pwc-archive/papers-with-abstracts huggingface dataset:
Mean-$ρ$ portfolio selection and $ρ$-arbitrage for coherent risk measures
Authors: Martin Herdegen, Nazem Khan
Date: 2020-09-11 00:00:00

We revisit mean-risk portfolio selection in a one-period financial market where risk is quantified by a positively homogeneous risk measure $\rho$. We first show that under mild assumptions, the set of optimal portfolios for a fixed return is nonempty and compact. However, unlike in classical mean-variance portfolio selection, it can happen that no efficient portfolios exist. We call this situation $\rho$-arbitrage, and prove that it cannot be excluded -- unless $\rho$ is as conservative as the worst-case risk measure. After providing a primal characterisation of $\rho$-arbitrage, we focus our attention on coherent risk measures that admit a dual representation and give a necessary and sufficient dual characterisation of $\rho$-arbitrage. We show that the absence of $\rho$-arbitrage is intimately linked to the interplay between the set of equivalent martingale measures (EMMs) for the discounted risky assets and the set of absolutely continuous measures in the dual representation of $\rho$. A special case of our result shows that the market does not admit $\rho$-arbitrage for Expected Shortfall at level $\alpha$ if and only if there exists an EMM $\mathbb{Q} \approx \mathbb{P}$ such that $\Vert \frac{\text{d}\mathbb{Q}}{\text{d}\mathbb{P}} \Vert_\infty < \frac{1}{\alpha}$.
When Probabilistic Shaping Realizes Improper Signaling for Hardware Distortion Mitigation
Authors: Sidrah Javed, Ahmed Elzanaty, Osama Amin, Basem Shihada, Mohamed-Slim Alouini
Date: 2020-09-11 00:00:00

Hardware distortions (HWD) render drastic effects on the performance of communication systems. They are recently proven to bear asymmetric signatures; and hence can be efficiently mitigated using improper Gaussian signaling (IGS), thanks to its additional design degrees of freedom. Discrete asymmetric signaling (AS) can practically realize the IGS by shaping the signals' geometry or probability. In this paper, we adopt the probabilistic shaping (PS) instead of uniform symbols to mitigate the impact of HWD and derive the optimal maximum a posterior detector. Then, we design the symbols' probabilities to minimize the error rate performance while accommodating the improper nature of HWD. Although the design problem is a non-convex optimization problem, we simplified it using successive convex programming and propose an iterative algorithm. We further present a hybrid shaping (HS) design to gain the combined benefits of both PS and geometric shaping (GS). Finally, extensive numerical results and Monte-Carlo simulations highlight the superiority of the proposed PS over conventional uniform constellation and GS. Both PS and HS achieve substantial improvements over the traditional uniform constellation and GS with up to one order magnitude in error probability and throughput.
Analytical Voltage Sensitivity Analysis for Unbalanced Power Distribution System
Date: 2020-09-11 00:00:00
Tasks: Sensitivity, valid

Large scale integration of distributed energy resources and electric vehicles
in a transactive energy environment present new challenges in terms of voltage
stability and fluctuations in a power distribution system. The impact of
different level of DER/EV penetration on the voltages across the network is
typically quantified through voltage sensitivity analyses. Existing methods of
voltage sensitivity analysis are computationally expensive and prior efforts to
develop analytical approximation lacks generality and have not been effectively
validated. The objective of this work is to provide a new analytical method of
voltage sensitivity analysis that has low computational cost and also allows
for stochastic analysis of voltage change. This paper first derives an
analytical approximation of change in voltage at a particular bus due to change
in power consumption at other bus in a radial three phase unbalanced power
distribution system. Then, the proposed method is shown to be valid for
different load configurations, which demonstrates its generality. The results
from our analytical approach is validated via classical load flow simulation of
the test system based on IEEE 37 bus network. The proposed method is shown to
have good accuracy, and computation complexity is of order O(1), compared to
O(n3) in classical sensitivity analysis approaches.
RF-Based Low-SNR Classification of UAVs Using Convolutional Neural Networks
Authors: Ender Ozturk, Fatih Erden, Ismail Guvenc
Date: 2020-09-11 00:00:00
Tasks: Drone Controller, General Classification, Time Series, Time Series Analysis

