pwc-archive/papers-with-abstracts huggingface dataset:
Feasibility Study of UAV-Assisted Anti-Jamming Positioning
Authors: Zijie Wang, Rongke Liu, Qirui Liu, Lincong Han
Date: 2020-11-05 00:00:00
Tasks: Position

As the cost and technical difficulty of jamming devices continue to decrease, jamming has become one of the major threats to positioning service. Unfortunately, most conventional technologies are vulnerable to jamming attacks due to their inherent shortcomings like weak signal strength and unfavorable anchor geometry. Thanks to the high operational flexibility, unmanned aerial vehicle (UAV) could fly close to users to enhance signal strength while maintaining a satisfactory geometry, making it a potential solution to the above challenges. In this article, we propose a UAV-assisted anti-jamming positioning system, in which multiple UAVs first utilize time-difference-of-arrival (TDoA) measurements from ground reference stations and double-response two-way ranging (DR-TWR) measurements from UAV-to-UAV links to perform self-localization as well as clock synchronization, and then act as anchor nodes to provide TDoA positioning service for ground users in the presence of jamming. To evaluate the feasibility and performance of the proposed system, we first derive the Cramer-Rao lower bound (CRLB) of UAV self-localization. Then, the impacts of UAV position uncertainty and synchronization errors caused by jamming on positioning service are modeled, and the theoretical root-mean-square error (RMSE) of user position estimate is further derived. Numerical results demonstrate that the proposed system is a promising alternative to existing positioning systems when their services are disrupted by jamming. The most notable advantage of the proposed system is that it is fully compatible with existing user equipment (UE) and positioning methods.
DNN-based Detectors for Massive MIMO Systems with Low-Resolution ADCs
Authors: Ly V. Nguyen, Duy H. N. Nguyen, A. Lee Swindlehurst
Date: 2020-11-05 00:00:00

Low-resolution analog-to-digital converters (ADCs) have been considered as a practical and promising solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. Unfortunately, low-resolution ADCs significantly distort the received signals, and thus make data detection much more challenging. In this paper, we develop a new deep neural network (DNN) framework for efficient and low-complexity data detection in low-resolution massive MIMO systems. Based on reformulated maximum likelihood 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 simple structures designed for low-resolution MIMO receivers and thus can be efficiently trained and implemented. Numerical results also show that OBMNet and FBMNet significantly outperform existing detection methods.
Aerial Platforms with Reconfigurable Smart Surfaces for 5G and Beyond
Authors: Safwan Alfattani, Wael Jaafar, Yassine Hmamouche, Halim Yanikomeroglu, Abbas Yongaçoglu, Ng\d{o}c Dũng Đào, Peiying Zhu
Date: 2020-06-16 00:00:00

Aerial platforms are expected to deliver enhanced and seamless connectivity in the fifth generation (5G) wireless networks and beyond (B5G). This is generally achievable by supporting advanced onboard communication features embedded in heavy and energy-intensive equipment. Alternatively, reconfigurable smart surfaces (RSS), which smartly exploit/recycle signal reflections in the environment, are increasingly being recognized as a new wireless communication paradigm to improve communication links. In fact, their reduced cost, low power use, light weight, and flexible deployment make them an attractive candidate for integration with 5G/B5G technologies. In this article, we discuss comprehensive approaches to the integration of RSS in aerial platforms. First, we present a review of RSS technology, its operations and types of communication. Next, we describe how RSS can be used in aerial platforms, and we propose a control architecture workflow. Then, several potential use cases are presented and discussed. Finally, associated research challenges are identified.
Temporal Resolution of Measurements and the Effects on Calibrating Building Energy Models
Authors: Fazel Khayatian, Andrew Bollinger, Philipp Heer
Date: 2020-11-04 00:00:00
Tasks: energy management, Management, Sensitivity

