fluctuation and minimizing the total operational cost. However, the uncertainties inside the behaviors of individual drivers have tremendous impact on the cost effectiveness of microgrid operations, which have not been carefully studied. In this paper, we propose a predictive management strategy in a community microgrid, using datasets of system base load, solar generation and EV charging behaviors from real-world cases. Two stage operations are modeled for cost-effective EV management, i.e. wholesale market partition in the first stage and load profile following in the second stage. Predictive control strategies, including receding horizon control, are adapted to solve the energy allocation problem in a decentralized fashion. The experimental results indicate the proposed approach can considerably reduce the total energy cost and decrease the ramping index of total system load up to 56.3%.

The one-bit spatial Sigma-Delta concept has recently been proposed as an approach for achieving low distortion and low power consumption for massive multi-input multi-output systems. The approach exploits users located in known angular sectors or spatial oversampling to shape the quantization noise away from desired directions of arrival. While reducing the antenna spacing alleviates the adverse impact of quantization noise, it can potentially deteriorate the performance of the massive array due to excessive mutual coupling. In this paper, we analyze the impact of mutual coupling on the uplink spectral efficiency of a spatial one-bit Sigma-Delta massive MIMO architecture, and compare the resulting performance degradation to standard one-bit quantization as well as the ideal case with infinite precision. Our simulations show that the one-bit Sigma-Delta array is particularly advantageous in space-constrained scenarios, can still provide significant gains even in the presence of mutual coupling when the antennas are closely spaced.

The ability to detect sparse signals from noisy, high-dimensional data is a top priority in modern science and engineering. It is well known that a sparse solution of the linear system [Formula: see text] can be found efficiently with an [Formula: see text]-norm minimization approach if the data are noiseless. However, detection of the signal from data corrupted by noise is still a challenging problem as the solution depends, in general, on a regularization parameter with optimal value that is not easy to choose. We propose an efficient approach that does not require any parameter estimation. We introduce a no-phantom weight τ and the Noise Collector matrix C and solve an augmented system [Formula: see text], where e is the noise. We show that the [Formula: see text]-norm minimal solution of this system has zero false discovery rate for any level of noise, with probability that tends to one as the dimension of [Formula: see text] increases to infinity. We obtain exact support recovery if the noise is not too large and develop a fast Noise Collector algorithm, which makes the computational cost of solving the augmented system comparable with that of the original one. We demonstrate the effectiveness of the method in applications to passive array imaging.

Recent advances in Internet of Things (IoT) technologies have enabled the use of wearables for remote patient monitoring. Wearable sensors capture the patient’s vital signs, and provide alerts or diagnosis based on the collected data. Unfortunately, wearables typically have limited energy and computational capacity, making their use challenging for healthcare applications where monitoring must continue uninterrupted for a long time, without the need to charge or change the battery. Fog computing can alleviate this problem by offloading computationally intensive tasks from the sensor layer to higher layers, thereby not only meeting the sensors’ limited computational capacity but also enabling the use of local closed-loop energy optimization algorithms to increase the battery life. Furthermore, the patient’s contextual information - including health and activity status - can be exploited to guide energy optimization algorithms more effectively. By incorporating the patient’s contextual information, a desired quality of experience can be achieved by creating a dynamic balance between energy-efficiency and measurement accuracy. We present a runtime distributed control-based solution to find the most energy-efficient system state for a given context while keeping the accuracy of the decision-making process over a certain threshold. Our optimization algorithm resides in the fog layer to avoid imposing computational overheads to the sensor layer. Our solution can be extended to reduce the probability of errors in the data collection process to ensure the accuracy of the results. The implementation of our fog-assisted control solution on a remote monitoring system shows a significant improvement in energy efficiency with a bounded loss in accuracy.

The stress detection problem is receiving great attention in related researchcommunities. This is due to its essential part in behavioral studies for manyserious health problems and physical illnesses. There are different methods andalgorithms for stress detection using different physiological signals. Previousstudies have already shown that Galvanic Skin Response (GSR), also known asElectrodermal Activity (EDA), is one of the leading indicators for stress.However, the GSR signal itself is not trivial to analyze. Different featuresare extracted from GSR signals to detect stress in people like the number ofpeaks, max peak amplitude, etc. In this paper, we are proposing an open-sourcetool for GSR analysis, which uses deep learning algorithms alongsidestatistical algorithms to extract GSR features for stress detection. Then weuse different machine learning algorithms and Wearable Stress and AffectDetection (WESAD) dataset to evaluate our results. The results show that we arecapable of detecting stress with the accuracy of 92 percent using 10-foldcross-validation and using the features extracted from our tool.

