pwc-archive/papers-with-abstracts huggingface dataset:
MA-RECON: Mask-aware deep-neural-network for robust fast MRI k-space interpolation
Authors: Nitzan Avidan, Moti Freiman
Date: 2022-08-31 00:00:00
Tasks: compressed sensing, MRI Reconstruction

High-quality reconstruction of MRI images from under-sampled `k-space' data, which is in the Fourier domain, is crucial for shortening MRI acquisition times and ensuring superior temporal resolution. Over recent years, a wealth of deep neural network (DNN) methods have emerged, aiming to tackle the complex, ill-posed inverse problem linked to this process. However, their instability against variations in the acquisition process and anatomical distribution exposes a deficiency in the generalization of relevant physical models within these DNN architectures. The goal of our work is to enhance the generalization capabilities of DNN methods for k-space interpolation by introducing `MA-RECON', an innovative mask-aware DNN architecture and associated training method. Unlike preceding approaches, our `MA-RECON' architecture encodes not only the observed data but also the under-sampling mask within the model structure. It implements a tailored training approach that leverages data generated with a variety of under-sampling masks to stimulate the model's generalization of the under-sampled MRI reconstruction problem. Therefore, effectively represents the associated inverse problem, akin to the classical compressed sensing approach. The benefits of our MA-RECON approach were affirmed through rigorous testing with the widely accessible fastMRI dataset. Compared to standard DNN methods and DNNs trained with under-sampling mask augmentation, our approach demonstrated superior generalization capabilities. This resulted in a considerable improvement in robustness against variations in both the acquisition process and anatomical distribution, especially in regions with pathology. In conclusion, our mask-aware strategy holds promise for enhancing the generalization capacity and robustness of DNN-based methodologies for MRI reconstruction from undersampled k-space data.
On the Energy-Efficiency Maximization for IRS-Assisted MIMOME Wiretap Channels
Authors: Anshu Mukherjee, Vaibhav Kumar, Derrick Wing Kwan Ng, Le-Nam Tran
Date: 2022-09-01 00:00:00

Security and energy efficiency have become crucial features in the modern-era wireless communication. In this paper, we consider an energy-efficient design for intelligent reflecting surface (IRS)-assisted multiple-input multiple-output multiple-eavesdropper (MIMOME) wiretap channels (WTC). Our objective is to jointly optimize the transmit covariance matrix and the IRS phase-shifts to maximize the secrecy energy efficiency (SEE) of the considered system subject to a secrecy rate constraint at the legitimate receiver. To tackle this challenging non-convex problem in which the design variables are coupled in the objective and the constraint, we propose a penalty dual decomposition based alternating gradient projection (PDDAPG) method to obtain an efficient solution. We also show that the computational complexity of the proposed algorithm grows only linearly with the number of reflecting elements at the IRS, as well as with the number of antennas at transmitter/receivers' nodes. Our results confirm that using an IRS is helpful to improve the SEE of MIMOME WTC compared to its no-IRS counterpart only when the power consumption at IRS is small. In particular, and a large-sized IRS is not always beneficial for the SEE of a MIMOME WTC.
Recurrent LSTM-based UAV Trajectory Prediction with ADS-B Information
Authors: Yifan Zhang, Ziye Jia, Chao Dong, Yuntian Liu, Lei Zhang, Qihui Wu
Date: 2022-09-01 00:00:00
Tasks: Prediction, Time Series Analysis, Trajectory Prediction

Recently, unmanned aerial vehicles (UAVs) are gathering increasing attentions from both the academia and industry. The ever-growing number of UAV brings challenges for air traffic control (ATC), and thus trajectory prediction plays a vital role in ATC, especially for avoiding collisions among UAVs. However, the dynamic flight of UAV aggravates the complexity of trajectory prediction. Different with civil aviation aircrafts, the most intractable difficulty for UAV trajectory prediction depends on acquiring effective location information. Fortunately, the automatic dependent surveillance-broadcast (ADS-B) is an effective technique to help obtain positioning information. It is widely used in the civil aviation aircraft, due to its high data update frequency and low cost of corresponding ground stations construction. Hence, in this work, we consider leveraging ADS-B to help UAV trajectory prediction. However, with the ADS-B information for a UAV, it still lacks efficient mechanism to predict the UAV trajectory. It is noted that the recurrent neural network (RNN) is available for the UAV trajectory prediction, in which the long short-term memory (LSTM) is specialized in dealing with the time-series data. As above, in this work, we design a system of UAV trajectory prediction with the ADS-B information, and propose the recurrent LSTM (RLSTM) based algorithm to achieve the accurate prediction. Finally, extensive simulations are conducted by Python to evaluate the proposed algorithms, and the results show that the average trajectory prediction error is satisfied, which is in line with expectations.
Beyond Diagonal Reconfigurable Intelligent Surfaces: A Multi-Sector Mode Enabling Highly Directional Full-Space Wireless Coverage
Authors: Hongyu Li, Shanpu Shen, Bruno Clerckx
Date: 2022-09-01 00:00:00