With the recent interest in installing building energy management systems, the availability of data enables calibration of building energy models. This study compares calibration on eight different temporal resolutions and contrasts the benefits and drawbacks of each. While calibrating heating and cooling energy consumption shows less sensitivity to the temporal resolution, the accuracy of electricity energy consumption and domestic hot water greatly varies depending on the temporal resolution.
Sparse Array Beamforming Design for Wideband Signal Models
Authors: Syed A. Hamza, Moeness Amin
Date: 2020-11-04 00:00:00
Tasks: Matrix Completion

We develop sparse array receive beamformer design methods achieving maximum signal-to-interference plus noise ratio (MaxSINR) for wideband sources and jammers. Both tapped delay line (TDL) filtering and the DFT realizations to wideband array processing are considered. The array sparsity stems from the limited number of available RF transmission chains that switch between the sensors, thereby configuring different arrays at different times. The sparse array configuration design problem is formulated as a quadratically constraint quadratic program (QCQP) and solved by using SDR (semidefinite relaxation). A computationally viable approach through SCA (successive convex relaxation) is also pursued. In order to realize an implementable design, in presence of missing autocorrelation lags, we propose parameter-free block Toeplitz matrix completion to estimate the received data correlation matrix across the entire array aperture. It is shown that the optimum wideband sparse array effectively utilizes the array aperture and provides considerable performance improvement over suboptimal array topologies.
Direction of Arrival Estimation for Non-Coherent Sub-Arrays via Joint Sparse and Low-Rank Signal Recovery
Authors: Tom Tirer, Oded Bialer
Date: 2020-11-04 00:00:00
Tasks: Direction of Arrival Estimation

Estimating the directions of arrival (DOAs) of multiple sources from a single snapshot obtained by a coherent antenna array is a well-known problem, which can be addressed by sparse signal reconstruction methods, where the DOAs are estimated from the peaks of the recovered high-dimensional signal. In this paper, we consider a more challenging DOA estimation task where the array is composed of non-coherent sub-arrays (i.e., sub-arrays that observe different unknown phase shifts due to using low-cost unsynchronized local oscillators). We formulate this problem as the reconstruction of a joint sparse and low-rank matrix and solve its convex relaxation. While the DOAs can be estimated from the solution of the convex problem, we further show how an improvement is obtained if instead one estimates from this solution the phase shifts, creates "phase-corrected" observations and applies another final (plain, coherent) sparsity-based DOA estimation. Numerical experiments show that the proposed approach outperforms strategies that are based on non-coherent processing of the sub-arrays as well as other sparsity-based methods.
Base Station and Passive Reflectors Placement for Urban mmWave Networks
Authors: Chethan Kumar Anjinappa, Fatih Erden, Ismail Guvenc
Date: 2020-11-03 00:00:00
Tasks: 3D geometry

The use of millimeter-wave (mmWave) bands in 5G networks introduce a new set of challenges to network planning. Vulnerability to blockages and high path loss at mmWave frequencies require careful planning of the network to achieve the desired service quality. In this paper, we propose a novel 3D geometry-based framework for deploying mmWave base stations (gNBs) in urban environments by considering first-order reflection effects. We also provide a solution for the optimum deployment of passive metallic reflectors (PMRs) to extend radio coverage to non-line-of-sight (NLoS) areas. In particular, we perform visibility analysis to find the direct and indirect visibility regions, and using these, we derive a geometry-and-blockage-aided path loss model. We then formulate the network planning problem as two independent optimization problems, placement of gNB(s) and PMRs, to maximize the coverage area with a certain quality-of-service constraint and minimum cost. We test the efficacy of our proposed approach using a generic map and compare our simulation results with the ray-tracing solution. Our simulation results show that considering the first-order reflections in planning the mmWave network helps reduce the number of PMRs required to cover the NLoS area and the gNB placement aided with PMRs requires fewer gNBs to cover the same area, which in turn reduces the deployment cost.
DNN-based mask estimation for distributed speech enhancement in spatially unconstrained microphone arrays
Authors: Nicolas Furnon, Romain Serizel, Irina Illina, Slim Essid
Date: 2020-11-03 00:00:00
Tasks: Diversity, Noise Estimation, Speech Enhancement, speech-recognition, Speech Recognition

