pwc-archive/papers-with-abstracts huggingface dataset:
Soft Attention Convolutional Neural Networks for Rare Event Detection in Sequences
Authors: Mandar Kulkarni, Aria Abubakar
Date: 2020-11-02 00:00:00
Tasks: Event Detection, Position

Automated event detection in the sequences is an important aspect of temporal data analytics. The events can be in the form of peaks, changes in data distribution, changes of spectral characteristics etc. In this work, we propose a Soft-Attention Convolutional Neural Network (CNN) based approach for rare event detection in sequences. For the purpose of demonstration, we experiment with well logs where we aim to detect events depicting the changes in the geological layers (a.k.a. well tops/markers). Well logs (single or multivariate) are inputted to a soft attention CNN and a model is trained to locate the marker position. Attention mechanism enables the machine to relatively scale the relevant log features for the task. Experimental results show that our approach is able to locate the rare events with high precision.
Deep Reinforcement Learning in Electricity Generation Investment for the Minimization of Long-Term Carbon Emissions and Electricity Costs
Authors: Alexander J. M. Kell, Pablo Salas, Jean-Francois Mercure, Matthew Forshaw, A. Stephen McGough
Date: 2020-11-02 00:00:00
Tasks: Deep Reinforcement Learning

A change from a high-carbon emitting electricity power system to one based on renewables would aid in the mitigation of climate change. Decarbonization of the electricity grid would allow for low-carbon heating, cooling and transport. Investments in renewable energy must be made over a long time horizon to maximise return of investment of these long life power generators. Over these long time horizons, there exist multiple uncertainties, for example in future electricity demand and costs to consumers and investors. To mitigate for imperfect information of the future, we use the deep deterministic policy gradient (DDPG) deep reinforcement learning approach to optimize for a low-cost, low-carbon electricity supply using a modified version of the FTT:Power model. In this work, we model the UK and Ireland electricity markets. The DDPG algorithm is able to learn the optimum electricity mix through experience and achieves this between the years of 2017 and 2050. We find that a change from fossil fuels and nuclear power to renewables, based upon wind, solar and wave would provide a cheap and low-carbon alternative to fossil fuels.
A $0.11-0.38$ pJ/cycle Differential Ring Oscillator in $65$ nm CMOS for Robust Neurocomputing
Authors: Xueyong Zhang, Jyotibdha Acharya, Arindam Basu
Date: 2020-11-02 00:00:00

This paper presents a low-area and low-power consumption CMOS differential current controlled oscillator (CCO) for neuromorphic applications. The oscillation frequency is improved over the conventional one by reducing the number of MOS transistors thus lowering the load capacitor in each stage. The analysis shows that for the same power consumption, the oscillation frequency can be increased about $11\%$ compared with the conventional one without degrading the phase noise. Alternatively, the power consumption can be reduced $15\%$ at the same frequency. The prototype structures are fabricated in a standard $65$ nm CMOS technology and measurements demonstrate that the proposed CCO operates from $0.7-1.2$ V supply with maximum frequencies of $80$ MHz and energy/cycle ranging from $0.11-0.38$ pJ over the tuning range. Further, system level simulations show that the nonlinearity in current-frequency conversion by the CCO does not affect its use as a neuron in a Deep Neural Network if accounted for during training.
Power Allocation for Fingerprint-Based PHY-Layer Authentication with mmWave UAV Networks
Authors: Sung Joon Maeng, Yavuz Yapıcı, İsmail Güvenç, Huaiyu Dai, Arupjyoti Bhuyan
Date: 2020-11-02 00:00:00
Tasks: TAG

Physical layer security (PLS) techniques can help to protect wireless networks from eavesdropper attacks. In this paper, we consider the authentication technique that uses fingerprint embedding to defend 5G cellular networks with unmanned aerial vehicle (UAV) systems from eavesdroppers and intruders. Since the millimeter wave (mmWave) cellular networks use narrow and directional beams, PLS can take further advantage of the 3D spatial dimension for improving the authentication of UAV users. Considering a multi-user mmWave cellular network, we propose a power allocation technique that jointly takes into account splitting of the transmit power between the precoder and the authentication tag, which manages both the secrecy as well as the achievable rate. Our results show that we can obtain optimal achievable rate with expected secrecy.
3D Near-Field Millimeter-Wave Synthetic Aperture Radar Imaging
Authors: Shahrokh Hamidi, Safieddin Safavi-Naeini
Date: 2020-11-01 00:00:00

