pwc-archive/papers-with-abstracts huggingface dataset:
Coordinating charging request allocation between self-interested navigation service platforms
Authors: Marianne Guillet, Maximilian Schiffer
Date: 2022-08-19 00:00:00

Current electric vehicle market trends indicate an increasing adoption rate across several countries. To meet the expected growing charging demand, it is necessary to scale up the current charging infrastructure and to mitigate current reliability deficiencies, e.g., due to broken connectors or misreported charging station availability status. However, even within a properly dimensioned charging infrastructure, a risk for local bottlenecks remains if several drivers cannot coordinate their charging station visit decisions. Here, navigation service platforms can optimally balance charging demand over available stations to reduce possible station visit conflicts and increase user satisfaction. While such fleet-optimized charging station visit recommendations may alleviate local bottlenecks, they can also harm the system if self-interested navigation service platforms seek to maximize their own customers' satisfaction. To study these dynamics, we model fleet-optimized charging station allocation as a resource allocation game in which navigation platforms constitute players and assign potentially free charging stations to drivers. We show that no pure Nash equilibrium guarantee exists for this game, which motivates us to study VCG mechanisms both in offline and online settings, to coordinate players' strategies toward a better social outcome. Extensive numerical studies for the city of Berlin show that when coordinating players through VCG mechanisms, the social cost decreases on average by 42 % in the online setting and by 52 % in the offline setting.
Deep 3D Vessel Segmentation based on Cross Transformer Network
Authors: Chengwei Pan, Baolian Qi, Gangming Zhao, Jiaheng Liu, Chaowei Fang, Dingwen Zhang, Jinpeng Li
Date: 2022-08-22 00:00:00
Tasks: Computed Tomography (CT), Segmentation

The coronary microvascular disease poses a great threat to human health. Computer-aided analysis/diagnosis systems help physicians intervene in the disease at early stages, where 3D vessel segmentation is a fundamental step. However, there is a lack of carefully annotated dataset to support algorithm development and evaluation. On the other hand, the commonly-used U-Net structures often yield disconnected and inaccurate segmentation results, especially for small vessel structures. In this paper, motivated by the data scarcity, we first construct two large-scale vessel segmentation datasets consisting of 100 and 500 computed tomography (CT) volumes with pixel-level annotations by experienced radiologists. To enhance the U-Net, we further propose the cross transformer network (CTN) for fine-grained vessel segmentation. In CTN, a transformer module is constructed in parallel to a U-Net to learn long-distance dependencies between different anatomical regions; and these dependencies are communicated to the U-Net at multiple stages to endow it with global awareness. Experimental results on the two in-house datasets indicate that this hybrid model alleviates unexpected disconnections by considering topological information across regions. Our codes, together with the trained models are made publicly available at https://github.com/qibaolian/ctn.
What Does a One-Bit Quanta Image Sensor Offer?
Authors: Stanley H. Chan
Date: 2022-08-19 00:00:00
Tasks: Quantization

The one-bit quanta image sensor (QIS) is a photon-counting device that captures image intensities using binary bits. Assuming that the analog voltage generated at the floating diffusion of the photodiode follows a Poisson-Gaussian distribution, the sensor produces either a ``1'' if the voltage is above a certain threshold or ``0'' if it is below the threshold. The concept of this binary sensor has been proposed for more than a decade, and physical devices have been built to realize the concept. However, what benefits does a one-bit QIS offer compared to a conventional multi-bit CMOS image sensor? Besides the known empirical results, are there theoretical proofs to support these findings? The goal of this paper is to provide new theoretical support from a signal processing perspective. In particular, it is theoretically found that the sensor can offer three benefits: (1) Low-light: One-bit QIS performs better at low-light because it has a low read noise, and its one-bit quantization can produce an error-free measurement. However, this requires the exposure time to be appropriately configured. (2) Frame rate: One-bit sensors can operate at a much higher speed because a response is generated as soon as a photon is detected. However, in the presence of read noise, there exists an optimal frame rate beyond which the performance will degrade. A Closed-form expression of the optimal frame rate is derived. (3) Dynamic range: One-bit QIS offers a higher dynamic range. The benefit is brought by two complementary characteristics of the sensor: nonlinearity and exposure bracketing. The decoupling of the two factors is theoretically proved, and closed-form expressions are derived.
GEO Payload Power Minimization: Joint Precoding and Beam Hopping Design
Authors: Vu Nguyen Ha, Ti Ti Nguyen, Eva Lagunas, Juan Carlos Merlano Duncan, Symeon Chatzinotas
Date: 2022-08-22 00:00:00
Tasks: compressed sensing

