With the introduction of spectrum sharing and het-erogeneous services in next-generation networks, the base stations need to sense the wideband spectrum and identify the spectrum resources to meet the quality-of-service, bandwidth, and latency constraints. Sub-Nyquist sampling (SNS) enables digitization for sparse wideband spectrum without needing Nyquist speed analog-to-digital converters. However, SNS demands additional signal processing algorithms for spectrum reconstruction, such as the well-known orthogonal matching pursuit (OMP) algorithm. OMP is also widely used in other compressed sensing applications. The first contribution of this work is efficiently mapping the OMP algorithm on the Zynq system-on-chip (ZSoC) consisting of an ARM processor and FPGA. Experimental analysis shows a significant degradation in OMP performance for sparse spectrum. Also, OMP needs prior knowledge of spectrum sparsity. We address these challenges via deep-learning-based architectures and efficiently map them on the ZSoC platform as second contribution. Via hardware-software co-design, different versions of the proposed architecture obtained by partitioning between software (ARM processor) and hardware (FPGA) are considered. The resource, power, and execution time comparisons for given memory constraints and a wide range of word lengths are presented for these architectures.

This is the pre-review draft of the first of two papers submitted to Journal of Lightwave Technology, where we propose and apply a methodology for the analysis of the loss in tube lattice fibers (TLFs) based on the azimuthal Fourier decomposition (AFD) of the fiber’s modes along the perimeters of the tubes composing the fiber cladding. This technique combined with the coupled mode theory constitutes an effective approach for gaining insight in the waveguiding mechanism and for additional loss analysis due to fiber non-idealities. In this part I we describe the approach and apply it to loss analysis of ideal TLFs. The approach is then applied to the analysis of tube thickness variation effects in the part II.

In Part I of this work, we presented a three-stage framework for solving stochastic non-convex optimal power flow problems using a parameterized deterministic look-ahead policy and minimizing the cost of scheduling flexibility reserves using blockchain and smart contracts. Here, in Part II, a case study with two balancing authority (BA) areas is presented to show the effectiveness of the proposed algorithm. The BA area is modeled by the collection of generation, transmission, and distribution networks. Next, the reserve sharing strategy between two or more BA areas is presented to collectively maintain and supply the flexibility reserves. The reserve sharing will help reduce the total flexibility reserve scheduling costs in the BA areas. And finally, the advantage of updating the flexibility reserve schedule at each time step using a smart contract, by tabulating the flexibility metrics “variation in generation capacity (VIGC)” and uncertainty quantification of epistemic and aleatory uncertainties in variable distributed energy resources (DERs) generation and loads, is also presented.

The massive integration of power electronic converters into the power grid has led to a decrease in the mechanical inertia of the power system, causing an increase in the rate of change of frequency (RoCoF) that may lead to stability problems. The scientific community has focused on developing grid-forming control techniques, although their implementation implies a significant change in the firmware of the converter. Grid-supporting approaches, on the other hand, are an interesting alternative to add frequency support to the grid while preserving the original control structure of the converter. This paper proposes three new grid-supporting control techniques based on the dynamic behaviour of a synchronous machine (SM) and its equivalence with a second-order low-pass filter. They endow the converter with the capability of providing synthetic inertia, damping, droop-based p/f primary response and virtual reactance. The dynamics of the proposed implementations are compared with those of a reduced-order synchronous machine by means of time-domain simulations and in-depth state-space based small signal analyses. Besides, their operation is validated in a nine-bus low-inertia power system. Hardware in the loop (HIL) laboratory results are used to validate experimentally the proposed techniques.

In our work, we developed a pattern-producing neural network that we named Transitional Pattern Producing Networks (TPPN). The TPPN generates soft robot morphologies much faster than existing methods. Our TPPN architecture uses only sine activation function with trainable frequency and phase. It uses a Boolean mask that turns neural connections on/off during evolution. In addition to running much faster than existing methods, the TPPN generates morphologies resembling biological patterns such as bilateral symmetry and smoothly transitions between patterns during successive evolutionary runs. The ability to smoothly transition between patterns during successive generations has been a major challenge for existing pattern-producing models, especially in robotics.

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Increasing renewable energy penetrations bring complex feasibility and stability problems. Data-driven methods are applied in extracting and embedding these feasibility and stability rules in power system operations and planning. This paper presents a method of alternate support vector machine decision trees for rule extraction problems. The method has significant improvements to the classical decision-tree-based algorithms in terms of efficiency, stability and versatility. Finally, we apply the method to several power and energy system scenarios to show its effectiveness.