In this paper, we present 3D high resolution radar imaging process at millimeter-Wave (mmWave) frequencies by creating the effect of a large aperture synthetically. We use a low cost fully integrated Frequency Modulated Continuous Wave (FMCW) radar operating at $\rm 79\;GHz$ and then perform Synthetic Aperture Radar (SAR) imaging in the near-filed zone. At the end, we conduct a real experiment and present the reconstructed image.
Joint Tracking of Multiple Beams in Beamspace MIMO Systems
Authors: Liza Afeef, Murat Karabacak, Hüseyin Arslan
Date: 2020-11-01 00:00:00

In millimeter-wave (mmWave) systems, beamforming is needed to overcome harsh channel environments. As a promising beamforming solution, lens antenna array (LAA) implementation can provide a cost-effective solution without notable performance degradation compared to its counterpart. However, an appropriate beam selection is a challenge since it requires efficient channel estimation via an extensive beam training process for perfect beam alignment. In this paper, we propose a high mobility beam and channel tracking algorithm based on the unscented Kalman filter (UKF) to address this challenge, where the channel changes can be monitored over a certain time. The proposed algorithm tracks the channel changes after establishing a connection with an appropriate beam. The algorithm is first introduced in a multi-user beamspace multiple-input multiple-output (MIMO) system with LAA where a single beam is tracked at the user side at downlink transmission. Then, it is employed for multi-beam joint-tracking at the base station side in the uplink transmission. The analysis indicates that under different channel variation conditions, the proposed algorithm outmatches the popular extended Kalman filter (EKF) in both single-beam and multi-beam tracking systems. While it is common to individually track the beams in a multi-beam system scenario, the proposed joint tracking approach can provide around 62% performance enhancement compared to individual beam tracking using the conventional EKF method.
A novel receiver design for energy packet-based dispatching
Authors: Friedrich Wiegel, Edoardo De Din, Antonello Monti, Klaus Wehrle, Marc Hiller, Martina Zitterbart, Veit Hagenmeyer
Date: 2020-11-01 00:00:00

A steadily growing share of renewable energies with uctuating and decentralized generation as well as rising peak loads require novel solutions to ensure the reliability of electricity supply. More speci cally, grid stability is endangered by equally relevant line constraints and battery capacity limits. In this light, energy packet-based dispatching with power signal dual modulation has recently been introduced as an innovative solution. However, this approach assumes a central synchronicity provision unit for energy packet dispatching. In order to overcome this assumption, the present paper's main contribution is a design of an energy packet receiver which recovers the required synchronicity information directly from the received signal itself. Key implementation aspects are discussed in detail. By means of a DC grid example, simulation results show the performance and applicability of the proposed novel receiver for packet-based energy dispatching.
D2D Assisted Multi-antenna Coded Caching
Authors: Hamidreza Bakhshzad Mahmoodi, Jarkko Kaleva, Seyed Pooya Shariatpanahi, Antti Tolli
Date: 2020-10-09 00:00:00

A device-to-device (D2D) aided multi-antenna coded caching scheme is proposed to improve the average delivery rate and reduce the downlink (DL) beamforming complexity.} Novel beamforming and resource allocation schemes are proposed where local data exchange among nearby users is exploited. The transmission is split into two phases: local D2D content exchange and DL transmission. In the D2D phase, subsets of users are selected to share content with the adjacent users directly. {In this regard, a low complexity D2D mode selection algorithm is proposed to find the appropriate set of users for the D2D phase with comparable performance to the optimal exhaustive search. {During} the DL phase, the base station multicasts the remaining data requested by all the users. We identify scenarios and conditions where D2D transmission can reduce the delivery time. Furthermore, we demonstrate how} adding the new D2D phase to the DL-only scenario can significantly reduce the beamformer design complexity in the DL phase. The results further highlight that by partly delivering requested data in the D2D phase, the transmission rate can be boosted due to more efficient use of resources during the subsequent DL phase. As a result, the overall content delivery performance is greatly enhanced, especially in the finite signal-to-noise (SNR) regime.
Deep Learning Enabled Semantic Communication Systems
Authors: Huiqiang Xie, Zhijin Qin, Geoffrey Ye Li, Biing-Hwang Juang
Date: 2020-06-18 00:00:00
Tasks: Deep Learning, Semantic Communication, Sentence, Sentence Similarity, Transfer Learning