Reconfigurable intelligent surface (RIS) has gained much traction due to its potential to manipulate the propagation environment via nearly-passive reconfigurable elements. In our previous work, we have analyzed and proposed a beyond diagonal RIS (BD-RIS) model, which is not limited to traditional diagonal phase shift matrices, to unify different RIS modes/architectures. In this paper, we create a new branch of BD-RIS supporting a multi-sector mode. A multi-sector BD-RIS is modeled as multiple antennas connected to a multi-port group-connected reconfigurable impedance network. More specifically, antennas are divided into $L$ ($L \ge 2$) sectors and arranged as a polygon prism with each sector covering $1/L$ space. Different from the recently introduced concept of intelligent omni-surface (or simultaneously transmitting and reflecting RIS), the multi-sector BD-RIS not only achieves a full-space coverage, but also has significant performance gains thanks to the highly directional beam of each sector.We derive the constraint of the multi-sector BD-RIS and the corresponding channel model taking into account the relationship between antenna beamwidth and gain. With the proposed model, we first derive the scaling law of the received signal power for a multi-sector BD-RIS-assisted single-user system. We then propose efficient beamforming design algorithms to maximize the sum-rate of the multi-sector BD-RIS-assisted multiuser system. Simulation results verify the effectiveness of the proposed design and demonstrate the performance enhancement of the proposed multi-sector BD-RIS.
Energy-Efficient Trajectory Design of a Multi-IRS Assisted Portable Access Point
Authors: Nithin Babu, Marco Virgili, Mohammad Al-jarrah, Xiaoye Jing, Emad Alsusa, Petar Popovski, Andrew Forsyth, Christos Masouros, Constantinos B. Papadias
Date: 2022-09-01 00:00:00
Tasks: Scheduling

In this work, we propose a framework for energy-efficient trajectory design of an unmanned aerial vehicle (UAV)-based portable access point (PAP) deployed to serve a set of ground nodes (GNs). In addition to the PAP and GNs, the system consists of a set of intelligent reflecting surfaces (IRSs) mounted on man-made structures to increase the number of bits transmitted per Joule of energy consumed measured as the global energy efficiency (GEE). The GEE trajectory for the PAP is designed by considering the UAV propulsion energy consumption and the Peukert effect of the PAP battery, which represents an accurate battery discharge profile as a non-linear function of the UAV power consumption profile. The GEE trajectory design problem is solved in two phases: in the first, a path for the PAP and feasible positions for the IRS modules are found using a multi-tier circle packing method, and the required IRS phase shift values are calculated using an alternate optimization method that considers the interdependence between the amplitude and phase responses of an IRS element; in the second phase, the PAP flying velocity and user scheduling are calculated using a novel multilap trajectory design algorithm. Numerical evaluations show that: neglecting the Peukert effect overestimates the available flight time of the PAP; after a certain threshold, increasing the battery size reduces the available flight time of the PAP; the presence of IRS modules improves the GEE of the system compared to other baseline scenarios; the multi-lap trajectory saves more energy compared to a single-lap trajectory developed using a combination of sequential convex programming and Dinkelbach algorithm.
Correction to Friis Noise Factors
Authors: Ankitha E. Bangera
Date: 2022-09-01 00:00:00

