pwc-archive/papers-with-abstracts huggingface dataset:
A Graph-based Algorithm for Robust Sequential Localization Exploiting Multipath for Obstructed-LOS-Bias Mitigation
Authors: Alexander Venus, Erik Leitinger, Stefan Tertinek, Klaus Witrisal
Date: 2022-07-18 00:00:00
Tasks: Position, Single Particle Analysis

This paper presents a factor graph formulation and particle-based sum-product algorithm (SPA) for robust sequential localization in multipath-prone environments. The proposed algorithm jointly performs data association, sequential estimation of a mobile agent position, and adapts all relevant model parameters. We derive a novel non-uniform false alarm (FA) model that captures the delay and amplitude statistics of the multipath radio channel. This model enables the algorithm to indirectly exploit position-related information contained in the multipath components (MPCs) for the estimation of the agent position without using any prior information such as floorplan information or training data. Using simulated and real measurements in different channel conditions, we demonstrate that the algorithm can provide high-accuracy position estimates even in fully obstructed line-of-sight (OLOS) situations and show that the performance of our algorithm constantly attains the posterior Cramer-Rao lower bound (P-CRLB), facilitating the additional information contained in the presented FA model. The algorithm is shown to provide robust estimates in both, dense multipath channels as well as channels showing specular, resolved MPCs, significantly outperforming state-of-the-art radio-based localization methods.
Probabilistic 5G Indoor Positioning Proof of Concept with Outlier Rejection
Authors: Marcus Henninger, Traian E. Abrudan, Silvio Mandelli, Maximilian Arnold, Stephan Saur, Veli-Matti Kolmonen, Siegfried Klein, Thomas Schlitter, Stephan ten Brink
Date: 2022-07-18 00:00:00
Tasks: Position

The continuously increasing bandwidth and antenna aperture available in wireless networks laid the foundation for developing competitive positioning solutions relying on communications standards and hardware. However, poor propagation conditions such as non-line of sight (NLOS) and rich multipath still pose many challenges due to outlier measurements that significantly degrade the positioning performance. In this work, we introduce an iterative positioning method that reweights the time of arrival (ToA) and angle of arrival (AoA) measurements originating from multiple locators in order to efficiently remove outliers. In contrast to existing approaches that typically rely on a single locator to set the time reference for the time difference of arrival (TDoA) measurements corresponding to the remaining locators, and whose measurements may be unreliable, the proposed iterative approach does not rely on a reference locator only. The resulting robust position estimate is then used to initialize a computationally efficient gradient search to perform maximum likelihood position estimation. Our proposal is validated with an experimental setup at 3.75 GHz with 5G numerology in an indoor factory scenario, achieving an error of less than 50 cm in 95% of the measurements. To the best of our knowledge, this paper describes the first proof of concept for 5G-based joint ToA and AoA localization.
Auto-Positioning in Radio-based Localization Systems: A Bayesian Approach
Authors: Andrea Jung, Paul Schwarzbach, Oliver Michler
Date: 2022-07-18 00:00:00
Tasks: Position

The application of radio-based positioning systems is ever increasing. In light of the dissemination of the Internet of Things and location-aware communication systems, the demands on localization architectures and amount of possible use cases steadily increases. While traditional radio-based localization is performed by utilizing stationary nodes, whose positions are absolutely referenced, collaborative auto-positioning methods aim to estimate location information without any a-priori knowledge of the node distribution. The usage of auto-positioning decreases the installation efforts of localization systems and therefore allows their market-wide dissemination. Since observations and position information in this scenario are correlated, the uncertainties of all nodes need to be considered. In this paper we propose a discrete Bayesian method based on a multi-dimensional histogram filter to solve the task of robust auto-positioning, allowing to propagate historical positions and estimated position uncertainties, as well as lowering the demands on observation availability when compared to conventional closed-form approaches. The proposed method is validated utilizing different multipath-, outlier and failure-corrupted ranging measurements in a static environment, where we obtain at least 58% higher positioning accuracy compared to a baseline closed-form auto-positioning approach.
Graph Neural Networks for Distributed Power Allocation in Wireless Networks: Aggregation Over-the-Air
Authors: Yifan Gu, Changyang She, Zhi Quan, Chen Qiu, Xiaodong Xu
Date: 2022-07-18 00:00:00
Tasks: Graph Embedding