Recently, deep learned enabled end-to-end (E2E) communication systems have been developed to merge all physical layer blocks in the traditional communication systems, which make joint transceiver optimization possible. Powered by deep learning, natural language processing (NLP) has achieved great success in analyzing and understanding large amounts of language texts. Inspired by research results in both areas, we aim to providing a new view on communication systems from the semantic level. Particularly, we propose a deep learning based semantic communication system, named DeepSC, for text transmission. Based on the Transformer, the DeepSC aims at maximizing the system capacity and minimizing the semantic errors by recovering the meaning of sentences, rather than bit- or symbol-errors in traditional communications. Moreover, transfer learning is used to ensure the DeepSC applicable to different communication environments and to accelerate the model training process. To justify the performance of semantic communications accurately, we also initialize a new metric, named sentence similarity. Compared with the traditional communication system without considering semantic information exchange, the proposed DeepSC is more robust to channel variation and is able to achieve better performance, especially in the low signal-to-noise (SNR) regime, as demonstrated by the extensive simulation results.
Multipath-Enhanced Device-Free Localization in Wideband Wireless Networks
Authors: Martin Schmidhammer, Christian Gentner, Stephan Sand, Uwe-Carsten Fiebig
Date: 2020-10-09 00:00:00

State-of-the-art device-free localization systems infer presence and location of users based on received signal strength measurements of line-of-sight links in wireless networks. In this letter, we propose to enhance device-free localization systems by exploiting multipath propagation between the individual network nodes. Particularly indoors, wireless propagation channels are characterized by multipath propagation, i.e., received signals comprise multipath components due to reflection and scattering. Given prior information about the surrounding environment, e.g., a floor plan, the individual propagation paths of multipath components can be derived geometrically. Inherently, these propagation paths differ spatially from the line-of-sight propagation path and can be considered as additional links in the wireless network. This extended network determines the novel multipath-enhanced device-free localization system. Using theoretical performance bounds on the localization error, we show that including multipath components into device-free localization systems improves the overall localization performance and extends the effective observation area significantly.
Weak Radio Frequency Signal Detection Based on Piezo-Opto-Electro-Mechanical System: Architecture Design and Sensitivity Prediction
Authors: Shanchi Wu, Chen Gong, Chengjie Zuo, Shangbin Li, Junyu Zhang, Zhongbin Dai, Kai Yang, Ming Zhao, Rui Ni, Zhengyuan Xu, Jinkang Zhu
Date: 2020-03-29 00:00:00
Tasks: Sensitivity

We propose a novel radio-frequency (RF) receiving architecture based on micro-electro-mechanical system (MEMS) and optical coherent detection module. The architecture converts the received electrical signal into mechanical vibration through the piezoelectric effect and adopts an optical detection module to detect the mechanical vibration. We analyze the response function of piezoelectric film to an RF signal, the noise limited sensitivity of the optical detection module and the system transfer function in the frequency domain. Finally, we adopt simple on-off keying (OOK) modulation with bandwidth 1 kHz and carrier frequency 1 GHz, to numerically evaluate the detection sensitivity. The result shows that, considering the main noise sources in wireless channel and circuits, the signal detection sensitivity can reach around -160 dBm with a 50 $\Omega$ impedance. Such sensitivity significantly outperforms that of the currently deployed Long Term Evolution (LTE) system, when normalizing the transmission bandwidth also to 1 kHz.
Collision Prediction from UWB Range Measurements
Authors: Alemayehu Solomon Abrar, Anh Luong, Gregory Spencer, Nathan Genstein, Neal Patwari, Mark Minor
Date: 2020-10-09 00:00:00
Tasks: Prediction