The one-bit spatial Sigma-Delta concept has recently been proposed as an approach for achieving low distortion and low power consumption for massive multi-input multi-output systems. The approach exploits users located in known angular sectors or spatial oversampling to shape the quantization noise away from desired directions of arrival. While reducing the antenna spacing alleviates the adverse impact of quantization noise, it can potentially deteriorate the performance of the massive array due to excessive mutual coupling. In this paper, we analyze the impact of mutual coupling on the uplink spectral efficiency of a spatial one-bit Sigma-Delta massive MIMO architecture, and compare the resulting performance degradation to standard one-bit quantization as well as the ideal case with infinite precision. Our simulations show that the one-bit Sigma-Delta array is particularly advantageous in space-constrained scenarios, can still provide significant gains even in the presence of mutual coupling when the antennas are closely spaced.

Recent advances in Internet of Things (IoT) technologies have enabled the use of wearables for remote patient monitoring. Wearable sensors capture the patient’s vital signs, and provide alerts or diagnosis based on the collected data. Unfortunately, wearables typically have limited energy and computational capacity, making their use challenging for healthcare applications where monitoring must continue uninterrupted for a long time, without the need to charge or change the battery. Fog computing can alleviate this problem by offloading computationally intensive tasks from the sensor layer to higher layers, thereby not only meeting the sensors’ limited computational capacity but also enabling the use of local closed-loop energy optimization algorithms to increase the battery life. Furthermore, the patient’s contextual information - including health and activity status - can be exploited to guide energy optimization algorithms more effectively. By incorporating the patient’s contextual information, a desired quality of experience can be achieved by creating a dynamic balance between energy-efficiency and measurement accuracy. We present a runtime distributed control-based solution to find the most energy-efficient system state for a given context while keeping the accuracy of the decision-making process over a certain threshold. Our optimization algorithm resides in the fog layer to avoid imposing computational overheads to the sensor layer. Our solution can be extended to reduce the probability of errors in the data collection process to ensure the accuracy of the results. The implementation of our fog-assisted control solution on a remote monitoring system shows a significant improvement in energy efficiency with a bounded loss in accuracy.

The use of low-resolution analog-to-digital converters (ADCs) is considered to be an effective technique to reduce the power consumption and hardware complexity of wireless transceivers. However, in systems with low-resolution ADCs, obtaining channel state information (CSI) is difficult due to significant distortions in the received signals. The primary motivation of this paper is to show that learning techniques can mitigate the impact of CSI unavailability. We study the blind detection problem in multiple-input-multiple-output (MIMO) systems with low-resolution ADCs using learning approaches. Two methods, which employ a sequence of pilot symbol vectors as the initial training data, are proposed. The first method exploits the use of a cyclic redundancy check (CRC) to obtain more training data, which helps improve the detection accuracy. The second method is based on the perspective that the to-be-decoded data can itself assist the learning process, so no further training information is required except the pilot sequence. For the case of 1-bit ADCs, we provide a performance analysis of the vector error rate for the proposed methods. Based on the analytical results, a criterion for designing transmitted signals is also presented. Simulation results show that the proposed methods outperform existing techniques and are also more robust.

In this letter, we propose a trellis-coded non-orthogonal multiple access (NOMA) scheme. The signals for different users are produced by trellis coded modulation (TCM) and then superimposed on different power levels. By interpreting the encoding process via the tensor product of trellises, we introduce a joint detection method based on the Viterbi algorithm. Then, we determine the optimal power allocation between the two users by maximizing the free distance of the tensor product trellis. Finally, we manifest that the trellis-coded NOMA outperforms the uncoded NOMA at high signal-to-noise ratio (SNR).

In this paper, we consider the amplify-and-forward relay networks in mmWave systems and propose a hybrid precoder/combiner design approach. The phase-only RF precoding/combining matrices are first designed to support multi-stream transmission, where we compensate the phase for the eigenmodes of the channel. Then, the baseband precoders/combiners are performed to achieve the maximum mutual information. Based on the data processing inequality for the mutual information, we first jointly design the baseband source and relay nodes to maximize the mutual information before the destination baseband receiver. The proposed low-complexity iterative algorithm for the source and relay nodes is based on the equivalence between the mutual information maximization and the weighted MMSE. After we obtain the optimal precoder and combiner for the source and relay nodes, we implement the MMSE-SIC filter at the baseband receiver to keep the mutual information unchanged, thus obtaining the optimal mutual information for the whole relay system. Simulation results show that our algorithm achieves better performance with lower complexity compared with other algorithms in the literature. In addition, we also propose a robust joint transceiver design for imperfect channel state information.