In this work, we optimize the 3D trajectory of an unmanned aerial vehicle (UAV)-based portable access point (PAP) that provides wireless services to a set of ground nodes (GNs).  Moreover, as per the Peukert effect, we consider pragmatic non-linear battery discharge for UAV’s battery. Thus, we formulate the problem in a novel manner that represents the maximization of a fairness-based energy efficiency metric and is named fair energy efficiency (FEE). The FEE metric defines a system that lays importance on both the per-user service fairness and the PAP’s energy efficiency. The formulated problem takes the form of a non-convex problem with non-tractable constraints. To obtain a solution we represent the problem as a Markov Decision Process (MDP) with continuous state and action spaces. Considering the complexity of the solution space, we use the twin delayed deep deterministic policy gradient (TD3) actor-critic deep reinforcement learning (DRL) framework to learn a policy that maximizes the FEE of the system. We perform two types of RL training to exhibit the effectiveness of our approach: the first (offline) approach keeps the positions of the GNs the same throughout the training phase; the second approach generalizes the learned policy to any arrangement of GNs by changing the positions of GNs after each training episode. Numerical evaluations show that neglecting the Peukert effect overestimates the air-time of the PAP and can be addressed by optimally selecting the PAP’s flying speed. Moreover, the user fairness, energy efficiency, and hence the FEE value of the system can be improved by efficiently moving the PAP above the GNs. \color{black}As such, we notice massive FEE improvements over baseline scenarios of up to 88.31%, 272.34%, and 318.13% for suburban, urban, and dense urban environments, respectively.

In networks consisting of agents communicating with a central coordinator and working together to solve a global optimization problem in a distributed manner, the agents are often required to solve private proximal minimization subproblems. Such a setting often requires a decomposition method to solve the global distributed problem, resulting in extensive communication overhead. In networks where communication is expensive, it is crucial to reduce the communication overhead of the distributed optimization scheme. Gaussian processes (GPs) are effective at learning the agents’ local proximal operators, thereby reducing the communication between the agents and the coordinator. We propose combining this learning method with adaptive uniform quantization for a hybrid approach that can achieve further communication reduction. In our approach, the GP algorithm is modified to account for the introduced quantization noise statistics due to data quantization. We further improve our approach by introducing an orthogonalization process to the quantizer’s input to address the inherent correlation of the input components. We also use dithering to ensure uncorrelation between the quantizer’s introduced noise and its input. We propose multiple measures to quantify the trade-off between the communication cost reduction and the optimization solution’s accuracy/optimality. Under such metrics, our proposed algorithms can achieve significant communication reduction for distributed optimization with acceptable accuracy, even at low quantization resolutions. This result is demonstrated by simulations of a distributed sharing problem with quadratic cost functions for the agents.

The integration of power electronic converter (PEC)-based renewable distributed energy resources (DERs) and controllable loads in electric power grids is growing exponentially due to increased interest in reducing carbon emissions. However, the uncertainty in DER power output limits their contribution to the overall system reliability and alleviates its flexibility. Hence, three-stage power flow and flexibility reserve optimization are proposed to address these issues. In the first stage, the epistemic and aleatory uncertainties in the power flow problem are addressed by the proposed parameterized deterministic lookahead (DLA) method. The flexibility metric “variation in generation capacity (VIGC)” and sharing alternating direction method of multipliers (S-ADMM) based distributed optimization is used to schedule flexibility reserves and minimize its cost in the second and the third stage to enhance system flexibility. The flexibility reserve sharing and blockchain/smart contract-based bidding to schedule those reserves for multiple balancing authority (BA) areas are also proposed in this work. The proposed algorithm’s results and e?ectiveness are presented in Part II of this work.

Reconfigurable intelligent surfaces (RISs) are a recent yet revolutionary development in communications systems. Particularly applicable to millileter wave (mmWave) systems, these surfaces can increase localization performance and decrease vulnerability to environmental influences, all by  adjusting the incoming signals’ phase. At the same time, manufacturing ideal hardware to be deployed at the transceivers is not feasible nor practical. These non-linearities in hardware, collectively known hardware impairments (HWIs), cause signal degradation and adversely affect localization. In this paper, the effect of HWIs on RIS-aided localization is examined. Towards that, the mean squared error (MSE) of the user’s position is found through a maximum likelihood estimator (MLE) and its functionality is verified by the position error bounds (PEB),  derived from Cramer-Rao lower bounds (CRLB). Our numerical results show that active RISs mitigate the deteriorating effect of HWIs on the user’s PEB. Based on our outcome, increasing the inter-RISs space generally creates more resolvable paths and leads to improved localization.

Abstract—Estimation of individual treatment effect (ITE) for different types of treatment is a common challenge in therapy assessments, clinical trials and diagnosis. Deep learning methods, namely representation based, adversarial, and variational, have shown promising potential in ITE estimation. However, it was unclear whether the hyperparameters of the originally proposed methods were well optimized for different benchmark datasets. To solve these problems, we created a public code library containing representation-based, adversarial, and variational methods written in TensorFlow. In order to have a broader collection of ITE estimation methods, we have also included neural network based meta-learners. The code library is made accessible for reproducibility and facilitating future works in the field of causal inference. Our results demonstrate that performance of most methods can be improved using automatic hyperparameter optimization. Additionally, we review the methods and compare the performance of the optimized models from our library on publicly available datasets. The potential of hyperparameter optimization may encourage researchers to focus on this aspect when creating new methods for inferring individual treatment effect.