This paper aims to jointly determine linear precoding (LP) vectors, beam hopping (BH), and discrete DVB-S2X transmission rates for the GEO satellite communication systems to minimize the payload power consumption and satisfy ground users' demands within a time window. Regarding constraint on the maximum number of illuminated beams per time slot, the technical requirement is formulated as a sparse optimization problem in which the hardware-related beam illumination energy is modeled in a sparsity form of the LP vectors. To cope with this problem, the compressed sensing method is employed to transform the sparsity parts into the quadratic form of precoders. Then, an iterative window-based algorithm is developed to update the LP vectors sequentially to an efficient solution. Additionally, two other two-phase frameworks are also proposed for comparison purposes. In the first phase, these methods aim to determine the MODCOD transmission schemes for users to meet their demands by using a heuristic approach or DNN tool. In the second phase, the LP vectors of each time slot will be optimized separately based on the determined MODCOD schemes.
Chirp Spread Spectrum-based Waveform Design and Detection Mechanisms for LPWAN-based IoT -- A Survey
Authors: Ali Waqar Azim, Ahmad Bazzi, Raed Shubair, Marwa Chafii
Date: 2022-08-22 00:00:00

LoRa is a widely adopted method of utilizing chirp spread spectrum (CSS) techniques at the physical (PHY) layer to facilitate low-power wide-area network (LPWAN) connectivity. By tailoring the spreading factors, LoRa can achieve a diverse spectral and energy efficiency (EE) levels, making it amenable to a plethora of Internet-of-Things (IoT) applications that rely on LPWANs. However, a primary drawback of LoRa is its relatively low data data rate. Despite this, there has been a dearth of research dedicated to enhancing the data transfer capabilities of LoRa until recently, when a plethora of CSS-based PHY layer alternatives to LoRa for LPWANs was proposed. This survey, for the first time, presents a comprehensive examination of the waveform design of these CSS-based PHY layer alternatives, proposed between \(2019\) and \(2022\). A total of fifteen alternatives to LoRa are analyzed. This study delves deeply into the waveform design of alternatives to LoRa. The CSS schemes studied in this study are classified into three categories: single chirp, multiple chirps, and multiple chirps with index modulation, based on the number of activated frequency shifts activated for un-chirped symbols. The transceiver architecture of these schemes is thoroughly explicated. Additionally, we propose coherent/non-coherent detection mechanisms for specific schemes that have not been previously documented in the literature. We also provide some key insights and recommendations based on the performance of the schemes. The performance of the schemes is evaluated based on metrics such as EE, spectral efficiency, the bit-error-rate (BER) in additive white Gaussian noise, and BER in the presence of phase and frequency offsets. Finally, we highlight some open research issues and future research directions in this field.
Doppler Exploitation in Bistatic mmWave Radio SLAM
Authors: Yu Ge, Ossi Kaltiokallio, Hui Chen, Fan Jiang, Jukka Talvitie, Mikko Valkama, Lennart Svensson, Henk Wymeersch
Date: 2022-08-22 00:00:00
Tasks: Simultaneous Localization and Mapping

Networks in 5G and beyond utilize millimeter wave (mmWave) radio signals, large bandwidths, and large antenna arrays, which bring opportunities in jointly localizing the user equipment and mapping the propagation environment, termed as simultaneous localization and mapping (SLAM). Existing approaches mainly rely on delays and angles, and ignore the Doppler, although it contains geometric information. In this paper, we study the benefits of exploiting Doppler in SLAM through deriving the posterior Cram\'er-Rao bounds (PCRBs) and formulating the extended Kalman-Poisson multi-Bernoulli sequential filtering solution with Doppler as one of the involved measurements. Both theoretical PCRB analysis and simulation results demonstrate the efficacy of utilizing Doppler.
ArNet-ECG: Deep Learning for the Detection of Atrial Fibrillation from the Raw Electrocardiogram
Authors: Noam Ben-Moshe, Shany Biton, Joachim A. Behar
Date: 2022-08-22 00:00:00
Tasks: Time Series, Time Series Analysis