This paper investigates the problem of classification of unmanned aerial vehicles (UAVs) from radio frequency (RF) fingerprints at the low signal-to-noise ratio (SNR) regime. We use convolutional neural networks (CNNs) trained with both RF time-series images and the spectrograms of 15 different off-the-shelf drone controller RF signals. When using time-series signal images, the CNN extracts features from the signal transient and envelope. As the SNR decreases, this approach fails dramatically because the information in the transient is lost in the noise, and the envelope is distorted heavily. In contrast to time-series representation of the RF signals, with spectrograms, it is possible to focus only on the desired frequency interval, i.e., 2.4 GHz ISM band, and filter out any other signal component outside of this band. These advantages provide a notable performance improvement over the time-series signals-based methods. To further increase the classification accuracy of the spectrogram-based CNN, we denoise the spectrogram images by truncating them to a limited spectral density interval. Creating a single model using spectrogram images of noisy signals and tuning the CNN model parameters, we achieve a classification accuracy varying from 92% to 100% for an SNR range from -10 dB to 30 dB, which significantly outperforms the existing approaches to our best knowledge.
Smart Jamming Attacks in 5G New Radio: A Review
Authors: Youness Arjoune, Saleh Faruque
Date: 2020-09-11 00:00:00

The fifth generation of wireless cellular networks (5G) is expected to be the infrastructure for emergency services, natural disasters rescue, public safety, and military communications. 5G, as any previous wireless cellular network, is vulnerable to jamming attacks, which create deliberate interference to hinder the communication of legitimate users. Therefore, jamming 5G networks can be a real threat to public safety. Thus, there is a strong need to investigate to what extent these networks are vulnerable to jamming attacks. For this investigation, we consider the 3GPP standard released in 2017, which is widely accepted as the primary reference for the deployment of these networks. First, we describe the key elements of 5G New Radio (NR) architecture, such as different channels and signals exchanged between the base station and user equipment. Second, we conduct an in-depth review of the jamming attack models and we assess the 5G NR vulnerabilities to these jamming attacks. Then, we present the state-of-the-art detection and mitigation techniques, and we discuss their suitability to defeat smart jammers in 5G wireless networks. Finally, we provide some recommendations and future research directions at the end of this paper.
Deep Learning Interference Cancellation in Wireless Networks
Authors: Yiming Zhou, Ashkan Samiee, Tingyi Zhou, Bahram Jalali
Date: 2020-09-11 00:00:00
Tasks: Deep Learning, Reinforcement Learning (RL)

With the crowding of the electromagnetic spectrum and the shrinking cell size in wireless networks, crosstalk between base stations and users is a major problem. Although hand-crafted functional blocks and coding schemes are proven effective to guarantee reliable data transfer, currently deep learning-based approaches have drawn increasing attention in the communication system modeling. In this paper, we propose a Neural Network (NN) based signal processing technique that works with traditional DSP algorithms to overcome the interference problem in realtime. This technique doesn't require any feedback protocol between the receiver and transmitter which makes it very suitable for low-latency and high data-rate applications such as autonomy and augmented reality. While there has been recent work on the use of Reinforcement Learning (RL) in the control layer to manage and control the interference, our approach is novel in the sense that it introduces a neural network for signal processing at baseband data rate and in the physical layer. We demonstrate this "Deep Interference Cancellation" technique using a convolutional LSTM autoencoder. When applied to QAM-OFDM modulated data, the network produces significant improvement in the symbol error rate (SER). We further discuss the hardware implementation including latency, power consumption, memory requirements, and chip area.
Deep Analog-to-Digital Converter for Wireless Communication
Authors: Ashkan Samiee, Yiming Zhou, Tingyi Zhou, Bahram Jalali
Date: 2020-09-11 00:00:00

With the advent of the 5G wireless networks, achieving tens of gigabits per second throughputs and low, milliseconds, latency has become a reality. This level of performance will fuel numerous real-time applications, such as autonomy and augmented reality, where the computationally heavy tasks can be performed in the cloud. The increase in the bandwidth along with the use of dense constellations places a significant burden on the speed and accuracy of analog-to-digital converters (ADC). A popular approach to create wideband ADCs is utilizing multiple channels each operating at a lower speed in the time-interleaved fashion. However, an interleaved ADC comes with its own set of challenges. The parallel architecture is very sensitive to the inter-channel mismatch, timing jitter, clock skew between different ADC channels as well as the nonlinearity within individual channels. Consequently, complex post-calibration is required using digital signal processing (DSP) after the ADC. The traditional DSP calibration consumes a significant amount of power and its design requires knowledge of the source and type of errors which are becoming increasingly difficult to predict in nanometer CMOS processes. In this paper, instead of individually targeting each source of error, we utilize a deep learning algorithm to learn the complete and complex ADC behavior and to compensate for it in realtime. We demonstrate this "Deep ADC" technique on an 8G Sample/s 8-channel time-interleaved ADC with the QAM-OFDM modulated data. Simulation results for different QAM symbol constellations and OFDM subcarriers show dramatic improvements of approximately 5 bits in the dynamic range with a concomitant drastic reduction in symbol error rate. We further discuss the hardware implementation including latency, power consumption, memory requirements, and chip area.
Covid-19 impact on cryptocurrencies: evidence from a wavelet-based Hurst exponent
Authors: M. Belén Arouxet, Aurelio F. Bariviera, Verónica E. Pastor, Victoria Vampa
Date: 2020-09-11 00:00:00
Tasks: Time Series, Time Series Analysis