The signal-to-noise ratio of a multistage cascade network is often estimated using Friis formulas for noise factors (or the noise figures in decibel). In this letter, the correct formulas to calculate the stage-wise noise factors and the total noise factor in terms of the stage-wise noise factors of an n-stage cascade network are derived. A comparison of our derived formulas for noise factors with Friis's formulas is presented. Contrary to Friis's total noise factor in terms of the stage-wise noise factors, we define the actual total noise factor of the n-stage cascade network as the product of its stage-wise noise factors.
Radio Localization and Sensing -- Part II: State-of-the-art and Challenges
Authors: Henk Wymeersch, Gonzalo Seco-Granados
Date: 2022-09-01 00:00:00

This letter is part of a two-letter tutorial on radio localization and sensing, with a focus on mobile radio systems, i.e., 5G and beyond. Building on Part I, which focused on the fundamentals, here we go deeper into the state-of-the-art advances, as well as 6G, covering enablers and challenges related to modeling, coverage, and accuracy.
Radio Localization and Sensing -- Part I: Fundamentals
Authors: Henk Wymeersch, Gonzalo Seco-Granados
Date: 2022-09-01 00:00:00

This letter is part of a two-letter tutorial on radio localization and sensing, with focus on mobile radio systems in 5G mmWave and beyond. Part I introduces the fundamentals, covering an overview of the relevant literature, as well as the different aspects of localization and sensing problems. Then, different performance metrics are presented, which are important in the evaluation of methods. Methods are detailed in the last part of this letter. Part I thus provides the necessary background to delve into more forward-looking problems in Part II.
Non-Cooperative Resource Management for Intelligent Reflecting Surface Aided Networks
Authors: Wenhao Cai, Ming Li, Qian Liu
Date: 2022-09-01 00:00:00
Tasks: Management

Intelligent reflecting surface (IRS) has emerged as a promising and revolutionizing technology for future wireless networks. Most existing IRS studies focus on simple cooperative systems which usually have a single frequency band. In realistic non-cooperative multi-band networks, however, the existing IRS designs may be not applicable or have severe performance degradation. Thus, in the complex network environment, it is more rational to consider IRSs as public resources to be dynamically allocated to appropriate users. In this paper, we first introduce the auction theory to tackle the resource management problem for a multi-IRS-assisted non-cooperative network. An efficient auction algorithm framework is introduced to sub-optimally solve this non-convex problem. Simulation result illustrates that the significant performance improvement can be achieved by applying the auction algorithm in the complex multi-IRS-assisted non-cooperative network.
Joint Beamforming Design for Intelligent Omni Surface Assisted Wireless Communication Systems
Authors: Wenhao Cai, Ming Li, Yang Liu, Qingqing Wu, Qian Liu
Date: 2022-09-01 00:00:00

Intelligent reflecting surface (IRS) has been widely considered as one of the key enabling techniques for future wireless communication networks owing to its ability of dynamically controlling the phase shift of reflected electromagnetic (EM) waves to construct a favorable propagation environment. While IRS only focuses on signal reflection, the recently emerged innovative concept of intelligent omni-surface (IOS) can provide the dual functionality of manipulating reflecting and transmitting signals. Thus, IOS is a new paradigm for achieving ubiquitous wireless communications. In this paper, we consider an IOSassisted multi-user multi-input single-output (MU-MISO) system where the IOS utilizes its reflective and transmissive properties to enhance the MU-MISO transmission. Both power minimization and sum-rate maximization problems are solved by exploiting the second-order cone programming (SOCP), Riemannian manifold, weighted minimum mean square error (WMMSE), and block coordinate descent (BCD) methods. Simulation results verify the advancements of the IOS for wireless systems and illustrate the significant performance improvement of our proposed joint transmit beamforming, reflecting and transmitting phase-shift, and IOS energy division design algorithms. Compared with conventional IRS, IOS can significantly extend the communication coverage, enhance the strength of received signals, and improve the quality of communication links.
Analysis of Indistinguishable Trajectories of a Nonholonomic Vehicle Subject to Range Measurements
Authors: Francesco Riz, Luigi Palopoli, Daniele Fontanelli
Date: 2022-09-01 00:00:00
Tasks: Translation

