pwc-archive/papers-with-abstracts huggingface dataset:
Maximum correntropy criterion for robust TOA-based localization in NLOS environments
Authors: Wenxin Xiong, Christian Schindelhauer, Hing Cheung So, Zhi Wang
Date: 2020-09-13 00:00:00

We investigate the problem of time-of-arrival (TOA) based localization under possible non-line-of-sight (NLOS) propagation conditions. To robustify the squared-range-based location estimator, we follow the maximum correntropy criterion, essentially the Welsch $M$-estimator with a redescending influence function which behaves like $\ell_0$-minimization towards the grossly biased measurements, to derive the formulation. The half-quadratic technique is then applied to settle the resulting optimization problem in an alternating maximization (AM) manner. By construction, the major computational challenge at each AM iteration boils down to handling an easily solvable generalized trust region subproblem. It is worth noting that the implementation of our localization method requires nothing but merely the TOA-based range measurements and sensor positions as prior information. Simulation and experimental results demonstrate the competence of the presented scheme in outperforming several state-of-the-art approaches in terms of positioning accuracy, especially in scenarios where the percentage of NLOS paths is not large enough.
Network analysis and disease subnets for the SARS-CoV-2/Human interactome
Authors: Beatriz Luna, Marcelino Ramírez, Edgardo Galán
Date: 2020-09-13 00:00:00

Motivation: With the aim to amplify and make sense of interactions of virus-human proteins in the case of SARS-CoV-2, we performed a structural analysis of the network of protein interactions obtained from the integration of three sources: 1) proteins of virus SARS-CoV-2, 2)physical interactions between SARS-CoV-2 and human proteins, 3) known interactions of these human proteins between them and the dossier of affections in which these proteins are implicated. Results: As a product of this research, we present two networks, one from the interactions virus-host, and the other restricted to host-host, the last one is not usually considered for network analysis. We identified the most important proteins in both networks, those that have the maximal value of calculated invariants, these proteins are considered as the most: affected, connected or those that best monitor the flow of information in the network, among them we find UBC, a human protein related with ubiquination, linked with different stages of coronavirus disease, and ORF7A a virus protein that induces apoptosis in infected cells, associated with virion tethering. Using the constructed networks, we establish the more significant diseases corresponding with human proteins and their connections with other proteins. It is relevant that the identified diseases coincide with comorbidities, particularly the subnetwork of diabetes involves a great quantity of virus and human proteins (56%) and interactions (60%), this could explain the effect of this condition as an important cause of disease complications.
Millimeter Wave Full-Duplex Radios: New Challenges and Techniques
Authors: Ian P. Roberts, Jeffrey G. Andrews, Hardik B. Jain, Sriram Vishwanath
Date: 2020-09-13 00:00:00
Tasks: Management

Equipping millimeter wave (mmWave) systems with full-duplex capability would accelerate and transform next-generation wireless applications and forge a path for new ones. Full-duplex mmWave transceivers could capitalize on the already attractive features of mmWave communication by supplying spectral efficiency gains and latency improvements while also affording future networks with deployment solutions in the form of interference management and wireless backhaul. Foreseeable challenges and obstacles in making mmWave full-duplex a reality are presented in this article along with noteworthy unknowns warranting further investigation. With these novelties of mmWave full-duplex in mind, we lay out potential solutions---beyond active self-interference cancellation---that harness the spatial degrees of freedom bestowed by dense antenna arrays to enable simultaneous transmission and reception in-band.
Simple Learning Rules Generate Complex Canonical Circuits
Date: 2020-09-13 00:00:00