Cryptocurrency history begins in 2008 as a means of payment proposal. However, cryptocurrencies evolved into complex, high yield speculative assets. Contrary to traditional financial instruments, they are not (mostly) traded in organized, law-abiding venues, but on online platforms, where anonymity reigns. This paper examines the long term memory in return and volatility, using high frequency time series of eleven important coins. Our study covers the pre-Covid-19 and the subsequent pandemia period. We use a recently developed method, based on the wavelet transform, which provides more robust estimators of the Hurst exponent. We detect that, during the peak of Covid-19 pandemic (around March 2020), the long memory of returns was only mildly affected. However, volatility suffered a temporary impact in its long range correlation structure. Our results could be of interest for both academics and practitioners.
Reinforcement Learning for Optimal Primary Frequency Control: A Lyapunov Approach
Authors: Wenqi Cui, Yan Jiang, Baosen Zhang
Date: 2020-09-11 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

As more inverter-connected renewable resources are integrated into the grid, frequency stability may degrade because of the reduction in mechanical inertia and damping. A common approach to mitigate this degradation in performance is to use the power electronic interfaces of the renewable resources for primary frequency control. Since inverter-connected resources can realize almost arbitrary responses to frequency changes, they are not limited to reproducing the linear droop behaviors. To fully leverage their capabilities, reinforcement learning (RL) has emerged as a popular method to design nonlinear controllers to optimize a host of objective functions. Because both inverter-connected resources and synchronous generators would be a significant part of the grid in the near and intermediate future, the learned controller of the former should be stabilizing with respect to the nonlinear dynamics of the latter. To overcome this challenge, we explicitly engineer the structure of neural network-based controllers such that they guarantee system stability by construction, through the use of a Lyapunov function. A recurrent neural network architecture is used to efficiently train the controllers. The resulting controllers only use local information and outperform optimal linear droop as well as other state-of-the-art learning approaches.
Preamble-Based Packet Detection in Wi-Fi: A Deep Learning Approach
Authors: Vukan Ninkovic, Dejan Vukobratovic, Aleksandar Valka, Dejan Dumic
Date: 2020-09-12 00:00:00

Wi-Fi systems based on the family of IEEE 802.11 standards that operate in unlicenced bands are the most popular wireless interfaces that use Listen Before Talk (LBT) methodology for channel access. Distinctive feature of majority of LBT-based systems is that the transmitters use preambles that precede the data to allow the receivers to acquire initial signal detection and synchronization. The first digital processing step at the receiver applied over the incoming discrete-time complex-baseband samples after analog-to-digital conversion is the packet detection step, i.e., the detection of the initial samples of each of the frames arriving within the incoming stream. Since the preambles usually contain repetitions of training symbols with good correlation properties, conventional digital receivers apply correlation-based methods for packet detection. Following the recent interest in data-based deep learning (DL) methods for physical layer signal processing, in this paper, we challenge the conventional methods with DL-based approach for Wi-Fi packet detection. Using one-dimensional Convolutional Neural Networks (1D-CNN), we present a detailed complexity vs performance analysis and comparison between conventional and DL-based Wi-Fi packet detection approaches.
Decentralized Model-free Loss Minimization in Distribution Grids with the Use of Inverters
Authors: Ilgiz Murzakhanov, Spyros Chatzivasileiadis
Date: 2020-09-12 00:00:00

Distribution grids are experiencing a massive penetration of fluctuating distributed energy resources (DERs). As a result, the real-time efficient and secure operation of distribution grids becomes a paramount problem. While installing smart sensors and enhancing communication infrastructure improves grid observability, it is computationally impossible for the distribution system operator (DSO) to optimize setpoints of millions of DER units. This paper proposes communication-free and model-free algorithms that can actively control converter-connected devices, and can operate either as stand-alone or in combination with centralized optimization algorithms. We address the problem of loss minimization in distribution grids, and we analytically prove that our proposed algorithms reduce the total grid losses without any prior information about the network, requiring no communication, and based only on local measurements. Going a step further, we combine our proposed local algorithms with a central optimization of a very limited number of converters. The hybrid approaches we propose have much lower communication and computation requirement than traditional methods, while they also provide performance guarantees in case of communication failure. We demonstrate our algorithms in four networks of varying sizes: a 5-bus network, an IEEE 141-bus system, a real Danish distribution system, and a meshed IEEE 30-bus system.
Identifying Grey-box Thermal Models with Bayesian Neural Networks
Date: 2020-09-13 00:00:00
Tasks: Time Series Analysis

