pwc-archive/papers-with-abstracts huggingface dataset:
Fast Beam Tracking for Reconfigurable Intelligent Surface Assisted Mobile mmWave Networks
Authors: Xiaowen Tian, Zhi Sun
Date: 2021-02-22 00:00:00

Millimeter wave (mmWave) communications are vulnerable to blockages and node mobility due to the highly directional signal beams. The emerging Reconfigurable Intelligent Surfaces (RISs) technique can effectively mitigate the blockage problem by exploring the non-line-of-sight (NLOS) path, where the beam switching is realized by digitally configuring the phases of RIS elements. To date, most efforts have been made in the stationary scenario. However, when considering node mobility, beam tracking algorithms designed specifically for RIS are needed in order to maintain the NLOS link. In this paper, a fast RIS-based beam tracking algorithm is developed by partly transforming the large amount of signaling time into the calculation happens at base station in a mmWave system with mobile users. Specifically, the differential form of optimal RIS configuration is exploited as the updating beam tracking parameter to avoid complex channel estimation procedure. The RIS-based beam tracking problem is then transformed into an optimization problem whose solution is found by a calculation-based search. Finally, by training on a small set candidate, RIS-based beam tracking is realized. The effectiveness and efficiency of the proposed RIS-based beam tracking algorithm is evaluated by simulations. It shows that the proposed algorithm has near-optimal performance with dramatic savings in terms of signaling time.
Elevated LiDAR based Sensing for 6G -- 3D Maps with cm Level Accuracy
Authors: Madhushanka Padmal, Dileepa Marasinghe, Vijitha Isuru, Nalin Jayaweera, Samad Ali, Nandana Rajatheva
Date: 2021-02-22 00:00:00

One key vertical application that will be enabled by 6G is the automation of the processes with the increased use of robots. As a result, sensing and localization of the surrounding environment becomes a crucial factor for these robots to operate. Light detection and ranging (LiDAR) has emerged as an appropriate method of sensing due to its capability of generating detail-rich information with high accuracy. However, LiDARs are power hungry devices that generate a lot of data, and these characteristics limit their use as on-board sensors in robots. In this paper, we present a novel approach on the methodology of generating an enhanced 3D map with improved field-of-view using multiple LiDAR sensors. We utilize an inherent property of LiDAR point clouds; rings and data from the inertial measurement unit (IMU) embedded in the sensor for registration of the point clouds. The generated 3D map has an accuracy of 10 cm when compared to the real-world measurements. We also carry out the practical implementation of the proposed method using two LiDAR sensors. Furthermore, we develop an application to utilize the generated map where a robot navigates through the mapped environment with minimal support from the sensors on-board. The LiDARs are fixed in the infrastructure at elevated positions. Thus this is applicable to vehicular and factory scenarios. Our results further validate the idea of using multiple elevated LiDARs as a part of the infrastructure for various applications.
A bi-atrial statistical shape model for large-scale in silico studies of human atria: model development and application to ECG simulations
Authors: Claudia Nagel, Steffen Schuler, Olaf Dössel, Axel Loewe
Date: 2021-02-22 00:00:00
Tasks: Specificity

Large-scale electrophysiological simulations to obtain electrocardiograms (ECG) carry the potential to produce extensive datasets for training of machine learning classifiers to, e.g., discriminate between different cardiac pathologies. The adoption of simulations for these purposes is limited due to a lack of ready-to-use models covering atrial anatomical variability. We built a bi-atrial statistical shape model (SSM) of the endocardial wall based on 47 segmented human CT and MRI datasets using Gaussian process morphable models. Generalization, specificity, and compactness metrics were evaluated. The SSM was applied to simulate atrial ECGs in 100 random volumetric instances. The first eigenmode of our SSM reflects a change of the total volume of both atria, the second the asymmetry between left vs. right atrial volume, the third a change in the prominence of the atrial appendages. The SSM is capable of generalizing well to unseen geometries and 95% of the total shape variance is covered by its first 23 eigenvectors. The P waves in the 12-lead ECG of 100 random instances showed a duration of 104ms in accordance with large cohort studies. The novel bi-atrial SSM itself as well as 100 exemplary instances with rule-based augmentation of atrial wall thickness, fiber orientation, inter-atrial bridges and tags for anatomical structures have been made publicly available. The novel, openly available bi-atrial SSM can in future be employed to generate large sets of realistic atrial geometries as a basis for in silico big data approaches.
Electromagnetic Model of Reflective Intelligent Surfaces
Authors: Filippo Costa, Michele Borgese
Date: 2021-02-21 00:00:00
Tasks: model

