pwc-archive/papers-with-abstracts huggingface dataset:
Cost-Effective Quasi-Parallel Sensing Instrumentation for Industrial Chemical Species Tomography
Authors: Godwin Enemali, Rui Zhang, Hugh McCann, Chang Liu
Date: 2020-11-20 00:00:00
Tasks: Image Reconstruction

Chemical Species Tomography (CST) has been widely applied for imaging of critical gas-phase parameters in industrial processes. To acquire high-fidelity images, CST is typically implemented by line-of-sight Wavelength Modulation Spectroscopy (WMS) measurements from multiple laser beams. The modulated transmission signal on each laser beam needs to be a) digitised by a high-speed analogue-to-digital converter (ADC); b) demodulated by a digital lock-in (DLI) module; and c) transferred to high-level processor for image reconstruction. Although a fully parallel data acquisition (DAQ) and signal processing system can achieve these functionalities with maximised temporal response, it leads to a highly complex, expensive and power-consuming instrumentation system with high potential for inconsistency between the sampled beams due to the electronics alone. In addition, the huge amount of spectral data sampled in parallel significantly burdens the communication process in industrial applications where in situ signal digitisation is distanced from the high-level data processing. To address these issues, a quasi-parallel sensing technique and electronic circuits were developed for industrial CST, in which the digitisation and demodulation of the multi-beam transmission signals are multiplexed over the high-frequency modulation within a wavelength scan. Our development not only maintains the temporal response of the fully parallel sensing scheme, but also facilitates the cost-effective implementation of industrial CST with very low complexity and reduced load on data transfer. The proposed technique is analytically proven, numerically examined by noise-contaminated CST simulations, and experimentally validated using a lab-scale CST system with 32 laser beams.
Linearization for High-Speed Current-Steering DACs Using Neural Networks
Authors: Daniel Beauchamp, Keith M. Chugg
Date: 2020-11-20 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This paper proposes a novel foreground linearization scheme for a high-speed CS-DAC. The technique leverages neural networks (NNs) to derive a LUT that maps the inverse of the DAC transfer characteristic onto the input codes. The algorithm is shown to improve conventional methods by at least 6dB in terms of intermodulation (IM) performance for frequencies up to 9GHz on a state-of-the-art 10-bit CS-DAC operating at 40.96GS/s (gigasamples-per-second) in 14nm CMOS.
Power Adaptation for Vector Parameter Estimation according to Fisher Information based Optimality Criteria
Authors: Doğa Gürgünoğlu, Berkan Dulek, Sinan Gezici
Date: 2020-11-20 00:00:00
Tasks: parameter estimation

The optimal power adaptation problem is investigated for vector parameter estimation according to various Fisher information based optimality criteria. By considering an observation model that involves a linear transformation of the parameter vector and an additive noise component with an arbitrary probability distribution, six different optimal power allocation problems are formulated based on Fisher information based objective functions. Via optimization theoretic approaches, various closed-form solutions are derived for the proposed problems. Also, the results are extended to cases in which nuisance parameters exist in the system model or certain types of nonlinear transformations are applied on the parameter vector. Numerical examples are presented to investigate performance of the proposed power allocation strategies.
Delay Constrained Buffer-Aided Relay Selection in the Internet of Things with Decision-Assisted Reinforcement Learning
Authors: Chong Huang, Gaojie Chen, Yu Gong
Date: 2020-11-20 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper investigates the reinforcement learning for the relay selection in the delay-constrained buffer-aided networks. The buffer-aided relay selection significantly improves the outage performance but often at the price of higher latency. On the other hand, modern communication systems such as the Internet of Things often have strict requirement on the latency. It is thus necessary to find relay selection policies to achieve good throughput performance in the buffer-aided relay network while stratifying the delay constraint. With the buffers employed at the relays and delay constraints imposed on the data transmission, obtaining the best relay selection becomes a complicated high-dimensional problem, making it hard for the reinforcement learning to converge. In this paper, we propose the novel decision-assisted deep reinforcement learning to improve the convergence. This is achieved by exploring the a-priori information from the buffer-aided relay system. The proposed approaches can achieve high throughput subject to delay constraints. Extensive simulation results are provided to verify the proposed algorithms.
A Remote Carrier Synchronization Technique for Coherent Distributed Remote Sensing Systems
Authors: Juan Merlano Duncan, Liz Martinez-Marrero, Jorge Querol, Sumit Kumar, Adriano Camps, Symeon Chatzinotas, Bjorn Ottersten
Date: 2020-11-20 00:00:00