This paper investigates unmanned aerial vehicle (UAV)-aided backscatter communication (BackCom) networks, where the UAV is leveraged to help the backscatter device (BD) forward signals to the receiver. Based on the presence or absence of a direct link between BD and receiver, two protocols, namely transmit-backscatter (TB) protocol and transmit-backscatter-relay (TBR) protocol, are proposed to utilize the UAV to assist the BD. In particular, we formulate the system throughput maximization problems for the two protocols by jointly optimizing the time allocation, reflection coefficient and UAV trajectory. Different static/dynamic circuit power consumption models for the two protocols are analyzed. The resulting optimization problems are shown to be non-convex, which are challenging to solve. We first consider the dynamic circuit power consumption model, and decompose the original problems into three sub-problems, namely time allocation optimization with fixed UAV trajectory and reflection coefficient, reflection coefficient optimization with fixed UAV trajectory and time allocation, and UAV trajectory optimization with fixed reflection coefficient and time allocation. Then, an efficient iterative algorithm is proposed for both protocols by leveraging the block coordinate descent method and successive convex approximation (SCA) techniques. In addition, for the static circuit power consumption model, we obtain the optimal time allocation with a given reflection coefficient and UAV trajectory and the optimal reflection coefficient with low computational complexity by using the Lagrangian dual method. Simulation results show that the proposed protocols are able to achieve significant throughput gains over the compared benchmarks.

A central problem in analog wireless sensor networks is to design the gain or phase-shifts of the sensor nodes (i.e. the relaying configuration) in order to achieve an accurate estimation of some parameter of interest at a fusion center, or more generally, at each node by employing a distributed parameter estimation scheme. In this paper, by using an over-parametrization of the original design problem, we devise a cyclic optimization approach that can handle tuning both gains and phase-shifts of the sensor nodes, even in intricate scenarios involving sensor selection or discrete phase-shifts. Each iteration of the proposed design framework consists of a combination of the Gram-Schmidt process and power method-like iterations, and as a result, enjoys a low computational cost. Along with formulating the design problem for a fusion center, we further present a consensus-based framework for decentralized estimation of deterministic parameters in a distributed network, which results in a similar sensor gain design problem. The numerical results confirm the computational advantage of the suggested approach in comparison with the state-of-the-art methods-an advantage that becomes more pronounced when the sensor network grows large.

We analyze synthetic aperture radar (SAR) imaging of complex ground scenesthat contain both stationary and moving targets. In the usual SAR acquisitionscheme, we consider ways to preprocess the data so as to separate thecontributions of the moving targets from those due to stationary backgroundreflectors. Both components of the data, that is, reflections from stationaryand moving targets, are considered as signal that is needed for target imagingand tracking, respectively. The approach we use is to decompose the data matrixinto a low rank and a sparse part. This decomposition enables us to capture thereflections from moving targets into the sparse part and those from stationarytargets into the low rank part of the data. The computational tool for this isrobust principal component analysis (RPCA) applied to the SAR data matrix. Wealso introduce a lossless baseband transformation of the data, which simplifiesthe analysis and improves the performance of the RPCA algorithm. Our maincontribution is a theoretical analysis that determines an optimal choice ofparameters for the RPCA algorithm so as to have an effective and stableseparation of SAR data coming from moving and stationary targets. This analysisgives also a lower bound for detectable target velocities. We show inparticular that the rank of the sparse matrix is proportional to the squareroot of the target's speed in the direction that connects the SAR platformtrajectory to the imaging region. The robustness of the approach is illustratedwith numerical simulations in the X-band SAR regime.

An integrity-based path planning strategy for autonomous ground vehicle (AGV)navigation in urban environments is developed. The vehicle is assumed tonavigate by utilizing cellular long-term evolution (LTE) signals in addition toGlobal Positioning System (GPS) signals. Given a desired destination, anoptimal path is calculated, which minimizes a cost function that considers boththe horizontal protection level (HPL) and travel distance. The constraints arethat (i) the ratio of nodes with faulty signals to the total nodes be lowerthan a maximum allowable ratio and (ii) the HPLs along each candidate path belower than the horizontal alert limit (HAL). To predict the faults and HPLbefore the vehicle is driven, GPS and LTE pseudoranges along the candidatepaths are generated utilizing a commercial ray-tracing software andthree-dimensional (3D) terrain and building maps. Simulated pseudoranges informthe path planning algorithm about potential biases due to reflections frombuildings in urban environments. Simulation results are presented showing thatthe optimal path produced by the proposed path planning strategy has theminimum average HPL among the candidate paths.