An accurate and simple analytical model for the computation of the reflection amplitude and phase of Reconfigurable Intelligent Surfaces is presented. The model is based on a transmission-line circuit representation of the RIS which takes into account the physics behind the structure including the effect of all relevant geometrical and electrical parameters. The proposed representation of the RIS allows to take into account the effect of incidence angle, mutual coupling among elements and the effect of the interaction of the periodic surface with the RIS ground plane. It is shown that the proposed approach allows to design a physically realisable RIS without recurring to onerous electromagnetic simulations. The proposed model aims at filling the gap between RIS assisted communications algorithms and physical implementation issues which determine realistic performance of these surfaces.
QoE Optimization for Live Video Streaming in UAV-to-UAV Communications via Deep Reinforcement Learning
Authors: Liyana Adilla binti Burhanuddin, Xiaonan Liu, Yansha Deng, Ursula Challita, Andras Zahemszky
Date: 2021-02-21 00:00:00
Tasks: Deep Reinforcement Learning

A challenge for rescue teams when fighting against wildfire in remote areas is the lack of information, such as the size and images of fire areas. As such, live streaming from Unmanned Aerial Vehicles (UAVs), capturing videos of dynamic fire areas, is crucial for firefighter commanders in any location to monitor the fire situation with quick response. The 5G network is a promising wireless technology to support such scenarios. In this paper, we consider a UAV-to-UAV (U2U) communication scenario, where a UAV at a high altitude acts as a mobile base station (UAV-BS) to stream videos from other flying UAV-users (UAV-UEs) through the uplink. Due to the mobility of the UAV-BS and UAV-UEs, it is important to determine the optimal movements and transmission powers for UAV-BSs and UAV-UEs in real-time, so as to maximize the data rate of video transmission with smoothness and low latency, while mitigating the interference according to the dynamics in fire areas and wireless channel conditions. In this paper, we co-design the video resolution, the movement, and the power control of UAV-BS and UAV-UEs to maximize the Quality of Experience (QoE) of real-time video streaming. To learn the Deep Q-Network (DQN) and Actor-Critic (AC) to maximize the QoE of video transmission from all UAV-UEs to a single UAVBS. Simulation results show the effectiveness of our proposed algorithm in terms of the QoE, delay and video smoothness as compared to the Greedy algorithm.
Inverse design of Raman amplifier in frequency and distance domain using Convolutional Neural Networks
Authors: Mehran Soltani, Francesco Da Ros, Andrea Carena, Darko Zibar
Date: 2021-02-21 00:00:00

We present a Convolutional Neural Network (CNN) architecture for inverse Raman amplifier design. This model aims at finding the pump powers and wavelengths required for a target signal power evolution, both in distance along the fiber and in frequency. Using the proposed framework, the prediction of the pump configuration required to achieve a target power profile is demonstrated numerically with high accuracy in C-band considering both counter-propagating and bidirectional pumping schemes. For a distributed Raman amplifier based on a 100 km single-mode fiber, a low mean set (0.51, 0.54 and 0.64 dB) and standard deviation set (0.62, 0.43 and 0.38 dB) of the maximum test error are obtained numerically employing 2 and 3 counter, and 4 bidirectional propagating pumps, respectively.
Detection of Winding Axial Deformation in Power Transformers by UWB Radar Imaging
Authors: Mohammad S. Golsorkhi, M. A. Hejazi, G. B. Gharepetian, Razieh Mosayebi
Date: 2021-02-21 00:00:00
Tasks: Position