The ability to predict, and thus react to, oncoming collisions among a set of mobile agents is a fundamental requirement for safe autonomous movement, both human and robotic. This paper addresses systems that use range measurements between mobile agents for the purpose of collision prediction, which involves prediction of the agents' future paths to know if they will collide at any time. One straightforward system would use known-location static anchors to estimate agent coordinates over time, and use the track to predict collision. Fundamentally, no fixed coordinate system is required for collision prediction, so using only the pairwise range between two agents can be used to predict collision. We present lower bound analysis which shows the limitations of this pairwise method. As an alternative anchor-free method, we propose the friend-based autonomous collision prediction and tracking (FACT) method that uses all measured ranges between nearby (unknown location mobile) agents, in a distributed algorithm, to estimate their relative locations and velocities and predict future collisions between agents. Using analysis and simulation, we show the potential for FACT to achieve equal or better collision detection performance compared to other methods, while avoiding the need for anchors. We then build a network of $N$ ultra wideband (UWB) devices and an efficient multi-node protocol which allows all ${\cal O}(N^2)$ pairwise ranges to be measured in $N$ slots. We run experiments with up to six independent robot agents moving and colliding in a 2D plane and up to four anchor nodes to compare the performance of the collision prediction methods. We show that the FACT method can perform better than either other method but without the need for a fixed infrastructure of anchor nodes.
Band Assignment in Ultra-Narrowband (UNB) Systems for Massive IoT Access
Authors: Enes Krijestorac, Ghaith Hattab, Petar Popovski, Danijela Cabric
Date: 2020-10-09 00:00:00

In this work, we consider a novel type of Internet of Things (IoT) ultra-narrowband (UNB) network architecture that involves multiple multiplexing bands or channels for uplink transmission. An IoT device can randomly choose any of the multiplexing bands and transmit its packet. Due to hardware constraints, a base station (BS) is able to listen to only one multiplexing band. The hardware constraint is mainly due to the complexity of performing fast Fourier transform (FFT) at a very small sampling interval over the multiplexing bands in order to counter the uncertainty of IoT device frequency and synchronize onto transmissions. The objective is to find an assignment of BSs to multiplexing bands in order to maximize the packet decoding probability (PDP). We develop a learning-based algorithm based on a sub-optimal solution to PDP maximization. The simulation results show that our approach to band assignment achieves near-optimal performance in terms of PDP, while at the same time, significantly exceeding the performance of random assignment. We also develop a heuristic algorithm with no learning overhead based on the locations of the BSs that also outperforms random assignment and serves as a performance reference to our learning-based algorithm.
Nonlinear Quasi-Synchronous Multi User Chirp Spread Spectrum Signaling
Authors: Nozhan Hosseini, David W. Matolak
Date: 2019-09-21 00:00:00

Multi user orthogonal chirp spread spectrum (OCSS) can improve the spectral inefficiency of chirp spread spectrum (CSS) but is only feasible with perfect synchronism and without any channel dispersion. Asynchronism, channel dispersion, or unexpectedly large Doppler shifts can cause multiple access interference (MAI), which degrades performance. Conditions with small timing offsets we term quasi-synchronous (QS). In this paper, we propose two new sets of nonlinear chirps to improve CSS system performance in QS conditions. We analytically and numerically evaluate cross-correlation distributions. We also derive the bit error probability for Binary CSS analytically and validate our theoretical result with both numerical and simulation results; our error probability expression is applicable to any binary time-frequency (TF) chirp waveform. Finally, we show that in QS conditions our two new nonlinear chirp designs outperform the classical linear chirp and all existing nonlinear chirps from the literature. To complete our analysis, we demonstrate that our nonlinear CSS designs outperform existing chirps in two realistic (empirically modeled) dispersive air to ground channels.
Empirical Fourier Decomposition: An Accurate Adaptive Signal Decomposition Method
Authors: Wei Zhou, Zhongren Feng, Y. F. Xu, Xiongjiang Wang, Hao Lv
Date: 2020-09-17 00:00:00
Tasks: Computational Efficiency