Phase, frequency, and time synchronization are crucial requirements for many applications, such as multi-static remote sensing and communication systems. Moreover, the synchronization solution becomes even more challenging when the nodes are orbiting or flying on airborne or spaceborne platforms. This paper compares the available technologies used for the synchronization and coordination of nodes in distributed remote sensing applications. Additionally, this paper proposes a general system model and identifies preliminary guidelines and critical elements for implementing the synchronization mechanisms exploiting the inter-satellite communication link. The distributed phase synchronization loop introduced in this work deals with the self-interference in a full-duplex point to point scenario by transmitting two carriers at each node. All carriers appear with different frequency offsets around a central frequency, called the application central-frequency or the beamforming frequency. This work includes a detailed analysis of the proposed algorithm and the required simulations to verify its performance for different phase noise, AWGN, and Doppler shift scenarios.
Data-Driven Modulation and Antenna Classification of Wireless Digital Communication Signals
Authors: Apostolos Pappas, Antonios Argyriou
Date: 2020-11-20 00:00:00
Tasks: Classification, General Classification

In this paper we are interested to learn from a wireless digitally modulated signal the number of antennas that the transmitter (Tx) of this signal uses, as well as its specific modulation scheme (from phase-shift keying (PSK) or quadrature amplitude modulation (QAM)). Formally, these are modulation and antenna classification problems. We examine the problems with data-driven machine learning (ML)-based techniques. The two sub-problems of modulation and number of transmitter antenna classification are initially examined independently for a variety for system parameters, namely the SNR, number of receiver (Rx) antennas, and classification algorithms. Then we consider the joint problem where we follow two approaches. One, where the sub-problems are solved independently and in parallel, and one where the antenna classifier waits on the result of the modulation classifier. The two proposed schemes do not require any knowledge/details of the used modulation schemes and the way the Tx antennas are used (spatial multiplexing, space-time codes,etc.) as it is fully data-driven and not decision-theoretic based. The results of our approach are characterized by high classification accuracy and they pave the way for more ML-based data-driven techniques that reveal more characteristics of the Tx.
Improvement of accuracy for measurement of 100-km fibre latency with Correlation OTDR
Authors: Florian Azendorf, Annika Dochhan, Jim Zou, Bernhard Schmauss, Michael Eiselt
Date: 2020-11-20 00:00:00

We measured the latency of a 100 km fibre link using a Correlation OTDR. Improvements over previous results were achieved by increasing the probe signal rate to 10 Gbit/s, using dispersion compensation gratings, and coupling the receiver time base to an external PPS signal.
Deep Learning Applied to Beamforming in Synthetic Aperture Ultrasound
Authors: Nissim Peretz, Arie Feuer
Date: 2020-11-20 00:00:00
Tasks: Deep Learning

Deep learning methods can be found in many medical imaging applications. Recently, those methods were applied directly to the RF ultrasound multi-channel data to enhance the quality of the reconstructed images. In this paper, we apply a deep neural network to medical ultrasound imaging in the beamforming stage. Specifically, we train the network using simulated multi-channel data from two arrays with different sizes, using a variety of direction of arrival (DOA) angles, and test its generalization performance on real cardiac data. We demonstrate that our method can be used to improve image quality over standard methods, both in terms of resolution and contrast. Alternatively, it can be used to reduce the number of elements in the array, while maintaining the image quality. The utility of our method is demonstrated on both simulated and real data.
Flight Sensor Data and Beamforming based Integrated UAV Tracking with Channel Estimation using Gaussian Process Regression
Authors: Ha-Lim Song, Young-Chai Ko
Date: 2020-11-20 00:00:00
Tasks: GPR, Position, regression