In this paper, a novel method for detecting transformer winding axial displacement has been presented. In this method, which is based on UWB radar imaging, a UWB pulse is transmitted to the transformer winding and the reflection from it is received and recorded. By changing the antenna position this process is repeated on several points along the axis of the winding. The measured signals are mapped to a 2-D image of the winding. By analyzing this image, the axial displacement of the winding can detected and the magnitude of it is determined with an acceptable precision. Simulation results are provided to verify the proposed method.
Detection of Transformer Winding Axial Displacement by Kirchhoff and Delay and sum Radar Imaging Algorithms
Authors: Mohammad S. Golsorkhi, R. Mosayebi, M. A. Hejazi, G. B. Gharehpetian, H. Sheikhzadeh
Date: 2021-02-21 00:00:00

In this paper, a novel method for in detail detection of the winding axial displacement in power transformers based on UWB imaging is presented. In this method, the radar imaging process is implemented on the power transformer by using an ultra-wide band (UWB) transceiver. The result is a 2-D image of the transformer winding, which is analyzed to determine the occurrence as well as the magnitude of the winding axial displacement. The method is implemented on a transformer model. The experimental results illustrate the effectiveness of the proposed method.
Practical graph signal sampling with log-linear size scaling
Authors: Ajinkya Jayawant, Antonio Ortega
Date: 2021-02-21 00:00:00
Tasks: Missing Values

Graph signal sampling is the problem of selecting a subset of representative graph vertices whose values can be used to interpolate missing values on the remaining graph vertices. Optimizing the choice of sampling set using concepts from experiment design can help minimize the effect of noise in the input signal. While many existing sampling set selection methods are computationally intensive because they require an eigendecomposition, existing eigendecompostion-free methods are still much slower than random sampling algorithms for large graphs. In this paper, through optimizing sampling sets towards the D-optimal objective from experiment design, we propose a sampling algorithm that has complexity comparable to random sampling algorithms, while reaching accuracy similar to existing eigendecomposition-free methods for a broad range of graph types.
Dynamic Selective Positioning for High-Precision Accuracy in 5G NR V2X Networks
Authors: Abdurrahman Fouda, Ryan Keating, Amitava Ghosh
Date: 2021-02-20 00:00:00

The capability to achieve high-precision positioning accuracy has been considered as one of the most critical requirements for vehicle-to-everything (V2X) services in the fifth-generation (5G) cellular networks. The non-line-of-sight (NLOS) connectivity, coverage, reliability requirements, the minimum number of available anchors, and bandwidth limitations are among the main challenges to achieve high accuracy in V2X services. This work provides an overview of the potential solutions to provide the new radio (NR) V2X users (UEs) with high positioning accuracy in the future 3GPP releases. In particular, we propose a novel selective positioning solution to dynamically switch between different positioning technologies to improve the overall positioning accuracy in NR V2X services, taking into account the locations of V2X UEs and the accuracy of the collected measurements. Furthermore, we use high-fidelity system-level simulations to evaluate the performance gains of fusing the positioning measurements from different technologies in NR V2X services. Our numerical results show that the proposed hybridized schemes achieve a positioning error $\boldsymbol{\leq}$ 3 m with $\boldsymbol{\approx}$ 76\% availability compared to $\boldsymbol{\approx}$ 55\% availability when traditional positioning methods are used. The numerical results also reveal a potential gain of $\boldsymbol{\approx}$ 56\% after leveraging the road-side units (RSUs) to improve the tail of the UE's positioning error distribution, i.e., worst-case scenarios, in NR V2X services.
Denoising Higher-order Moments for Blind Digital Modulation Identification in Multiple-antenna Systems
Authors: Sofiane Kharbech, Eric Pierre Simon, Akram Belazi, Wei Xiang
Date: 2021-02-20 00:00:00
Tasks: Denoising