Distributed power allocation is important for interference-limited wireless networks with dense transceiver pairs. In this paper, we aim to design low signaling overhead distributed power allocation schemes by using graph neural networks (GNNs), which are scalable to the number of wireless links. We first apply the message passing neural network (MPNN), a unified framework of GNN, to solve the problem. We show that the signaling overhead grows quadratically as the network size increases. Inspired from the over-the-air computation (AirComp), we then propose an Air-MPNN framework, where the messages from neighboring nodes are represented by the transmit power of pilots and can be aggregated efficiently by evaluating the total interference power. The signaling overhead of Air-MPNN grows linearly as the network size increases, and we prove that Air-MPNN is permutation invariant. To further reduce the signaling overhead, we propose the Air message passing recurrent neural network (Air-MPRNN), where each node utilizes the graph embedding and local state in the previous frame to update the graph embedding in the current frame. Since existing communication systems send a pilot during each frame, Air-MPRNN can be integrated into the existing standards by adjusting pilot power. Simulation results validate the scalability of the proposed frameworks, and show that they outperform the existing power allocation algorithms in terms of sum-rate for various system parameters.
Latency Minimization for mmWave D2D Mobile Edge Computing Systems: Joint Task Allocation and Hybrid Beamforming Design
Authors: Yanzhen Liu, Yunlong Cai, An Liu, MinJian Zhao, Lajos Hanzo
Date: 2022-07-17 00:00:00
Tasks: Edge-computing

Mobile edge computing (MEC) and millimeter wave (mmWave) communications are capable of significantly reducing the network's delay and enhancing its capacity. In this paper we investigate a mmWave and device-to-device (D2D) assisted MEC system, in which user A carries out some computational tasks and shares the results with user B with the aid of a base station (BS). We propose a novel two-timescale joint hybrid beamforming and task allocation algorithm to reduce the system latency whilst cut down the required signaling overhead. Specifically, the high-dimensional analog beamforming matrices are updated in a frame-based manner based on the channel state information (CSI) samples, where each frame consists of a number of time slots, while the low-dimensional digital beamforming matrices and the offloading ratio are optimized more frequently relied on the low-dimensional effective channel matrices in each time slot. A stochastic successive convex approximation (SSCA) based algorithm is developed to design the long-term analog beamforming matrices. As for the short-term variables, the digital beamforming matrices are optimized relying on the innovative penalty-concave convex procedure (penalty-CCCP) for handling the mmWave non-linear transmit power constraint, and the offloading ratio can be obtained via the derived closed-form solution. Simulation results verify the effectiveness of the proposed algorithm by comparing the benchmarks.
Robust Vehicle Positioning based on Multi-Epoch and Multi-Antenna TOAs in Harsh Environments
Authors: Xinyuan An, Sihao Zhao, Xiaowei Cui, Gang Liu, Mingquan Lu
Date: 2022-07-17 00:00:00