Atrial fibrillation (AF) is the most prevalent heart arrhythmia. AF manifests on the electrocardiogram (ECG) though irregular beat-to-beat time interval variation, the absence of P-wave and the presence of fibrillatory waves (f-wave). We hypothesize that a deep learning (DL) approach trained on the raw ECG will enable robust detection of AF events and the estimation of the AF burden (AFB). We further hypothesize that the performance reached leveraging the raw ECG will be superior to previously developed methods using the beat-to-beat interval variation time series. Consequently, we develop a new DL algorithm, denoted ArNet-ECG, to robustly detect AF events and estimate the AFB from the raw ECG and benchmark this algorithms against previous work. Methods: A dataset including 2,247 adult patients and totaling over 53,753 hours of continuous ECG from the University of Virginia (UVAF) was used. Results: ArNet-ECG obtained an F1 of 0.96 and ArNet2 obtained an F1 0.94. Discussion and conclusion: ArNet-ECG outperformed ArNet2 thus demonstrating that using the raw ECG provides added performance over the beat-to-beat interval time series. The main reason found for explaining the higher performance of ArNet-ECG was its high performance on atrial flutter examples versus poor performance on these recordings for ArNet2.
Doubling the Number of Connected Devices in Narrow-band Internet of Things while Maintaining System Performance: An STC-based Approach
Authors: Abdulwahid Mohammed, Mohammed. S. El-Bakry, Hassan Mostafa, Abd El-Hady. A. Ammar
Date: 2022-08-22 00:00:00

Narrow-band Internet of Things (NB-IoT) is a low-power wide-area network (LPWAN) method that was first launched by the 3rd generation partnership project (3GPP) Rel- 13 with the purpose of enabling low-cost, low-power and wide-area cellular connection for the Internet of Things (IoT). As the demand for over-the-air services grows and with the number of linked wireless devices reaching 100 billion, wireless spectrum is becoming scarce, necessitating creative techniques that can increase the number of connected devices within a restricted spectral resource in order to satisfy service needs. Consequently, it is vital that academics develop efficient solutions to fulfill the quality of service (QoS) criteria of the NB-IoT in the context of 5th generation (5G) and beyond. This study paves the way for 5G networks and beyond to have increased capacity and data rate for NB-IoT. Whereas, this article suggests a method for increasing the number of connected devices by using a technique known as symbol time compression (STC). The suggested method compresses the occupied bandwidth of each device without increasing complexity, losing data throughput or bit error rate (BER) performance. The STC approach is proposed in the literature to work with the conventional orthogonal frequency division multiplexing (OFDM) to reduce bandwidth usage by 50% and improve the peak-to-average power ratio (PAPR). Specifically, An STC-based method is proposed that exploits the unused bandwidth to double the number of connected devices while keeping system performance and complexity. Furthermore, the {\mu}-law companding technique is leveraged to reduce the PAPR of the transmitted signals. The obtained simulation results reveal that the proposed approach using the {\mu}-law companding technique increases the transmitted data by twice and reduces the PAPR by 3.22 dB while maintaining the same complexity and BER.
Iterative Sparse Recovery based Passive Localization in Perceptive Mobile Networks
Authors: Lei Xie, Shenghui Song
Date: 2022-08-22 00:00:00