A novel cross-domain attentional multi-task architecture - xDom - for robust real-world wireless radio frequency (RF) fingerprinting is presented in this work. To the best of our knowledge, this is the first time such comprehensive attention mechanism is applied to solve RF fingerprinting problem. In this paper, we resort to real-world IoT WiFi and Bluetooth (BT) emissions (instead of synthetic waveform generation) in a rich multipath and unavoidable interference environment in an indoor experimental testbed. We show the impact of the time-frame of capture by including waveforms collected over a span of months and demonstrate the same time-frame and multiple time-frame fingerprinting evaluations. The effectiveness of resorting to a multi-task architecture is also experimentally proven by conducting single-task and multi-task model analyses. Finally, we demonstrate the significant gain in performance achieved with the proposed xDom architecture by benchmarking against a well-known state-of-the-art model for fingerprinting. Specifically, we report performance improvements by up to 59.3 % and 4.91x under single-task WiFi and BT fingerprinting respectively, and up to 50.5 % increase in fingerprinting accuracy under the multi-task setting.

This is the pre-review draft of the second of two papers submitted to Journal of Lightwave Technology, where the effect on confinement loss of thickness variations along the perimeter of the tubes composing the cladding of inhibited-coupling guiding Tubular Lattice hollow-core fibers is investigated by using the Azimuthal Fourier Decomposition technique (developed in Part I) for the description of the cladding modal dynamics and their interaction with fundamental core mode. The results show that the thickness inhomogeneity affects the confinement loss spectrum through confinement loss increase and frequency red- and blue-shift of the high loss spectral regions. The magnitudes of the confinement loss increase and the high-loss region frequency shift strongly depend on the spatial distribution of the thickness inhomogeneity. The study provides insight into the loss mechanism of non-ideal tube lattice fibers, it allows quantifying the impact of such kind of structural deformations, identifying the route to make fibers more resilient to such fabrication imperfections, and highlighting once again the importance played in inhibited-coupling fibers by the interaction between core modes and the intricate set of cladding modes.

Research in indoor localization has received increasing focus recently due to the various applications location-based services can provide, such as use cases in emergency response, health care, targeted marketing, and assets tracking. Extending indoor localization to multi-floor environments with accurate floor or height estimation helps exploit localization services more fully. However, the literature lacks an up-to-date survey that thoroughly discusses indoor localization in the multi-floor setting. This survey provides an exhaustive review of these systems to provide insights for further research toward better localization systems. We give a detailed description of the different indoor localization technologies such as WiFi, cellular, and various sensors, used with techniques such as fingerprinting and Pedestrian Dead Reckoning (PDR), underlining their use in the 3D context of multi-story environments. Moreover, we review several existing systems, highlighting their approaches’ strengths and limitations. The survey’s primary focus is systems addressing the localization of users and their devices. We also overview different metrics used in evaluating the systems, such as localization accuracy, deployment cost, scalability, robustness, generalizability, ubiquity, and energy efficiency. Furthermore, we discuss various challenges in localization, highlighting those that arise with multi-floor scenarios and indicating directions for further research. No data was collected for this work.

Autonomous driving requires accurate, robust, and fast decision-making perception systems to understand the driving environment. Object detection is critical in allowing the perception system to understand the environment. The perception systems, especially 2D object detection and classification, have succeeded because of the emergence of deep learning (DL) in computer vision (CV) applications. However, 2D object detection lacks depth information, which is crucial to understanding the driving environment. Therefore, 3D object detection is fundamental for the perception system of autonomous driving and robotics applications to estimate the objects’ location and understand the driving environment. The CV community has been giving much attention recently to 3D object detection because of the growth of DL models and the need to know accurate locations of objects. However, 3D object detection is still challenging because of scale changes, the lack of 3D sensor information, and occlusions. Researchers have been using multiple sensors to solve these problems and further enhance the performance of the perception system. This survey presents the multisensor (camera, radar, and LiDAR) fusion-based 3D object detection methods. The fully autonomous vehicles need to be equipped with multiple sensors for robust and reliable driving. Camera, LiDAR, and radar sensors and their corresponding advantages and disadvantages are also presented. Then, relevant datasets are summarized, and state-of-the-art multisensor fusion-based methods are reviewed. Finally, challenges, open issues, and possible research directions are presented.

How are we able to fix it so as to theoretically go over it, if the optimality and efficiency of the orbital angular momentum (OAM) multiplexing technique in a multi-input multi-output (MIMO) system fails in practice $-$ something that technically causes a considerable reduction in the maximum achievable value of the transceiver’s degree-of-freedom? Is the aforementioned challenging issue theoretically interpretable?

In this paper, as a generalization the famous Gaussian sums, we introduce a new kind of mixed character sums and their applications in the constructions of Mutually unbiased bases (MUBs) and signal sets. It is well-known that MUBs have significant applications in quantum information processing and other fields. However, only a few constructions of MUBs are known in the literature. In this note, we provide a recursive constructions of approximately mutually unbiased bases. Meanwhile, we present some constructions of asymptotically optimal signal sets by using the mixed character sums.