The paper proposes a new technique that substantially improves blind digital modulation identification (DMI) algorithms that are based on higher-order statistics (HOS). The proposed technique takes advantage of noise power estimation to make an offset on higher-order moments (HOM), thus getting an estimate of noise-free HOM. When tested for multiple-antenna systems, the proposed method outperforms other DMI algorithms, in terms of identification accuracy, that are based only on cumulants or do not consider HOM denoising, even for a receiver with impairments. The improvement is achieved with the same order of complexity of the common HOS-based DMI algorithms in the same context.
Sparse Array Transceiver Design for Enhanced Adaptive Beamforming in MIMO Radar
Authors: Syed A. Hamza, Weitong Zhai, Xiangrong Wang, Moeness G. Amin
Date: 2021-02-20 00:00:00

Sparse array design aided by emerging fast sensor switching technologies can lower the overall system overhead by reducing the number of expensive transceiver chains. In this paper, we examine the active sparse array design enabling the maximum signal to interference plus noise ratio (MaxSINR) beamforming at the MIMO radar receiver. The proposed approach entails an entwined design, i.e., jointly selecting the optimum transmit and receive sensor locations for accomplishing MaxSINR receive beamforming. Specifically, we consider a co-located multiple-input multiple-output (MIMO) radar platform with orthogonal transmitted waveforms, and examine antenna selections at the transmit and receive arrays. The optimum active sparse array transceiver design problem is formulated as successive convex approximation (SCA) alongside the two-dimensional group sparsity promoting regularization. Several examples are provided to demonstrate the effectiveness of the proposed approach in utilizing the given transmit/receive array aperture and degrees of freedom for achieving MaxSINR beamforming.
Coherent Integration for Targets with Constant Cartesian Velocities Based on Accurate Range Model
Authors: Gongjian Zhou, Zeyu Xu, Yuchao Yang
Date: 2021-02-19 00:00:00

Long-time coherent integration (LTCI) is one of the most important techniques to improve radar detection performance of weak targets. However, for the targets moving with constant Cartesian velocities (CCV), the existing LTCI methods based on polynomial motion models suffer from limited integration time and coverage of target speed due to model mismatch. Here, a novel generalized Radon Fourier transform method for CCV targets is presented, based on the accurate range evolving model, which is a square root of a polynomial with terms up to the second order with target speed as the factor. The accurate model instead of approximate polynomial models used in the proposed method enables effective energy integration on characteristic invariant with feasible computational complexity. The target samplings are collected and the phase fluctuation among pulses is compensated according to the accurate range model. The high order range migration and complex Doppler frequency migration caused by the highly nonlinear signal are eliminated simultaneously. Integration results demonstrate that the proposed method can not only achieve effective coherent integration of CCV targets regardless of target speed and coherent processing interval, but also provide additional observation and resolution in speed domain.
User Subgrouping in Multicast Massive MIMO over Spatially Correlated Rayleigh Fading Channels
Authors: Alejandro de la Fuente, Giovanni Interdonato, Giuseppe Araniti
Date: 2021-02-19 00:00:00
Tasks: Fairness

Massive multiple-input-multiple-output (MaMIMO) multicasting has received significant attention over the last years. MaMIMO is a key enabler of 5G systems to achieve the extremely demanding data rates of upcoming services. Multicast in the physical layer is an efficient way of serving multiple users, simultaneously demanding the same service and sharing radio resources. This work proposes a subgrouping strategy of multicast users based on their spatial channel characteristics to improve the channel estimation and precoding processes. We employ max-min fairness (MMF) power allocation strategy to maximize the minimum spectral efficiency (SE) of the multicast service. Additionally, we explore the combination of spatial multiplexing with orthogonal (time/frequency) multiple access. By varying the number of antennas at the base station (BS) and users' spatial distribution, we also provide the optimal subgroup configuration that maximizes the spectral efficiency per subgroup. Finally, we show that serving the multicast users into two orthogonal time/frequency intervals offers better performance than only relying on spatial multiplexing.
Fading Margins for Large-Scale Antenna Systems
Authors: Jens Abraham, Torbjörn Ekman
Date: 2021-02-19 00:00:00
Tasks: Diversity