Perceptive mobile networks (PMNs) were proposed to integrate sensing capability into current cellular networks where multiple sensing nodes (SNs) can collaboratively sense the same targets. Besides the active sensing in traditional radar systems, passive sensing based on the uplink communication signals from mobile user equipment may play a more important role in PMNs, especially for targets with weak electromagnetic wave reflection, e.g., pedestrians. However, without the properly designed active sensing waveform, passive sensing normally suffers from low signal to noise power ratio (SNR). As a result, most existing methods require a large number of data samples to achieve an accurate estimate of the covariance matrix for the received signals, based on which a power spectrum is constructed for localization purposes. Such a requirement will create heavy communication workload for PMNs because the data samples need to be transferred over the network for collaborative sensing. To tackle this issue, in this paper we leverage the sparse structure of the localization problem to reduce the searching space and propose an iterative sparse recovery (ISR) algorithm that estimates the covariance matrix and the power spectrum in an iterative manner. Experiment results show that, with very few samples in the low SNR regime, the ISR algorithm can achieve much better localization performance than existing methods.
An Improved Equiangular Division Algorithm for SBR based Ray Tracing Channel Modeling
Authors: Yuyang Zhou, Yinghua Wang, Yuxiao Li, Jialing Huang, Jie Huang, Cheng-Xiang Wang
Date: 2022-08-22 00:00:00

Compared with image method (IM) based ray tracing (RT), shooting and bouncing ray (SBR) method is characterized by fast speed but low accuracy. In this paper, an iterative precise algorithm based on equiangular division is proposed to make rough paths accurate, allowing SBR to calculate exact channel information. Different ray launching methods are compared to obtain a better launching method. By using equiangular division, rays are launched more uniformly from transmitter (Tx) compared with the current equidistant division method. With the proposed iterative precise algorithm, error of angle of departure (AOD) and angle of arrival (AOA) is below 0.01 degree. The relationship between the number of iterations and error reduction is also given. It is illustrated that the proposed method has the same accuracy as IM by comparing the power delay profile (PDP) and angle distribution of paths. This can solve the problem of low accuracy brougth by SBR.
An Improved Ray Tracing Acceleration Algorithm Based on Bounding Volume Hierarchies
Authors: Chen Wang, Yinghua Wang, Yuxiao Li, Jialing Huang, Jie Huang, Cheng-Xiang Wang
Date: 2022-08-22 00:00:00

Ray tracing is an efficient channel modeling method. However, the traditional ray tracing method has high computation complexity. To solve this problem, an improved bounding volume hierarchies (BVH) algorithm is proposed in this paper. Based on surface area heuristic (SAH) and spatial distance, the proposed algorithm can effectively reduce the number of unnecessary intersection tests between ray and triangular facets. In addition, the algorithm fully considers the influence of ray action range, which can not only make up for the defects of spatial division based on uniform grid method and k-dimensional (KD) tree, but also solve the problem of unsatisfactory spatial division based on traditional BVH algorithm. The simulation results show that compared with the traditional BVH algorithm, the proposed algorithm can improve the computation efficiency by 20% to 35% while ensuring the computation accuracy.
A Weighted Random Forest Based PositioningAlgorithm for 6G Indoor Communications
Authors: Yang Wu, Yinghua Wang, Jie Huang, Cheng-Xiang Wang, Chen Huang
Date: 2022-08-22 00:00:00

Due to the indoor none-line-of-sight (NLoS) propagation and multi-access interference (MAI), it is a great challenge to achieve centimeter-level positioning accuracy in indoor scenarios. However, the sixth generation (6G) wireless communications provide a good opportunity for the centimeter-level positioning. In 6G, the millimeter wave (mmWave) and terahertz (THz) communications have ultra-broad bandwidth so that the channel state information (CSI) will have a high resolution. In this paper, a weighted random forest (WRF) based indoor positioning algorithm using CSI based channel fingerprint feature is proposed to achieve high-precision positioning for 6G indoor communications. In addition, ray-tracing (RT) is used to improve the efficiency of establishing channel fingerprint database. The simulation results demonstrate the accuracy and robustness of the proposed algorithm. It is shown that the positioning accuracy of the algorithm is stable within 6 cm in different indoor scenarios with the channel fingerprint database established at 0.2 m intervals.
An SBR Based Ray Tracing Channel Modeling Method for THz and Massive MIMO Communications
Authors: Yuanzhe Wang, Hao Cao, Yifan Jin, Zizhe Zhou, Yinghua Wang, Jialing Huang, Yuxiao Li, Jie Huang, Cheng-Xiang Wang
Date: 2022-08-22 00:00:00