Anti-eavesdropping channel estimation (ANECE) is a method that uses speciallydesigned pilot signals to allow two or more full-duplex radio devices each withone or more antennas to estimate their channel state information (CSI)consistently and at the same time prevent eavesdropper (Eve) with any number ofantennas from obtaining its CSI consistently. This paper presents optimaldesigns of the pilots for ANECE based on two criteria. The first is the meansquared error (MSE) of channel estimation for the users, and the second is themutual information (MI) between the pilot-driven signals observed by the users.Closed-form optimal pilots are shown under the sum-MSE and sum-MI criteriasubject to a symmetric and isotropic condition. Algorithms for computing theoptimal pilots are shown for general cases. Fairness issues for three or moreusers are discussed. The performances of different designs are compared.

A central problem in analog wireless sensor networks is to design the gain or phase-shifts of the sensor nodes (i.e. the relaying configuration) in order to achieve an accurate estimation of some parameter of interest at a fusion center, or more generally, at each node by employing a distributed parameter estimation scheme. In this paper, by using an over-parametrization of the original design problem, we devise a cyclic optimization approach that can handle tuning both gains and phase-shifts of the sensor nodes, even in intricate scenarios involving sensor selection or discrete phase-shifts. Each iteration of the proposed design framework consists of a combination of the Gram-Schmidt process and power method-like iterations, and as a result, enjoys a low computational cost. Along with formulating the design problem for a fusion center, we further present a consensus-based framework for decentralized estimation of deterministic parameters in a distributed network, which results in a similar sensor gain design problem. The numerical results confirm the computational advantage of the suggested approach in comparison with the state-of-the-art methods-an advantage that becomes more pronounced when the sensor network grows large.

This article addresses the analysis and design of nonreciprocal filters based on time-modulated resonators. We analytically show that time modulating a resonator leads to a set of harmonic resonators composed of the unmodulated lumped elements plus a frequency-invariant element that accounts for differences in the resonant frequencies. We then demonstrate that harmonic resonators of different orders are coupled through nonreciprocal admittance inverters, whereas harmonic resonators of the same order couple with the admittance inverter coming from the unmodulated filter network. This coupling topology provides useful insights to understand and quickly design nonreciprocal filters and permits their characterization using an asynchronously tuned coupled resonators network together with the coupling matrix formalism. Two designed filters, of orders three and four, are experimentally demonstrated using quarter wavelength resonators implemented in microstrip technology and terminated by a varactor on one side. The varactors are biased using coplanar waveguides integrated into the ground plane of the device. Measured results are found to be in good agreement with numerical results, validating the proposed theory.

The electroencephalogram (EEG) provides a non-invasive, minimally restrictive, and relatively low-cost measure of mesoscale brain dynamics with high temporal resolution. Although signals recorded in parallel by multiple, near-adjacent EEG scalp electrode channels are highly-correlated and combine signals from many different sources, biological and non-biological, independent component analysis (ICA) has been shown to isolate the various source generator processes underlying those recordings. Independent components (IC) found by ICA decomposition can be manually inspected, selected, and interpreted, but doing so requires both time and practice as ICs have no order or intrinsic interpretations and therefore require further study of their properties. Alternatively, sufficiently-accurate automated IC classifiers can be used to classify ICs into broad source categories, speeding the analysis of EEG studies with many subjects and enabling the use of ICA decomposition in near-real-time applications. While many such classifiers have been proposed recently, this work presents the ICLabel project comprised of (1) the ICLabel dataset containing spatiotemporal measures for over 200,000 ICs from more than 6000 EEG recordings and matching component labels for over 6000 of those ICs, all using common average reference, (2) the ICLabel website for collecting crowdsourced IC labels and educating EEG researchers and practitioners about IC interpretation, and (3) the automated ICLabel classifier, freely available for MATLAB. The ICLabel classifier improves upon existing methods in two ways: by improving the accuracy of the computed label estimates and by enhancing its computational efficiency. The classifier outperforms or performs comparably to the previous best publicly available automated IC component classification method for all measured IC categories while computing those labels ten times faster than that classifier as shown by a systematic comparison against other publicly available EEG IC classifiers.