With explosively increasing demands for unmanned aerial vehicle (UAV) applications, reliable link acquisition for serving UAVs is required. Considering the dynamic characteristics of UAV, it is hugely challenging to persist a reliable link without beam misalignment. In this paper, we propose a flight sensor data and beamforming signal based integrated UAV tracking scheme to deal with this problem. The proposed scheme provides a compatible integrated system considering the practical specification of the flight sensor data and the beamforming pilot signal. The UAV position tracking is comprised of two steps: 1) UAV position prediction by the flight sensor data and 2) position update with the beamforming signal using Gaussian process regression (GPR) method, which is a nonparametric machine learning. The flight sensor data can assist ground station (GS) or UAV nodes in designing the precoding and the receive beamforming matrix with drastically reduced overheads. The beamforming signal can accomplish high beamforming gain to be maintained even when the flight sensor data is absent. Therefore, the proposed scheme can support the moving target continuously by utilizing these two signals. The simulation results are provided to confirm that the proposed scheme outperforms other conventional beam tracking schemes. We also derive 3-dimensional (3D) beamforming gain and spectral efficiency (SE) from the mean absolute error (MAE) of the angular value estimation, which can be used as beamforming performance metrics of the data transmission link in advance.
Full-Duplex Non-Orthogonal Multiple Access Cooperative Overlay Spectrum-Sharing Networks with SWIPT
Authors: Quang Nhat Le, Animesh Yadav, Nam-Phong Nguyen, Octavia A. Dobre, Ruiqin Zhao
Date: 2020-11-19 00:00:00
Tasks: STS

This paper proposes a novel non-orthogonal multiple access (NOMA) assisted cooperative spectrum sharing network, in which one of the full-duplex (FD) secondary transmitters (STs) is chosen among many for forwarding the primary transmitter's and its own information to primary receiver and secondary receivers, respectively, using NOMA technique. To stimulate the ST to conduct cooperative transmission and sustain its operations, the simultaneous wireless information and power transfer (SWIPT) technique is utilized by the ST to harvest the primary signal's energy. In order to evaluate the proposed system's performance, the outage probability and system throughput for the primary and secondary networks are derived in tight closed-form approximations. Further, the sum rate optimization problem is formulated for the proposed cooperative network and a rapid convergent iterative algorithm is proposed to obtain the optimized power allocation coefficients. Numerical results show that FD, SWIPT, and NOMA techniques greatly boost the performance of cooperative spectrum-sharing network in terms of outage probability, system throughput, and sum rate compared to that of half-duplex NOMA and the conventional orthogonal multiple access-time division multiple access networks.
A Joint Wireless-Optical Front-haul Solution for Multi-user Massive MIMO 5G RAN
Authors: Son T. Le, Stefan Wesemann, Roman Dischler, Sivarama Venkatesan
Date: 2020-11-18 00:00:00

We demonstrate a high capacity IF-over-fiber mobile fronthaul solution for multi-user massive MIMO 5G RAN. Using this scheme, a record aggregated radio bandwidth of 25.6 GHz was transmitted on a single optical wavelength over 40 km without fiber chromatic dispersion compensation
Towards Assistive Diagnoses in m-Health: A Gray-box Neural Model for Cerebral Autoregulation Index
Authors: Jorge Cuevas, Claudio Henriquez, Francisco Cruz
Date: 2020-11-17 00:00:00

The cerebral autoregulation system (CAS), is a mechanism which aims to regulate pressure variations occurring in the cerebral circulatory system. At present, there only exist invasive methods and, in turn, they are not used to prevent cerebrovascular accidents. Nowadays, the emergent concept of m-Health allows to use mobile devices to assist the cerebral autoregulation index (ARI). For this, it is necessary to find novel models which allow to approximate the ARI by using the blood pressure value. This work proposes a gray-box neural model to find a relation between the arterial blood pressure (ABP) and the cerebral blood flow velocity (CBFV) in order to obtain the ARI. Preliminary results show good performance by using a phenomenological model in comparison to the Aaslid-Tiecks model.
Optimizing Age of Information Through Aerial Reconfigurable Intelligent Surfaces: A Deep Reinforcement Learning Approach
Authors: Moataz Samir, Mohamed Elhattab, Chadi Assi, Sanaa Sharafeddine, Ali Ghrayeb
Date: 2020-11-09 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning (RL)