Terahertz (THz) communication and the application of massive multiple-input multiple-output (MIMO) technology have been proved significant for the sixth generation (6G) communication systems, and have gained global interests. In this paper, we employ the shooting and bouncing ray (SBR) method integrated with acceleration technology to model THz and massive MIMO channel. The results of ray tracing (RT) simulation in this paper, i.e., angle of departure (AoD), angle of arrival (AoA), and power delay profile (PDP) under the frequency band supported by the commercial RT software Wireless Insite (WI) are in agreement with those produced by WI. Based on the Kirchhoff scattering effect on material surfaces and atmospheric absorption loss showing at THz frequency band, the modified propagation models of Fresnel reflection coefficients and free-space attenuation are consistent with the measured results. For massive MIMO, the channel capacity and the stochastic power distribution are analyzed. The results indicate the applicability of SBR method for building deterministic models of THz and massive MIMO channels with extensive functions and acceptable accuracy.
Repetitive transcranial magnetic stimulation and epilepsy: A brief essay
Authors: Arsalan Heidarpanah
Date: 2022-08-21 00:00:00

During the last three decades, many studies have been conducted in the field of treatment with non-invasive methods. In this way, researchers try to use alternative methods including the use of electromagnetic waves in the treatment of diseases. As a result, the therapeutic use of electromagnetic waves in the treatment of neurological diseases has made significant progress. Among the various techniques that have revolutionized the non-invasive treatment of neurological disorders, there is a remarkable technique called Repetitive Transcranial Magnetic Stimulation (rTMS). On the other hand, there is a wide range of neurological conditions (like epilepsy) that are somewhat drug-resistant or can only be controlled with high-risk treatments. In this article, the effect of rTMS on epilepsy is investigated.
A New Radar Signal Multiparameter-Based Deinterleaving Method
Authors: Wang Chao, Liu Weisong, Li Xueqiong, Wang Xiang, Huang Zhitao
Date: 2022-08-21 00:00:00

Radar signal deinterleaving has been extensively and thoroughly investigated in the electronic reconnaissance field. In this work, a new radar signal multiparameter-based deinterleaving method is proposed. In this method, semantic information composed of the pulse repetition interval (PRI), pulse width (PW), radio frequency (RF), and pulse amplitude (PA) of a radar signal is used to deinterleave radar signals. A bidirectional gated recurrent unit (BGRU) is employed, and the difference of time of arrival (DTOA)/RF, DTOA/PW, and DTOA/PA of the pulse stream are input into the BGRU. Based on the semantic information contained in different radar signal types, each pulse in the obtained pulse stream is classified according to the semantic information category, and the radar signals are deinterleaved. Compared to the PRI-based deinterleaving methods, the proposed method utilizes the multidimensional information of radar signals. As a result, higher deinterleaving accuracy is achieved. Compared to other existing radar signal multiparameter-based deinterleaving methods, the proposed method can adapt to radar signals with complex parameter features as well as to complex signal environments, and can complete the use of multiparameter in one step.
Green Joint Communications and Sensing Employing Analog Multi-Beam Antenna Arrays
Authors: Kai Wu, J. Andrew Zhang, Xiaojing Huang, Robert W. Heath Jr., Y. Jay Guo
Date: 2022-08-21 00:00:00

Joint communications and sensing (JCAS) is potentially a hallmark technology for the sixth generation mobile network (6G). Most existing JCAS designs are based on digital arrays, analog arrays with tunable phase shifters, or hybrid arrays, which are effective but are generally complicated to design and power inefficient. This article introduces the energy-efficient and easy-to-design multi-beam antenna arrays (MBAAs) for JCAS. Using pre-designed and fixed analog devices, such as lens or Butler matrix, MBAA can simultaneously steer multiple beams yet with negligible power consumption compared with other techniques. Moreover, MBAAs enable flexible beam synthesis, accurate angle-of-arrival estimation, and easy handling/utilization of the beam squint effect. All these features have not been well captured by the JACS community yet. To promote the awareness of them, we intuitively illustrate them and also exploit them for constructing a multi-beam JCAS framework. Finally, the challenges and opportunities are discussed to foster the development of green JCAS systems.