We propose a global constructibility analysis for a vehicle moving on a planar surface. Assuming that the vehicle follows a trajectory that can be uniquely identified by the sequence of control inputs and by some intermittent ranging measurements from known points in the environment, we can model the trajectory as a rigid body subject to rotation and translation in the plane. This way, the localisation problem can be reduced to finding the conditions for the existence of a unique roto-translation of the trajectory from a known reference frame to the world reference frame, given the collected measurements. As discussed in this paper, such conditions can be expressed in terms of the shape of the trajectory, of the layout of the ranging sensors, and of the numbers of measurements collected from each of them. The approach applies to a large class of kinematic models. Focusing on the special case of unicycle kinematics, we provide additional local constructibility results.
Systems Theoretic Process Analysis of a Run Time Assured Neural Network Control System
Authors: Kerianne L. Hobbs, Benjamin K. Heiner, Lillian Busse, Kyle Dunlap, Jonathan Rowanhill, Ashlie B. Hocking, Aditya Zutshi
Date: 2022-09-01 00:00:00
Tasks: Collision Avoidance, Deep Reinforcement Learning, Reinforcement Learning (RL)

This research considers the problem of identifying safety constraints and developing Run Time Assurance (RTA) for Deep Reinforcement Learning (RL) Tactical Autopilots that use neural network control systems (NNCS). This research studies a specific use case of an NNCS performing autonomous formation flight while an RTA system provides collision avoidance and geofence assurances. First, Systems Theoretic Accident Models and Processes (STAMP) is applied to identify accidents, hazards, and safety constraints as well as define a functional control system block diagram of the ground station, manned flight lead, and surrogate unmanned wingman. Then, Systems Theoretic Process Analysis (STPA) is applied to the interactions of the the ground station, manned flight lead, surrogate unmanned wingman, and internal elements of the wingman aircraft to identify unsafe control actions, scenarios leading to each, and safety requirements to mitigate risks. This research is the first application of STAMP and STPA to an NNCS bounded by RTA.
Towards Hexapod Gait Adaptation using Enumerative Encoding of Gaits: Gradient-Free Heuristics
Authors: Victor Parque
Date: 2022-09-01 00:00:00

The quest for the efficient adaptation of multilegged robotic systems to changing conditions is expected to render new insights into robotic control and locomotion. In this paper, we study the performance frontiers of the enumerative (factorial) encoding of hexapod gaits for fast recovery to conditions of leg failures. Our computational studies using five nature-inspired gradient-free optimization heuristics have shown that it is possible to render feasible recovery gait strategies that achieve minimal deviation to desired locomotion directives with a few evaluations (trials). For instance, it is possible to generate viable recovery gait strategies reaching 2.5 cm. (10 cm.) deviation on average with respect to a commanded direction with 40 - 60 (20) evaluations/trials. Our results are the potential to enable efficient adaptation to new conditions and to explore further the canonical representations for adaptation in robotic locomotion problems.
A Unified Framework for Consensus and Synchronization on Lie Groups admitting a Bi-Invariant Metric
Authors: Rama Seshan Chandrasekharan, Ravi N Banavar, Arun D Mahindrakar
Date: 2022-09-01 00:00:00

For a finite number of agents evolving on a Euclidean space and linked to each other by a connected graph, the Laplacian flow that is based on the inter-agent errors, ensures consensus or synchronization for both first and second-order dynamics. When such agents evolve on a circle (the Kuramoto oscillator), the flow that depends on the sinusoid of the inter-agent error angles generalizes the same. In this work, it is shown that the Laplacian flow and the Kuramoto oscillator are special cases of a more general theory of consensus on Lie groups that admit bi-invariant metrics. Such a theory not only enables generalization of these consensus and synchronization algorithms to Lie groups but also provide insight on to the abstract group theoretic and differential geometric properties that ensures convergence in Euclidean space and the circle.
Invariant and Dual Invariant Subspaces of $k$-valued Networks
Authors: Daizhan Cheng, HongSheng Qi, Xiao Zhang, Zhengping Ji
Date: 2022-09-01 00:00:00

Consider a $k$-valued network. Two kinds of (control) invariant subspaces, called state and dual invariant subspaces, are proposed, which are subspaces of state space and dual space respectively. Algorithms are presented to verify whether a dual subspace is a dual or dual control invariant subspace. The bearing space of $k$-valued (control) networks is introduced. Using the structure of bearing space, the universal invariant subspace is introduced, which is independent of the dynamics of particular networks. Finally, the relationship between state invariant subspace and dual invariant subspace of a network is investigated. A duality property shows that if a dual subspace is invariant then its perpendicular state subspace is also invariant and vice versa.
Equivalence of SS-based MPC and ARX-based MPC
Authors: Liang Wu
Date: 2022-08-31 00:00:00