Deep neural network (DNN)-based speech enhancement algorithms in microphone arrays have now proven to be efficient solutions to speech understanding and speech recognition in noisy environments. However, in the context of ad-hoc microphone arrays, many challenges remain and raise the need for distributed processing. In this paper, we propose to extend a previously introduced distributed DNN-based time-frequency mask estimation scheme that can efficiently use spatial information in form of so-called compressed signals which are pre-filtered target estimations. We study the performance of this algorithm under realistic acoustic conditions and investigate practical aspects of its optimal application. We show that the nodes in the microphone array cooperate by taking profit of their spatial coverage in the room. We also propose to use the compressed signals not only to convey the target estimation but also the noise estimation in order to exploit the acoustic diversity recorded throughout the microphone array.
Efficient Sensor Management for Multitarget Tracking in Passive Sensor Networks via Cauchy-Schwarz Divergence
Authors: Yun Zhu
Date: 2020-11-03 00:00:00
Tasks: Management

This paper presents an efficient sensor management approach for multi-target tracking in passive sensor networks. Compared with active sensor networks, passive sensor networks have larger uncertainty due to the nature of passive sensing. Multi-target tracking in passive sensor networks is challenging because the multi-sensor multi-target fusion problem is difficult and sensor management is necessary to achieve good trade-offs between tracking accuracy and energy consumption or other costs. To address this problem, we present an efficient information-theoretic approach to manage the sensors for better tracking of the unknown and time-varying number of targets. This is accomplished with two main technical innovations. The first is a tractable information-based multi-sensor selection solution via a partially observed Markov decision process framework. The Cauchy-Schwarz divergence is used as the criterion to select informative sensors sequentially from the candidates. The second is a novel dual-stage fusion strategy based on the iterated-corrector multi-sensor generalized labeled multi-Bernoulli filter. Since the performance of the iterated-corrector scheme is greatly influenced by the order of sensor updates, the selected sensors are first ranked in order of their abilities to detect targets according to the Cauchy-Schwarz divergence, followed the iterated-corrector update. The computation costs of ranking the sensors are negligible, since the Cauchy-Schwarz divergence has been computed in the multi-sensor selection procedure. Simulation results validate the effectiveness and efficiency of the proposed approach.
Enhanced RSS-based UAV Localization via Trajectory and Multi-base Stations
Authors: YiFan Li, Feng Shu, Baihua Shi, Xin Cheng, Yaoliang Song, Jiangzhou Wang
Date: 2020-11-03 00:00:00
Tasks: Position

To improve the localization precision of unmanned aerial vehicle (UAV), a novel framework is established by jointly utilizing multiple measurements of received signal strength (RSS) from multiple base stations (BSs) and multiple points on trajectory. First, a joint maximum likelihood (ML) of exploiting both trajectory information and multi-BSs is proposed. To reduce its high complexity, two low-complexity localization methods are designed. The first method is from BS to trajectory (BST), called LCSL-BST. First, fixing the nth BS, by exploiting multiple measurements along trajectory, the position of UAV is computed by ML rule. Finally, all computed positions of UAV for different BSs are combined to form the resulting position. The second method reverses the order, called LCSL-TBS. We also derive the Cramer-Rao lower boundary (CRLB) of the joint ML method. From simulation results, we can see that the proposed joint ML and separate LCSL-BST methods have made a significant improvement over conventional ML method without use of trajectory knowledge in terms of location performance. The former achieves the joint CRLB and the latter is of low-complexity.
Basis Signal Optimization for N-Continuous OFDM
Authors: Peng Wei, Yue Xiao, Wei Xiang
Date: 2018-12-28 00:00:00