Mobile phone operators have begun the roll-out of 5G networks, deploying massive MIMO base stations. Commercial product ranges start with 16 independent radio chains connected to a large-scale antenna system to exploit both channel hardening and favourable propagation in order to obtain increased spectral efficiency. In this work, the cumulative distribution function describing the gain for large-scale antenna systems considering spatial and spectral diversity is evaluated empirically in terms of a fading margin and compared to an analytical maximum diversity reference system. This allows for a simple investigation of the trade-off between deployment size and exploitation of channel hardening. For the considered site-specific measurement data, little additional diversity is harvested with systems larger than 32 antenna elements.
Environment-Aware and Training-Free Beam Alignment for mmWave Massive MIMO via Channel Knowledge Map
Authors: Di wu, Yong Zeng, Shi Jin, Rui Zhang
Date: 2021-02-19 00:00:00

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) communication system is expected to achieve enormous transmission rate, provided that the transmit and receive beams are properly aligned with the MIMO channel. However, existing beam alignment techniques rely on either channel estimation or beam sweeping, which incur prohibitively high training overhead, especially for future wireless systems with further increased antenna dimensions and more stringent requirement on cost-effective hardware architectures. In this paper, we propose a new beam alignment technique, which is environment-aware and training-free, by utilizing the emerging concept of channel knowledge map (CKM), together with the user location information that is readily available in contemporary wireless systems. CKM is a site-specific database, tagged with the transmitter/receiver locations, which contains useful channel information to facilitate or even obviate real-time channel state information (CSI) acquistion. Two instances of CKM are proposed for beam alignment in mmWave massive MIMO systems, namely channel path map (CPM) and beam index map (BIM). It is shown that compared with existing training-based beam alignment schemes, the proposed CKM-enabled environment-aware beam alignment is able to drastically improve the effective communication rate, even with moderate user location errors, thanks to its significant saving of the prohibitive training overhead.
Signal Detection in Distributed MIMO Radar with Non-Orthogonal Waveforms and Sync Errors
Authors: Hongbin Li, Fangzhou Wang, Cengcang Zeng, Mark A. Govoni
Date: 2021-02-19 00:00:00

Although routinely utilized in literature, orthogonal waveforms may lose orthogonality in distributed multi-input multi-output (MIMO) radar with spatially separated transmit (TX) and receive (RX) antennas, as the waveforms may experience distinct delays and Doppler frequency offsets unique to different TX-RX propagation paths. In such cases, the output of each waveform-specific matched filter (MF), employed to unravel the waveforms at the RXs, contains both an \auto term and multiple cross terms, i.e., the filtered response of the desired and, respectively, undesired waveforms. We consider the impact of non-orthogonal waveforms and their cross terms on target detection with or without timing, frequency, and phase errors. To this end, we present a general signal model for distributed MIMO radar, examine target detection using existing coherent/non-coherent detectors and two new detectors, including a hybrid detector that requires phase coherence locally but not across distributed antennas, and provide a statistical analysis leading to closed-form expressions of false alarm and detection probabilities for all detectors. Our results show that cross terms can behave like foes or allies, respectively, if they and the auto term add destructively or constructively, depending on the propagation delay, frequency, and phase offsets. Regarding sync errors, we show that phase errors affect only coherent detectors, frequency errors degrade all but the non-coherent detector, while all are impacted by timing errors, which result in a loss in the signal-to-noise ratio (SNR).
Joint Design of Radar Waveform and Detector via End-to-end Learning with Waveform Constraints
Authors: Wei Jiang, Alexander M. Haimovich, Osvaldo Simeone
Date: 2021-02-19 00:00:00

The problem of data-driven joint design of transmitted waveform and detector in a radar system is addressed in this paper. We propose two novel learning-based approaches to waveform and detector design based on end-to-end training of the radar system. The first approach consists of alternating supervised training of the detector for a fixed waveform and reinforcement learning of the transmitter for a fixed detector. In the second approach, the transmitter and detector are trained simultaneously. Various operational waveform constraints, such as peak-to-average-power ratio (PAR) and spectral compatibility, are incorporated into the design. Unlike traditional radar design methods that rely on rigid mathematical models with limited applicability, it is shown that radar learning can be robustified by training the detector with synthetic data generated from multiple statistical models of the environment. Theoretical considerations and results show that the proposed methods are capable of adapting the transmitted waveform to environmental conditions while satisfying design constraints.