Cortical circuits are characterized by exquisitely complex connectivity
patterns that emerge during development from undifferentiated networks. The
development of these circuits is governed by a combination of precise molecular
cues that dictate neuronal identity and location along with activity dependent
mechanisms that help establish, refine, and maintain neuronal connectivity.
Here we ask whether simple plasticity mechanisms can lead to assembling a
cortical microcircuit with canonical inter-laminar connectivity, starting from
a network with all-to-all connectivity. The target canonical microcircuit is
based on the pattern of connections between cortical layers typically found in
multiple cortical areas in rodents, cats and monkeys. We use a computational
model as a proof-of-principle to demonstrate that classical and reverse
spike-timing dependent plasticity rules lead to a formation of networks that
resemble canonical microcircuits. The model converges to biologically
reasonable solutions provided that there is a balance between potentiation and
depression and enhanced inputs to layer 4, only for a small combination of
plasticity rules. The model makes specific testable predictions about the
learning computations operant across cortical layers and their dynamic
deployment during development.
Phase-Only Beam Broadening of Contiguous Uniform Subarrayed Arrays Utilizing Three Metaheuristic Global Optimization Techniques
Authors: Barry Daniel, Carl Edwards, Adam Anderson
Date: 2020-09-14 00:00:00
Tasks: global-optimization

Radar beam broadening provides continuous coverage of a wider angular extent. While many methods have been published that address beam broadening of traditional (nonsubarrayed) arrays, there is a knowledge gap in the published literature with respect to efficient and effective beam broadening of contiguous uniform subarrayed arrays. This paper presents efficient and effective methods for beam broadening of contiguous uniform subarrayed arrays where elements of the array are grouped together to have the same element excitations. Particularly, this paper focuses on phase-only optimization to preserve maximum power output. The high dimensionality of the solution space of possible phase settings causes brute force techniques to be infeasible for exhaustively evaluating the entire space. This paper presents three metaheuristic global optimization techniques that efficiently and effectively search for optimal phase values in this large solution space that satisfy the desired broadened pattern. The techniques presented in this paper are simulated annealing, genetic algorithm with elitism, and particle swarm optimization. These techniques are evaluated on idealized 40x40 and 80x80 element rectangular arrays with 5x5 element subarrays. The results of this study show that as configured in this paper the simulated annealing and particle swarm techniques outshine the genetic algorithm technique for 40x40 and 80x80 rectangular arrays grouped into contiguous uniform 5x5 element subarrays.
Revisiting the nonlinear Gaussian noise model: The case of hybrid fiber spans
Authors: I. Roudas, J. Kwapisz, X. Jiang
Date: 2020-09-14 00:00:00

We rederive from first principles and generalize the theoretical framework of the nonlinear Gaussian noise model to the case of coherent optical systems with multiple fiber types per span and ideal Nyquist spectra. We focus on the accurate numerical evaluation of the integral for the nonlinear noise variance for hybrid fiber spans. This task consists in addressing four computational aspects: (i) Adopting a novel transformation of variables (other than using hyperbolic coordinates) that changes the integrand to a more appropriate form for numerical quadrature; (ii) Evaluating analytically the integral at its lower limit, where the integrand presents a singularity; (iii) Dividing the interval of integration into subintervals of size pi and approximating the integral in each subinterval by using various algorithms; and (iv) Deriving an upper bound for the relative error when the interval of integration is truncated in order to accelerate computation. We apply the proposed model to coherent optical communications systems with hybrid fiber spans composed of quasi-singlemode fiber and single-mode fiber segments. The accuracy of the final analytical relationship for the nonlinear noise variance in long-haul coherent optical communications systems with hybrid fiber spans is checked using the split-step Fourier method and Monte Carlo simulation. It is shown to be adequate to within 0.1 dBQ for the determination of the optimal fiber segment lengths per span that maximize system performance.
Low Complexity Lookup Table Aided Soft Output Semidefinite Relaxation based Faster-than-Nyquist Signaling Detector
Authors: Adem Cicek, Ian Marsland, Enver Cavus, Ebrahim Bedeer, Halim Yanikomeroglu
Date: 2020-09-14 00:00:00
Tasks: Decoder