For radio-based time-of-arrival (TOA) positioning systems applied in harsh environments, obstacles in the surroundings and on the vehicle itself will block the signals from the anchors, reduce the number of available TOA measurements and thus degrade the localization performance. Conventional multi-antenna positioning technique requires a good initialization to avoid local minima, and suffers from location ambiguity due to insufficient number of TOA measurements and/or poor geometry of anchors at a single epoch. A new initialization method based on semidefinite programming (SDP), namely MEMA-SDP, is first designed to address the initialization problem of the MEMA-TOA method. Then, an iterative refinement step is developed to obtain the optimal positioning result based on the MEMA-SDP initialization. We derive the Cramer-Rao lower bound (CRLB) to analyze the accuracy of the new MEMA-TOA method theoretically, and show its superior positioning performance over the conventional single-epoch and multi-antenna (SEMA) localization method. Simulation results in harsh environments demonstrate that i) the new MEMA-SDP provides an initial estimation that is close to the real location, and empirically guarantees the global optimality of the final refined positioning solution, and ii) compared with the conventional SEMA method, the new MEMA-TOA method has higher positioning accuracy without location ambiguity, consistent with the theoretical analysis.
Analysis of the attack and its solution in wireless sensor networks
Authors: Ali Taghavirashidizadeh, Arash Bahram Zarei, Arman Farsi
Date: 2022-07-16 00:00:00
Tasks: Management

Several years ago, wireless sensor networks were used only by the military. These networks, which have many uses and are subject to limitations, the most important of which is the energy constraint, this energy constraint creates the requirement that the number And the length of the messages exchanged between the sensors is low. Sensor networks do not have a stable topology due to their inaccessible environments, and continually changes with the disappearance or addition of a node, support for the topology is performed in three steps before deployment, after deployment and deployment. . The sensor nodes are scattered across the field and transmitted by a multidimensional connection to the sink. The communication protocol of the sensor networks used by all nodes in the network and sink. The protocol consists of five layers and three levels of management. Sensor networks require a kind of security mechanism due to inaccessibility and protection. Conventional security mechanisms are inefficient due to the inherent limitations of sensor nodes in these networks. Sensor nodes, due to energy and resource constraints, require security requirements such as the confidentiality of data integrity data, authentication, synchronization, etc. Currently, many organizations use wireless sensor networks for purposes such as air, Pollution, Traffic Control and Healthcare Security is the main concern of wireless sensor networks. In this article, I will focus on the types of security attacks and their detection. This article outlines security needs and security attacks in wireless sensor networks. Also, the security criteria in wireless sensor networks are mentioned.
Towards Realistic Statistical Channel Models For Positioning: Evaluating the Impact of Early Clusters
Authors: Mohammad Alawieh, George Yammine, Ernst Eberlein, Birendra Ghimire, Norbert Franke, Stephan Jäckel, Tobias Feigl, Christopher Mutschler
Date: 2022-07-16 00:00:00

Physical effects such as reflection, refraction, and diffraction cause a radio signal to arrive from a transmitter to a receiver in multiple replicas that have different amplitude and rotation. Bandwidth-limited signals, such as positioning reference signals, have a limited time resolution. In reality, the signal is often reflected in the close vicinity of a transmitter and receiver, which causes the displacement of the observed peak from the true peak expected according to the line of sight (LOS) geometry between the transmitter and receiver. In this paper, we show that the existing channel model specified for performance evaluation within 3GPP fails to model the above phenomena. As a result, the simulation results deviate significantly from the measured values. Based on our measurement and simulation results, we propose a model for incorporating the signal reflection by obstacles in the vicinity of transmitter or receiver, so that the outcome of the model corresponds to the measurement made in such scenario.
Complementary Semi-Deterministic Clusters for Realistic Statistical Channel Models for Positioning
Authors: Mohammad Alawieh, Ernst Eberlein, Stephan Jäckel, Norbert Franke, Birendra Ghimire, Tobias Feigl, George Yammine, Christopher Mutschler
Date: 2022-07-16 00:00:00

Positioning benefits from channel models that capture geometric effects and, in particular, from the signal properties of the first arriving path and the spatial consistency of the propagation condition of multiple links. The models that capture the physical effects observed in a realistic deployment scenario are essential for assessing the potential benefits of enhancements in positioning methods. Channel models based on ray-tracing simulations and statistical channel models, which are current state-of-the-art methods employed to evaluate performance of positioning in 3GPP systems, do not fully capture important aspects applicable to positioning. Hence, we propose an extension of existing statistical channel models with semi-deterministic clusters (SDCs). SDCs allow channels to be simulated using three types of clusters: fixed-, specular-, and random-clusters. Our results show that the proposed model aligns with measurements obtained in a real deployment scenario. Thus, our channel models can be used to develop advanced positioning solutions based on machine learning, which enable positioning with centimeter level accuracy in NLOS and multipath scenarios.
STEER: Beam Selection for Full-Duplex Millimeter Wave Communication Systems
Authors: Ian P. Roberts, Aditya Chopra, Thomas Novlan, Sriram Vishwanath, Jeffrey G. Andrews
Date: 2022-07-15 00:00:00