A novel basis signal optimization method is proposed for reducing the interference in the N-continuous orthogonal frequency division multiplexing (NC-OFDM) system. Compared to conventional NC-OFDM, the proposed scheme is capable of improving the transmission performance while maintaining an identical sidelobe suppression performance imposed by the linear combination of two groups of basis signals. Our performance results demonstrate that with a low complexity overhead, the proposed scheme is capable of striking a better trade-off among the bit error rate (BER), complexity, and the sidelobe suppression performance compared to its conventional counterpart.
Energy Efficient Pairing and Power Optimization for NOMA UAV Network under QoS Constraints
Authors: Irfan Azam, Muhammad Basit Shahab, Soo Young Shin
Date: 2020-11-03 00:00:00

Due to an increasing number of unmanned aerial vehicles (UAVs) with generally limited battery power, energy efficient data transmission schemes with massive connectivity capabilities are required for future wireless networks. Non-orthogonal multiple access (NOMA) is one of the promising techniques that provide such massive connectivity by allowing superposed data transmission of multiple users over the same resource block. Unlike existing literature, this paper presents a user pairing and power allocation technique for energy efficient and quality of service (QoS) aware NOMA transmission for cellular-connected mobile UAVs. The aim is to minimize the power consumption of the mobile UAVs during uplink data transmission and guarantee their required transmission rate by jointly optimizing the UAV pairing and power allocation. Furthermore, the performance and pairing complexity for mobile UAVs is analyzed to show that the proposed pairing technique efficiently fulfills their QoS requirements. Numerical and simulation results show the performance gain of the proposed technique compared to the conventional NOMA techniques.
Learning-based Load Balancing Handover in Mobile Millimeter Wave Networks
Authors: Sara Khosravi, Hossein S. Ghadikolaei, Marina Petrova
Date: 2020-11-03 00:00:00
Tasks: Management

Millimeter-wave (mmWave) communication is a promising solution to the high data rate demands in the upcoming 5G and beyond communication networks. When it comes to supporting seamless connectivity in mobile scenarios, resource and handover management are two of the main challenges in mmWave networks. In this paper, we address these two problems jointly and propose a learning-based load balancing handover in multi-user mobile mmWave networks. Our handover algorithm selects a backup base station and allocates the resource to maximize the sum rate of all the users while ensuring a target rate threshold and preventing excessive handovers. We model the user association as a non-convex optimization problem. Then, by applying a deep deterministic policy gradient (DDPG) method, we approximate the solution of the optimization problem. Through simulations, we show that our proposed algorithm minimizes the number of the events where a user's rate is less than its minimum rate requirement and minimizes the number of handovers while increasing the sum rate of all users.
Learning-based Handover in Mobile Millimeter-wave Networks
Authors: Sara Khosravi, Hossein S. Ghadikolaei, Marina Petrova
Date: 2020-03-24 00:00:00

Millimeter-wave (mmWave) communication is considered as a key enabler of ultra-high data rates in the future cellular and wireless networks. The need for directional communication between base stations (BSs) and users in mmWave systems, that is achieved through beamforming, increases the complexity of the channel estimation. Moreover, in order to provide better coverage, dense deployment of BSs is required which causes frequent handovers and increased association overhead. In this paper, we present an approach that jointly addresses the beamforming and handover problems. Our solution entails an efficient beamforming method with a minimum number of pilots and a learning-based handover method supporting mobile scenarios. We use reinforcement learning algorithm to learn the optimal choices of the backup BSs in different locations of a mobile user. We show that our method provides high rate and reliability in all locations of the user's trajectory with a minimal number of handovers. Simulation results in an outdoor environment based on geometric mmWave channel modeling and real building map data show the superior performance of our proposed solution in achievable instantaneous rate and trajectory rate.
Foveated Model Observers for Visual Search in 3D Medical Images
Authors: Miguel A. Lago, Craig K. Abbey, Miguel P. Eckstein
Date: 2020-11-03 00:00:00