We investigate the benefits of integrating unmanned aerial vehicles (UAVs) with reconfigurable intelligent surface (RIS) elements to passively relay information sampled by Internet of Things devices (IoTDs) to the base station (BS). In order to maintain the freshness of relayed information, an optimization problem with the objective of minimizing the expected sum Age-of-Information (AoI) is formulated to optimize the altitude of the UAV, the communication schedule, and phases-shift of RIS elements. In the absence of prior knowledge of the activation pattern of the IoTDs, proximal policy optimization algorithm is developed to solve this mixed-integer non-convex optimization problem. Numerical results show that our proposed algorithm outperforms all others in terms of AoI.
96 dB Linear High Dynamic Range CAOS Spectrometer Demonstration
Authors: Mohsin A. Mazhar, Nabeel A. Riza
Date: 2020-11-09 00:00:00
Tasks: Vocal Bursts Intensity Prediction

For the first time, a CAOS (i.e., Coded Access Optical Sensor) spectrometer is demonstrated. The design implemented uses a reflective diffraction grating and a time-frequency CAOS mode operations Digital Micromirror Device (DMD) in combination with a large area point photo-detector to enable highly programmable linear High Dynamic Range (HDR) spectrometry. Experiments are conducted with a 2850 K color temperature light bulb source and visible band color bandpass and high-pass filters as well as neutral density (ND) attenuation filters. A ~369 nm to ~715 nm input light source spectrum is measured with a designed ~1 nm spectral resolution. Using the optical filters and different CAOS modes, namely, Code Division Multiple Access (CDMA), Frequency Modulation (FM)-CDMA and FM-Time Division Multiple Access (TDMA) modes, measured are improving spectrometer linear dynamic ranges of 28 dB, 50 dB, and 96.2 dB, respectively. Applications for the linear HDR CAOS spectrometer includes materials inspection in biomedicine, foods, forensics, and pharmaceuticals.
A General Framework for Airplane Air-to-Ground Communications in mmWave and Microwave Bands
Authors: Ararat Shaverdian, Shahram Shahsavari, Catherine Rosenberg
Date: 2020-11-09 00:00:00

Airplane sensors and on-board equipment collect an increasingly large amount of maintenance data during flights that are used for airplane maintenance. We propose to download part of the data during airplane's descent via a cellular base station (BS) located at the airport. We formulate and solve an offline optimization problem to quantify how much data can be offloaded in a non-dedicated band while ensuring that the interference at the terrestrial BSs in the vicinity of the airport remains below a maximum allowable threshold. Our problem allows for adaptive tuning of transmit power, number of frequency channels to be used, and beamforming according to the position of the plane on the descent path. Our results show that during the last 5 minutes of descent, in the microwave band the plane can offload up to 5 GB of maintenance data in a 20~MHz band, while in the mmWave band the plane can offload up to 24 times more data in a 1~GHz band. Beamforming, power and bandwidth tuning are all crucial in maintaining a good performance in the mmWave band while in the microwave band, dynamic tuning of bandwidth does not improve the performance much.
A Review of Deep Reinforcement Learning for Smart Building Energy Management
Authors: Liang Yu, Shuqi Qin, Meng Zhang, Chao Shen, Tao Jiang, Xiaohong Guan
Date: 2020-08-12 00:00:00
Tasks: Deep Reinforcement Learning, energy management, Management, reinforcement-learning, Reinforcement Learning (RL)

Global buildings account for about 30% of the total energy consumption and carbon emission, raising severe energy and environmental concerns. Therefore, it is significant and urgent to develop novel smart building energy management (SBEM) technologies for the advance of energy-efficient and green buildings. However, it is a nontrivial task due to the following challenges. Firstly, it is generally difficult to develop an explicit building thermal dynamics model that is both accurate and efficient enough for building control. Secondly, there are many uncertain system parameters (e.g., renewable generation output, outdoor temperature, and the number of occupants). Thirdly, there are many spatially and temporally coupled operational constraints. Fourthly, building energy optimization problems can not be solved in real-time by traditional methods when they have extremely large solution spaces. Fifthly, traditional building energy management methods have respective applicable premises, which means that they have low versatility when confronted with varying building environments. With the rapid development of Internet of Things technology and computation capability, artificial intelligence technology find its significant competence in control and optimization. As a general artificial intelligence technology, deep reinforcement learning (DRL) is promising to address the above challenges. Notably, the recent years have seen the surge of DRL for SBEM. However, there lacks a systematic overview of different DRL methods for SBEM. To fill the gap, this paper provides a comprehensive review of DRL for SBEM from the perspective of system scale. In particular, we identify the existing unresolved issues and point out possible future research directions.
Energy Efficient Power Allocation in Massive MIMO Systems with Power Limited Users
Authors: Abdolrasoul Sakhaei Gharagezlou, Mahdi Nangir, Nima Imani, Erfan Mirhosseini
Date: 2020-11-09 00:00:00