Two kinds of control-oriented models used in MPC are the state-space (SS) model and the input-output model (such as the ARX model). The SS model has interpretability when obtained from the modeling paradigm, and the ARX model is black-box but adaptable. This paper aims to introduce interpretability into ARX models, thereby proposing a first-principle-based modeling paradigm for acquiring control-oriented ARX models, as an alternative to the existing data-driven ARX identification paradigm. That is, first to obtain interpretative SS models via linearizing the first-principle-based models at interesting points and then to transform interpretative SS models into their equivalent ARX models via the SS-to-ARX transformations. This paper presents the Cayley-Hamilton, Observer-Theory, and Kalman Filter based SS-to-ARX transformations, further showing that choosing the ARX model order should depend on the process noise to achieve a good closed-loop performance rather than the fitting criteria in data-driven ARX identification paradigm. An AFTI-16 MPC example is used to illustrate the equivalence of SS-based MPC and ARX-based MPC problems and to investigate the robustness of different SS-to-ARX transformations to noise.
A rapid-prototype MPC tool based on gPROMS platform
Authors: Liang Wu, Maarten Nauta
Date: 2022-08-31 00:00:00
Tasks: C++ code, Code Generation, Model Predictive Control

This paper presents a rapid-prototype Model Predictive Control (MPC) tool based on the gPROMS platform, with the support for the whole MPC design workflow. The gPROMS-MPC tool can not only directly interact with a first-principle-based gPROMS model for closed-loop simulations but also utilizes its mathematical information to derive simplified control-oriented models, basically via linearization techniques. It can inherit the interpretability of the first-principle-based gPROMS model, unlike the PAROC framework in which the control-oriented models are obtained from black-box system identification based on gPROMS simulation data. The gPROMS-MPC tool allows users to choose when to linearize such as at each sampling time (successive linearization) or some specific points to obtain one or multiple good linear models. The gPROMS-MPC tool implements our previous construction-free CDAL and the online parametric active-set qpOASES algorithms to solve sparse or condensed MPC problem formulations, respectively, for possible successive linearization or high state-dimension cases. Our CDAL algorithm is also matrix-free and library-free, thus supporting embedded C-code generation. After many example validations of the tool, here we only show one example to investigate the performance of different MPC schemes.
Analytic solution of the exact Daum-Huang flow equation for particle filters
Authors: Olivér Törő, Tamás Bécsi
Date: 2022-08-31 00:00:00
Tasks: State Estimation

State estimation for nonlinear systems, especially in high dimensions, is a generally intractable problem, despite the ever-increasing computing power. Efficient algorithms usually apply a finite-dimensional model for approximating the probability density of the state vector or treat the estimation problem numerically. In 2007 Daum and Huang introduced a novel particle filter approach that uses a homotopy-induced particle flow for the Bayesian update step. Multiple types of particle flows were derived since with different properties. The exact flow considered in this work is a first-order linear ordinary time-varying inhomogeneous differential equation for the particle motion. An analytic solution in the interval [0,1] is derived for the scalar measurement case, which enables significantly faster computation of the Bayesian update step for particle filters.
A Semi-Algebraic Framework for Verification and Synthesis of Control Barrier Functions
Authors: Andrew Clark
Date: 2022-08-31 00:00:00

Safety is a critical property for control systems in medicine, transportation, manufacturing, and other applications, and can be defined as ensuring positive invariance of a predefined safe set. This paper investigates the problems of verifying positive invariance of a semi-algebraic set as well as synthesizing sets that can be made positive invariant through Control Barrier Function (CBF)-based control. The key to our approach consists of mapping conditions for positive invariance to sum-of-squares constraints via the Positivstellensatz from real algebraic geometry. Based on these conditions, we propose a framework for verifying safety of CBF-based control including single CBFs, high-order CBFs, multi-CBFs, and systems with trigonometric dynamics and actuation constraints. In the area of synthesis, we propose algorithms for constructing CBFs, namely, an alternating-descent approach and a local CBF approach. We evaluate our approach through case studies on quadrotor UAV and power converter test systems.