We consider channel estimation for an uplink massive multiple input multiple output (MIMO) system where the base station (BS) uses a first-order spatial Sigma-Delta (Σ) analogto-digital converter (ADC) array. The Σ array consists of closely spaced sensors which oversample the received signal and provide a coarsely quantized (1-bit) output. We develop a linear minimum mean squared error (LMMSE) estimator based on the Bussgang decomposition that reformulates the nonlinear quantizer model using an equivalent linear model plus quantization noise. The performance of the proposed Σ LMMSE estimator is compared via simulation to channel estimation using standard 1-bit quantization and also infinite resolution ADCs.

State-of-the-art drone technologies have severe flight time limitations due to weight constraints, which inevitably lead to a relatively small amount of available energy. Therefore, frequent battery replacement or recharging is necessary in applications such as delivery, exploration, or support to the wireless infrastructure. Mobile charging stations (i.e., mobile stations with charging equipment) for outdoor ad-hoc battery charging is one of the feasible solutions to address this issue. However, the ability of these platforms to charge the drones is limited in terms of the number and charging time. This paper designs an auction-based mechanism to control the charging schedule in multi-drone setting. In this paper, charging time slots are auctioned, and their assignment is determined by a bidding process. The main challenge in developing this framework is the lack of prior knowledge on the distribution of the number of drones participating in the auction. Based on optimal second-price-auction, the proposed formulation, then, relies on deep learning algorithms to learn such distribution online. Numerical results from extensive simulations show that the proposed deep-learning-based approach provides effective battery charging control in multi-drone scenarios.

There is often latent network structure in spatial and temporal data and thetools of network analysis can yield fascinating insights into such data. Inthis paper, we develop a nonparametric method for network reconstruction fromspatiotemporal data sets using multivariate Hawkes processes. In contrast toprior work on network reconstruction with point-process models, which has oftenfocused on exclusively temporal information, our approach uses both temporaland spatial information and does not assume a specific parametric form ofnetwork dynamics. This leads to an effective way of recovering an underlyingnetwork. We illustrate our approach using both synthetic networks and networksconstructed from real-world data sets (a location-based social media network, anarrative of crime events, and violent gang crimes). Our results demonstratethat, in comparison to using only temporal data, our spatiotemporal approachyields improved network reconstruction, providing a basis for meaningfulsubsequent analysis --- such as community structure and motif analysis --- ofthe reconstructed networks.

This paper presents secrecy analyses of a full-duplex MIMOME network whichconsists of two full-duplex multi-antenna users (Alice and Bob) and anarbitrarily located multi-antenna eavesdropper (Eve). The paper assumes thatEve's channel state information (CSI) is completely unknown to Alice and Bobexcept for a small radius of secured zone. The first part of this paper aims tooptimize the powers of jamming noises from both users. To handle Eve's CSIbeing unknown to users, the focus is placed on Eve at the most harmfullocation, and the large matrix theory is applied to yield a hardened secrecyrate to work on. The performance gain of the power optimization in terms ofmaximum tolerable number of antennas on Eve is shown to be significant. Thesecond part of this paper shows two analyses of anti-eavesdropping channelestimation (ANECE) that can better handle Eve with any number of antennas. Oneanalysis assumes that Eve has a prior statistical knowledge of its CSI, whichyields lower and upper bounds on secure degrees of freedom of the system asfunctions of the number (N) of antennas on Eve and the size (K) of informationpacket. The second analysis assumes that Eve does not have any prior knowledgeof its CSI but performs blind detection of information, which yields anapproximate secrecy rate for the case of K being larger than N.

In this paper, we focus on the multiuser massive multiple-input single-output (MISO) downlink with low-cost 1-bit digital-to-analog converters (DACs) for PSK modulation, and propose a low-complexity refinement process that is applicable to any existing 1-bit precoding approaches based on the constructive interference (CI) formulation. With the decomposition of the signals along the detection thresholds, we first formulate a simple symbol-scaling method as the performance metric. The low-complexity refinement approach is subsequently introduced, where we aim to improve the introduced symbol-scaling performance metric by modifying the transmit signal on one antenna at a time. Numerical results validate the effectiveness of the proposed refinement method on existing approaches for massive MIMO with 1-bit DACs, and the performance improvements are most significant for the low-complexity quantized zero-forcing (ZF) method.