In this paper, we investigate energy-efficient (EE) power allocation (PA) for a special downlink scenario of the massive multiple-input multiple-output (MIMO) systems. We consider a minimum power required for each user to ensure that the quality of service (QoS) for each user is satisfied. In this method, a comparison between the sum of minimum power required by users and the maximum transmission power is done to determine whether maximizing EE is possible or not. If the sum of the minimum power required by users is less than the maximum transmission power, we maximize EE. Otherwise, the number of admitted users in a cluster is maximized. In both cases, the simulation results show that the proposed algorithm has better performance than similar algorithms.
Numerology Selection for OFDM Systems Based on Deep Neural Networks
Authors: Xiaoran Liu, Jiao Zhang, Jibo Wei
Date: 2020-11-09 00:00:00

In order to support diverse scenarios and deployments, the numerology of orthogonal frequency division multiplexing (OFDM) is defined for the parametrization of subcarrier spacing and cyclic prefix (CP). The time-frequency dispersion of mobile radio channels and the channel noise result in different performance deterioration in different numerologies. In this letter, we propose a deep neutral network (DNN) approach for numerology selection of OFDM systems. Considering the inter-symbol interference (ISI), inter-carrier interference (ICI) and noise level, the SNR loss is established as the objective to be minimized. We extract the power delay profile, mobile velocity and noise power as the input features to the DNN. The proposed DNN learns from the channel characteristics to obtain the optimal numerology selection. Simulation results show that the proposed DNN achieves better performance than the existing methods. The decision boundaries of different numerologies are also illustrated to show the application range according to the channel characteristics.
Deep Modulation Recognition with Multiple Receive Antennas: An End-to-end Feature Learning Approach
Authors: Lei LI, Qihang Peng, Jun Wang
Date: 2020-08-15 00:00:00

Modulation recognition using deep neural networks has shown promising advantages over conventional algorithms. However, most existing research focuses on single receive antenna. In this paper, two end-to-end feature learning deep architectures are introduced for modulation recognition with multiple receive antennas. The first is based on multi-view convolutional neural network by treating signals from different receive antennas as different views of a 3D object and designing the location and operation of view-pooling layer that are suitable for feature fusion of multi-antenna signals. Considering that the instantaneous SNRs could be different among receive antennas in wireless communications, we further propose weight-learning convolutional neural network which uses a weight-learning module to automatically learn the weights for feature combing of different receive antennas to perform end-to-end feature learning of multi-antenna signals. Results show that both end-to-end feature learning deep architectures outperform the existing algorithm, and the proposed weight-learning convolutional neural network achieves the best performance.
The Trajectory PHD Filter for Jump Markov System Models and Its Gaussian Mixture Implementation
Authors: Boxiang Zhang, Wei Yi
Date: 2020-08-10 00:00:00

The trajectory probability hypothesis density filter (TPHD) is capable of producing trajectory estimates in first principle without adding labels or tags. In this paper, we propose a new TPHD filter referred as MM-TPHD for jump Markov system (JMS) model that the highly dynamic targets movement switches between multiple models in multi-trajectory tracking. Firstly, we extend the concept of JMS to the multi-trajectory scenario of maneuvering target and derive the TPHD recursion for the proposed JMS model. Then, we develop the linear Gaussian Mixture (LGM) implementation of MM-TPHD recursion and also consider the L-scan computationally efficient implementations. Finally, simulation results in maneuvering multi-trajectory tracking demonstrate the performance of the proposed algorithm.