Signal decomposition is an effective tool to assist the identification of modal information in time-domain signals. Two signal decomposition methods, including the empirical wavelet transform (EWT) and Fourier decomposition method (FDM), have been developed based on Fourier theory. However, the EWT can suffer from a mode mixing problem for signals with closely-spaced modes and decomposition results by FDM can suffer from an inconsistency problem. An accurate adaptive signal decomposition method, called the empirical Fourier decomposition (EFD), is proposed to solve the problems in this work. The proposed EFD combines the uses of an improved Fourier spectrum segmentation technique and an ideal filter bank. The segmentation technique can solve the inconsistency problem by predefining the number of modes in a signal to be decomposed and filter functions in the ideal filter bank have no transition phases, which can solve the mode mixing problem. Numerical investigations are conducted to study the accuracy of the EFD. It is shown that the EFD can yield accurate and consistent decomposition results for signals with multiple non-stationary modes and those with closely-spaced modes, compared with decomposition results by the EWT, FDM, variational mode decomposition and empirical mode decomposition. It is also shown that the EFD can yield accurate time-frequency representation results and it has the highest computational efficiency among the compared methods.
Array Resource Allocation for Radar and Communication Integration Network
Authors: Zhenkai Zhang, Hamid Esmaeili Najafabadi, Henry Leung
Date: 2020-10-07 00:00:00

A radar and communication integration (RCI) system has great flexibility in allocating antenna resources to guarantee both radar and communication performance. This paper considers the array allocation problems for multiple target localization and multiple platforms communication in an RCI network. The objective of array allocation is to maximize the communication capacity for each channel and to minimize the localization error for each target. In this paper, we firstly build a localization and communication model for array allocation in an RCI network. Minorization maximization (MM) is then applied to create surrogate functions for multiple objective optimization problems. The projected gradient descent (PGD) method is further employed to solve two array allocation problems with and without a certain communication capacity constraint. Computer simulations are conducted to evaluate the performance of the proposed algorithms. The results show that the proposed algorithms have improved localization and communication performance after efficiently allocating the array resource in the RCI network.
Designing Sequence with Minimum PSL Using Chebyshev Distance and its Application for Chaotic MIMO Radar Waveform Design
Authors: Hamid Esmaeili Najafabadi, Mohammad Ataei, Mohamad F. Sabahi
Date: 2020-10-07 00:00:00
Tasks: Radar waveform design

Controlling peak side-lobe level (PSL) is of great importance in high-resolution applications of multiple-input multiple-output (MIMO) radars. In this paper, designing sequences with good autocorrelation properties are studied. The PSL of the autocorrelation is regarded as the main merit and is optimized through newly introduced cyclic algorithms, namely; PSL Minimization Quadratic Approach (PMQA), PSL Minimization Algorithm, the smallest Rectangular (PMAR), and PSL Optimization Cyclic Algorithm (POCA). It is revealed that minimizing PSL results in better sequences in terms of autocorrelation side-lobes when compared with traditional integrated side-lobe level (ISL) minimization. In order to improve the performance of these algorithms, fast-randomized Singular Value Decomposition (SVD) is utilized. To achieve waveform design for MIMO radars, this algorithm is applied to the waveform generated from a modified Bernoulli chaotic system. The numerical experiments confirm the superiority of the newly developed algorithms compared to high-performance algorithms in mono-static and MIMO radars.
3D beamforming and handover analysis for UAV networks
Authors: Achiel Colpaert, Evgenii Vinogradov, Sofie Pollin
Date: 2020-10-07 00:00:00

In future drone applications fast moving unmanned aerial vehicles (UAVs) will need to be connected via a high throughput ultra reliable wireless link. MmWave communication is assumed to be a promising technology for UAV communication, as the narrow beams cause little interference to and from the ground. A challenge for such networks is the beamforming requirement, and the fact that frequent handovers are required as the cells are small. In the UAV communication research community, mobility and especially handovers are often neglected, however when considering beamforming, antenna array sizes start to matter and the effect of azimuth and elevation should be studied, especially their impact on handover rate and outage capacity. This paper aims to fill some of this knowledge gap and to shed some light on the existing problems. This work will analyse the performance of 3D beamforming and handovers for UAV networks through a case study of a realistic 5G deployment using mmWave. We will look at the performance of a UAV flying over a city utilizing a beamformed mmWave link.