Spectrum scarcity necessitates innovative, spectral-efficient strategies to meet the ever-growing demand for high data rates. Faster-than-Nyquist (FTN) signaling emerges as a compelling spectral-efficient transmission method that pushes transmit data symbols beyond the Nyquist limit, offering enhanced spectral efficiency (SE). While FTN signaling maintains SE with the same energy and bandwidth as the Nyquist signaling, it introduces increased complexity, particularly at higher modulation levels. This complexity predominantly arises from the detection process, which seeks to mitigate the intentional intersymbol interference generated by FTN signaling. Another challenge involves the generation of reliable log-likelihood ratios (LLRs) vital for soft channel decoders. In this study, we introduce a lookup table (LUT) aided soft output semidefinite relaxation (soSDR) based sub-optimal FTN detector, which can be extended to higher modulation levels. This detector possesses polynomial computational complexity, given the negligible complexity associated with soft value generation. Our study assesses the performance of this soft output detector against that of the optimal FTN detector, Bahl, Cocke, Jelinek and Raviv (BCJR) algorithm as the benchmark. The likelihood values produced by our LUT aided semidefinite relaxation (SDR) based FTN signaling detector show promising viability in coded scenario.
Mitigation and Resiliency of Multi-Agent Systems Subject to Malicious Cyber Attacks on Communication Links
Date: 2020-09-14 00:00:00

This paper aims at investigating a novel type of cyber attack that is
injected to multi-agent systems (MAS) having an underlying directed graph. The
cyber attack, which is designated as the controllability attack, is injected by
the malicious adversary into the communication links among the agents. The
adversary, leveraging the compromised communication links disguises the cyber
attack signals and attempts to take control over the entire network of MAS. The
adversary aims at achieving this by directly attacking only a subset of the
multi-agents. Conditions under which the malicious hacker has control over the
entire MAS network are provided. Two notions of security controllability
indices are proposed and developed. These notions are utilized as metrics to
evaluate the controllability that each agent provides to the adversary for
executing the malicious cyber attack. Furthermore, the possibility of
introducing zero dynamics cyber attacks on the MAS through compromising the
communication links is also investigated. Finally, an illustrative numerical
example is provided to demonstrate the effectiveness of our proposed methods.
Enhancing Ambient Backscatter Communication Utilizing Coherent and Non-Coherent Space-Time Codes
Authors: Wenjing Liu, Shanpu Shen, Danny H. K. Tsang, Ross Murch
Date: 2020-09-14 00:00:00
Tasks: TAG, Unity

Ambient backscatter communication (AmBC) leverages the existing ambient radio frequency (RF) environment to implement communication with battery-free devices. The key challenge in the development of AmBC is the very weak RF signals backscattered by the AmBC Tag. To overcome this challenge, we propose the use of orthogonal space-time block codes (OSTBC) by incorporating multiple antennas at the Tag as well as at the Reader. Our approach considers both coherent and non-coherent OSTBC so that systems with and without channel state information can be considered. To allow the application of OSTBC, we develop an approximate linearized and normalized multiple-input multiple-output (MIMO) channel model for the AmBC system. This MIMO channel model is shown to be accurate for a wide range of useful operating conditions. Two coherent detectors and a non-coherent detector are also provided based on the proposed AmBC channel model. Simulation results show that enhanced bit error rate performance can be achieved, demonstrating the benefit of using multiple antennas at the Tag as well as the Reader.
Joint Optimization of Beamforming, Phase-Shifting and Power Allocation in a Multi-cluster IRS-NOMA Network
Authors: Ximing Xie, Fang Fang, Zhiguo Ding
Date: 2020-09-14 00:00:00

The combination of non-orthogonal multiple access (NOMA) and intelligent reflecting surface (IRS) is an efficient solution to significantly enhance the energy efficiency of the wireless communication system. In this paper, we focus on a downlink multi-cluster NOMA network, where each cluster is supported by one IRS. We aim to minimize the transmit power by jointly optimizing the beamforming, the power allocation and the phase shift of each IRS. The formulated problem is non-convex and challenging to solve due to the coupled variables, i.e., the beamforming vector, the power allocation coefficient and the phase shift matrix. To address this non-convex problem, we propose an alternating optimization based algorithm. Specifically, we divide the primal problem into the two subproblems for beamforming optimization and phase shifting feasiblity, where the two subproblems are solved iteratively. Moreover, to guarantee the feasibility of the beamforming optimization problem, an iterative algorithm is proposed to search the feasible initial points. To reduce the complexity, we also propose a simplified algorithm based on partial exhaustive search for this system model. Simulation results demonstrate that the proposed alternating algorithm can yield a better performance gain than the partial exhaustive search algorithm, OMA-IRS, and NOMA with random IRS phase shift.
Energy-Efficient Resource Allocation for NOMA enabled MEC Networks with Imperfect CSI
Authors: Fang Fang, Kaidi Wang, Zhiguo Ding, Victor C. M. Leung
Date: 2020-09-14 00:00:00
Tasks: Edge-computing