Model observers have a long history of success in predicting human observer performance in clinically-relevant detection tasks. New 3D image modalities provide more signal information but vastly increase the search space to be scrutinized. Here, we compared standard linear model observers (ideal observers, non-pre-whitening matched filter with eye filter, and various versions of Channelized Hotelling models) to human performance searching in 3D 1/f$^{2.8}$ filtered noise images and assessed its relationship to the more traditional location known exactly detection tasks and 2D search. We investigated two different signal types that vary in their detectability away from the point of fixation (visual periphery). We show that the influence of 3D search on human performance interacts with the signal's detectability in the visual periphery. Detection performance for signals difficult to detect in the visual periphery deteriorates greatly in 3D search but not in 3D location known exactly and 2D search. Standard model observers do not predict the interaction between 3D search and signal type. A proposed extension of the Channelized Hotelling model (foveated search model) that processes the image with reduced spatial detail away from the point of fixation, explores the image through eye movements, and scrolls across slices can successfully predict the interaction observed in humans and also the types of errors in 3D search. Together, the findings highlight the need for foveated model observers for image quality evaluation with 3D search.
Differential Reflecting Modulation for Reconfigurable Intelligent Surface Based Communications
Authors: Shuaishuai Guo, Jia Ye, Peng Zhang, Haixia Zhang, Mohamed-Slim Alouini
Date: 2020-08-03 00:00:00

Reconfigurable intelligent surface (RIS) based communications have emerged as a new paradigm. This letter proposes a differential reflecting modulation (DRM) scheme for RIS based communication systems. In DRM, information bits are jointly carried by the activation permutations of the reflecting patterns and the phases of the transmitted signals, leading to that DRM can work without any channel state information (CSI) at the transmitter, RIS or receiver. In other words, DRM can release the intricate and resource-consuming channel estimation in the transmission process. Simulation results show that the proposed DRM pays an acceptable SNR penalty compared to non-differential modulation with coherent detection.
Centre of pressure estimation during walking using only inertial-measurement units and end-to-end statistical modelling
Authors: Janez Podobnik, David Kraljic, Matjaz Zadravec, Marko Munih
Date: 2020-11-02 00:00:00

Estimation of the centre of pressure (COP) is an important part of the gait analysis, for example, when evaluating the functional capacity of individuals affected by motor impairment. Inertial measurement units (IMUs) and force sensors are commonly used to measure gait characteristic of healthy and impaired subjects. We present a methodology for estimating the COP solely from raw gyroscope, accelerometer, and magnetometer data from IMUs using statistical modelling. We demonstrate the viability of the method using an example of two models: a linear model and a non-linear Long-Short-Term Memory (LSTM) neural network model. Models were trained on the COP ground truth data measured using an instrumented treadmill and achieved the average intra-subject root mean square (RMS) error between estimated and ground truth COP of 12.3mm and the average inter-subject RMS error of 23.7mm which is comparable or better than similar studies so far. We show that the calibration procedure in the instrumented treadmill can be as short as a couple of minutes without the decrease in our model performance. We also show that the magnetic component of the recorded IMU signal, which is most sensitive to environmental changes, can be safely dropped without a significant decrease in model performance. Finally, we show that the number of IMUs can be reduced to five without deterioration in the model performance.
Precoding for Satellite Communications: Why, How and What next?
Authors: Bhavani Shankar Mysore, Eva Lagunas, Symeon Chatzinotas, Bjorn Ottersten
Date: 2020-11-02 00:00:00

Precoding has stood out as a promising multi-user transmission technique to meet the emerging throughput demand of satellite communication systems while awaiting the technological maturity for exploiting higher bands. Precoding enables the reduction of interference among co-channel beams through spatial processing while promoting aggressive frequency reuse and improving spectral efficiency. Satellite systems offer multitude of system and service configurations, resulting in different precoder design methodologies. This article explores the motivation for the introduction of precoding, offers an insight to their theoretical development in a diverse scenarios and presents some avenues for future development.