Modern millimeter wave (mmWave) communication systems rely on beam alignment to deliver sufficient beamforming gain to close the link between devices. We present a novel beam selection methodology for multi-panel, full-duplex mmWave systems, which we call STEER, that delivers high beamforming gain while significantly reducing the full-duplex self-interference coupled between the transmit and receive beams. STEER does not necessitate changes to conventional beam alignment methodologies nor additional over-the-air feedback, making it compatible with existing cellular standards. Instead, STEER uses conventional beam alignment to identify the general directions beams should be steered, and then it makes use of a minimal number of self-interference measurements to jointly select transmit and receive beams that deliver high gain in these directions while coupling low self-interference. We implement STEER on an industry-grade 28 GHz phased array platform and use further simulation to show that full-duplex operation with beams selected by STEER can notably outperform both half-duplex and full-duplex operation with beams chosen via conventional beam selection. For instance, STEER can reliably reduce self-interference by more than 20 dB and improve SINR by more than 10 dB, compared to conventional beam selection. Our experimental results highlight that beam alignment can be used not only to deliver high beamforming gain in full-duplex mmWave systems but also to mitigate self-interference to levels near or below the noise floor, rendering additional self-interference cancellation unnecessary with STEER.
Unrolled Optimization with Deep Learning-based Priors for Phaseless Inverse Scattering Problems
Authors: Samruddhi Deshmukh, Amartansh Dubey, Ross Murch
Date: 2022-07-15 00:00:00

Inverse scattering problems, such as those in electromagnetic imaging using phaseless data (PD-ISPs), involve imaging objects using phaseless measurements of wave scattering. Such inverse problems can be highly non-linear and ill-posed under extremely strong scattering conditions such as when the objects have very high permittivity or are large in size. In this work, we propose an end-to-end reconstruction framework using unrolled optimization with deep priors to solve PD-ISPs under very strong scattering conditions. We incorporate an approximate linear physics-based model into our optimization framework along with a deep learning-based prior and solve the resulting problem using an iterative algorithm which is unfolded into a deep network. This network not only learns data-driven regularization, but also overcomes the shortcomings of approximate linear models and learns non-linear features. More important, unlike existing PD-ISP methods, the proposed framework learns optimum values of all tunable parameters (including multiple regularization parameters) as a part of the framework. Results from simulations and experiments are shown for the use case of indoor imaging using 2.4 GHz phaseless Wi-Fi measurements, where the objects exhibit extremely strong scattering and low-absorption. Results show that the proposed framework outperforms existing model-driven and data-driven techniques by a significant margin and provides up to 20 times higher validity range.
Adaptive Random Fourier Features Kernel LMS
Authors: Wei Gao, Jie Chen, Cédric Richard, Wentao Shi, Qunfei Zhang
Date: 2022-07-14 00:00:00

We propose the adaptive random Fourier features Gaussian kernel LMS (ARFF-GKLMS). Like most kernel adaptive filters based on stochastic gradient descent, this algorithm uses a preset number of random Fourier features to save computation cost. However, as an extra flexibility, it can adapt the inherent kernel bandwidth in the random Fourier features in an online manner. This adaptation mechanism allows to alleviate the problem of selecting the kernel bandwidth beforehand for the benefit of an improved tracking in non-stationary circumstances. Simulation results confirm that the proposed algorithm achieves a performance improvement in terms of convergence rate, error at steady-state and tracking ability over other kernel adaptive filters with preset kernel bandwidth.
Multi-FEAT: Multi-Feature Edge AlignmenT for Targetless Camera-LiDAR Calibration
Authors: Bichi Zhang, Raj Thilak Rajan
Date: 2022-07-14 00:00:00