The combination of non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) can significantly improve the spectrum efficiency beyond the fifth-generation network. In this paper, we mainly focus on energy-efficient resource allocation for a multi-user, multi-BS NOMA assisted MEC network with imperfect channel state information (CSI), in which each user can upload its tasks to multiple base stations (BSs) for remote executions. To minimize the energy consumption, we consider jointly optimizing the task assignment, power allocation and user association. As the main contribution, with imperfect CSI, the optimal closed-form expressions of task assignment and power allocation are analytically derived for the two-BS case. Specifically, the original formulated problem is nonconvex. We first transform the probabilistic problem into a non-probabilistic one. Subsequently, a bilevel programming method is proposed to derive the optimal solution. In addition, by incorporating the matching algorithm with the optimal task and power allocation, we propose a low complexity algorithm to efficiently optimize user association for the multi-user and multi-BS case. Simulations demonstrate that the proposed algorithm can yield much better performance than the conventional OMA scheme but also the identical results with lower complexity from the exhaustive search with the small number of BSs.
Consensus of Multi-agent System via Constrained Invariant Set of a class of Unstable System
Authors: Chong Jin Ong, Bonan Hou
Date: 2020-09-14 00:00:00

This work shows an approach to achieve output consensus among heterogeneous agents in a multi-agent environment where each agent is subject to input constraints. The communication among agents is described by a time-varying directed/undirected graph. The approach is based on the well-known Internal Model Principle which uses an unstable reference system. One main contribution of this work is the characterization of the maximal constraint admissible invariant set (MCAI) for the combined agent-reference system. Typically, MCAI sets do not exist for unstable system. This work shows that for an important class of agent-reference system that is unstable, MCAI exists and can be computed. This MCAI set is used in a Reference Governor approach, combined with a projected consensus algorithm, to achieve output consensus of all agents while satisfying constraints of each. Examples are provided to illustrate the approach.
Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labels
Date: 2020-09-14 00:00:00
Tasks: Brain Segmentation, Segmentation, Super-Resolution, Transfer Learning

Segmentation of the developing fetal brain is an important step in
quantitative analyses. However, manual segmentation is a very time-consuming
task which is prone to error and must be completed by highly specialized
indi-viduals. Super-resolution reconstruction of fetal MRI has become standard
for processing such data as it improves image quality and resolution. However,
dif-ferent pipelines result in slightly different outputs, further complicating
the gen-eralization of segmentation methods aiming to segment super-resolution
data. Therefore, we propose using transfer learning with noisy multi-class
labels to automatically segment high resolution fetal brain MRIs using a single
set of seg-mentations created with one reconstruction method and tested for
generalizability across other reconstruction methods. Our results show that the
network can auto-matically segment fetal brain reconstructions into 7 different
tissue types, regard-less of reconstruction method used. Transfer learning
offers some advantages when compared to training without pre-initialized
weights, but the network trained on clean labels had more accurate
segmentations overall. No additional manual segmentations were required.
Therefore, the proposed network has the potential to eliminate the need for
manual segmentations needed in quantitative analyses of the fetal brain
independent of reconstruction method used, offering an unbiased way to quantify
normal and pathological neurodevelopment.
Correction to:"Position estimation from direction or range measurements"
Date: 2020-09-24 00:00:00
Tasks: LEMMA, Pose Estimation, Position

This technical communiqu\'e aims at correcting an erroneous statement (Lemma
2.4) in an earlier paper by the same authors concerning a sufficient condition
of uniform observability for a Linear Time-Varying (LTV) system. In this
earlier paper, the proofs of two other lemmas, about body-pose estimation from
range measurements, relied on this erroneous statement. For the sake of
conciseness, only a new proof of one of these lemmas is presented, the proof of
the second lemma being a simpler version of it.
Neurodynamic TDOA localization with NLOS mitigation via maximum correntropy criterion
Authors: Wenxin Xiong, Christian Schindelhauer, Hing Cheung So, Junli Liang, Zhi Wang
Date: 2020-09-14 00:00:00
Tasks: Position

In this paper, we exploit the maximum correntropy criterion (MCC) to robustify the traditional time-difference-of-arrival (TDOA) location estimator in the presence of non-line-of-sight (NLOS) propagation conditions. For the sake of statistical efficiency, the correntropy-based robust loss is imposed on the underlying time-of-arrival composition via joint estimation of the source position and onset time, instead of the TDOA counterpart generated in the postprocessing of sensor-collected timestamps. We then employ a neurodynamic optimization approach to tackle the highly nonconvex MCC formulation. Furthermore, we examine the local stability of equilibrium for the corresponding projection-type neural network model. Simulation investigations in representative NLOS propagation scenarios demonstrate that our neurodynamic robust TDOA localization solution is capable of outperforming several existing schemes in terms of positioning accuracy.
Feedback Prediction for Proactive HARQ in the Context of Industrial Internet of Things
Authors: Baris Göktepe, Tatiana Rykova, Thomas Fehrenbach, Thomas Schierl, Cornelius Hellge
Date: 2020-09-14 00:00:00
Tasks: Prediction

In this work, we investigate proactive Hybrid Automatic Repeat reQuest (HARQ) using link-level simulations for multiple packet sizes, modulation orders, BLock Error Rate (BLER) targets and two delay budgets of 1 ms and 2 ms, in the context of Industrial Internet of Things (IIOT) applications. In particular, we propose an enhanced proactive HARQ protocol using a feedback prediction mechanism. We show that the enhanced protocol achieves a significant gain over the classical proactive HARQ in terms of energy efficiency for almost all evaluated BLER targets at least for sufficiently large feedback delays. Furthermore, we demonstrate that the proposed protocol clearly outperforms the classical proactive HARQ in all scenarios when taking a processing delay reduction due to the less complex prediction approach into account, achieving an energy efficiency gain in the range of 11% up to 15% for very stringent latency budgets of 1 ms at $10^{-2}$ BLER and from 4% up to 7.5% for less stringent latency budgets of 2 ms at $10^{-3}$ BLER. Furthermore, we show that power-constrained proactive HARQ with prediction even outperforms unconstrained reactive HARQ for sufficiently large feedback delays.
Finding Acceptable Parameter Regions of Stochastic Hill functions for Multisite Phosphorylation Mechanism
Authors: Minghan Chen, Mansooreh Ahmadian, Layne Watson, Yang Cao
Date: 2020-09-14 00:00:00
Tasks: Stochastic Optimization, valid

Multisite phosphorylation plays an important role in regulating switchlike protein activity and has been used widely in mathematical models. With the development of new experimental techniques and more molecular data, molecular phosphorylation processes emerge in many systems with increasing complexity and sizes. These developments call for simple yet valid stochastic models to describe various multisite phosphorylation processes, especially in large and complex biochemical networks. To reduce model complexity, this work aims to simplify the multisite phosphorylation mechanism by a stochastic Hill function model. Further, this work optimizes regions of parameter space to match simulation results from the stochastic Hill function with the distributive multisite phosphorylation process. While traditional parameter optimization methods have been focusing on finding the best parameter vector, in most circumstances modelers would like to find a set of parameter vectors that generate similar system dynamics and results. This paper proposes a general $\alpha$-$\beta$-$\gamma$ rule to return an acceptable parameter region of the stochastic Hill function based on a quasi-Newton stochastic optimization (QNSTOP) algorithm. Different objective functions are investigated characterizing different features of the simulation-based empirical data, among which the approximate maximum log-likelihood method is recommended for general applications. Numerical results demonstrate that with an appropriate parameter vector value, the stochastic Hill function model depicts the multisite phosphorylation process well except the initial (transient) period.