The accurate environment perception of automobiles and UAVs (Unmanned Ariel Vehicles) relies on the precision of onboard sensors, which require reliable in-field calibration. This paper introduces a novel approach for targetless camera-LiDAR extrinsic calibration called Multi-FEAT (Multi-Feature Edge AlignmenT). Multi-FEAT uses the cylindrical projection model to transform the 2D(Camera)-3D(LiDAR) calibration problem into a 2D-2D calibration problem, and exploits various LiDAR feature information to supplement the sparse LiDAR point cloud boundaries. In addition, a feature matching function with a precision factor is designed to improve the smoothness of the solution space. The performance of the proposed Multi-FEAT algorithm is evaluated using the KITTI dataset, and our approach shows more reliable results, as compared with several existing targetless calibration methods. We summarize our results and present potential directions for future work.
Quantifying assays: A Modeling tale of variability in cancer therapeutics assessed on cancer cells
Authors: Roumen Anguelov, G Manjunath, Avulundiah E Phiri, Trevor T Nyakudya, Priyesh Bipath, June C Serem, Yvette N Hlophe
Date: 2022-07-18 00:00:00

Inhibiting a signalling pathway concerns controlling the cellular processes of a cancer cell's viability, cell division, and death. Assay protocols created to see if the molecular structures of the drugs being tested have the desired inhibition qualities often show great variability across experiments, and it is imperative to diminish the effects of such variability while inferences are drawn. In this paper we propose the study of experimental data through the lenses of a mathematical model depicting the inhibition mechanism and the activation-inhibition dynamics. The method is exemplified through assay data obtained from the study of inhibition of the CXCL12/CXCR4 activation axis for the melanoma cells. To mitigate the effects of the variability of the data on the cell viability measurement, the cell viability is theoretically constructed as a function of time depending on several parameters. The values of these parameters are estimated by using the experimental data. Deriving approximation for the cell viability in a theoretically pre-determined form has the advantages of (i) being less sensitive to data variability (ii) the estimated values of the parameters are interpreted directly in the biological processes, (iii) the amount of variability explained via the approximation validates the quality of the model, (iv) with the data integrated into the model one can derive a more complete view over the whole process. These advantages are demonstrated in the step-by-step implementation of the outlined approach.
Prediction of protein allosteric signalling pathways and functional residues through paths of optimised propensity
Authors: Nan Wu, Sophia N. Yaliraki, Mauricio Barahona
Date: 2022-07-14 00:00:00
Tasks: Drug Design

Allostery commonly refers to the mechanism that regulates protein activity through the binding of a molecule at a different, usually distal, site from the orthosteric site. The omnipresence of allosteric regulation in nature and its potential for drug design and screening render the study of allostery invaluable. Nevertheless, challenges remain as few computational methods are available to effectively predict allosteric sites, identify signalling pathways involved in allostery, or to aid with the design of suitable molecules targeting such sites. Recently, bond-to-bond propensity analysis has been shown successful at identifying allosteric sites for a large and diverse group of proteins from knowledge of the orthosteric sites and its ligands alone by using network analysis applied to energy-weighted atomistic protein graphs. To address the identification of signalling pathways, we propose here a method to compute and score paths of optimised propensity that link the orthosteric site with the identified allosteric sites, and identifies crucial residues that contribute to those paths. We showcase the approach with three well-studied allosteric proteins: h-Ras, caspase-1, and 3-phosphoinositide-dependent kinase-1 (PDK1). Key residues in both orthosteric and allosteric sites were identified and showed agreement with experimental results, and pivotal signalling residues along the pathway were also revealed, thus providing alternative targets for drug design. By using the computed path scores, we were also able to differentiate the activity of different allosteric modulators.