Smart thermostats are one of the most prevalent home automation products.
They learn occupant preferences and schedules, and utilize an accurate thermal
model to reduce the energy use of heating and cooling equipment while
maintaining the temperature for maximum comfort. Despite the importance of
having an accurate thermal model for the operation of smart thermostats, fast
and reliable identification of this model is still an open problem. In this
paper, we explore various techniques for establishing a suitable thermal model
using time series data generated by smart thermostats. We show that Bayesian
neural networks can be used to estimate parameters of a grey-box thermal model
if sufficient training data is available, and this model outperforms several
black-box models in terms of the temperature prediction accuracy. Leveraging
real data from 8,884 homes equipped with smart thermostats, we discuss how the
prior knowledge about the model parameters can be utilized to quickly build an
accurate thermal model for another home with similar floor area and age in the
same climate zone. Moreover, we investigate how to adapt the model originally
built for the same home in another season using a small amount of data
collected in this season. Our results confirm that maintaining only a small
number of pre-trained thermal models will suffice to quickly build accurate
thermal models for many other homes, and that 1~day smart thermostat data could
significantly improve the accuracy of transferred models in another season.
Inertia and feedback parameters adaptive control of virtual synchronous generator
Date: 2020-09-13 00:00:00

The virtual synchronous generator technology analogs the characteristics of
the synchronous generator via the controller design. It improved the stability
of the grid systems which include the new energy. At the same time, according
to the adjustable characteristics of the virtual synchronous generator
parameters, the parameter adaptive adjustment is used to improve the dynamic
performance of the system. However, the traditional virtual synchronous
generator adaptive control technology still has two drawbacks: on the one hand,
the large-scale adjustment of the damping droop coefficient and the virtual
moment of inertia requires the system having a high energy storage margin; On
the other hand, there is a power overshoot phenomenon in the transient
regulation process, which is disadvantageous to the power equipment. First,
this paper provides a convenient adjustment method for improving the transient
stability of the system, the system damping is adjusted by introducing the
output speed feedback. Second, according to the transient power-angle
characteristics of the system, a parameter adaptive control strategy is
proposed, which shortens the transient adjustment time and ensures that the
deviation of the system frequency in the transient adjustment process is within
the allowable range, and improves the transient performance of the grid
frequency adjustment, at the same time, the power overshoot is suppressed.
Finally, the experimental results show that the proposed control strategy is
superior to the existing adaptive control strategy.
Beamforming Optimization for MIMO Wireless Power Transfer with Nonlinear Energy Harvesting: RF Combining versus DC Combining
Authors: Shanpu Shen, Bruno Clerckx
Date: 2020-09-13 00:00:00

In this paper, we study the multiple-input and multiple-output (MIMO) wireless power transfer (WPT) system so as to enhance the output DC power of the rectennas. To that end, we revisit the rectenna nonlinearity considering multiple receive antennas. Two combining schemes for multiple rectennas at the receiver, DC and RF combinings, are modeled and analyzed. For DC combining, we optimize the transmit beamforming, adaptive to the channel state information (CSI), so as to maximize the total output DC power. For RF combining, we compute a closed-form solution of the optimal transmit and receive beamforming. In addition, we propose a practical RF combining circuit using RF phase shifter and RF power combiner and also optimize the analog receive beamforming adaptive to CSI. We also analytically derive the scaling laws of the output DC power as a function of the number of transmit and receive antennas. Those scaling laws confirm the benefits of using multiple antennas at the transmitter or receiver. They also highlight that RF combining significantly outperforms DC combining since it leverages the rectenna nonlinearity more efficiently. Two types of performance evaluations, based on the nonlinear rectenna model and based on realistic and accurate rectenna circuit simulations, are provided. The evaluations demonstrate that the output DC power can be linearly increased by using multiple rectennas at the receiver and that the relative gain of RF combining versus DC combining in terms of the output DC power level is very significant, of the order of 240% in a one-transmit antenna ten-receive antenna setup.
Multimodal Medical Image registration using Discrete Wavelet Transform and Gaussian Pyramids
Date: 2020-09-13 00:00:00
Tasks: Image Registration, Medical Image Registration

In this research paper, authors propose multimodal brain image registration
using discrete wavelet transform(DWT) followed by Gaussian pyramids. The
reference and target images are decomposed into their LL, LH, HL and LL DWT
coefficients and then are processed for image registration using Gaussian
pyramids. The image registration is also done using Gaussian pyramids only and
wavelets transforms only for comparison. The quality of registration is
measured by comparing the maximum MI values used by the three methods and also
by comparing their correlation coefficients. Our proposed technique proves to
show better results when compared with the other two methods.
Energy Efficient Power Allocation in Massive MIMO NOMA Systems Based on SIF Using Cell Division Technique
Authors: Abdolrasoul Sakhaei Gharagezlou, Jafar Pourrostam, Mahdi Nangir, Mir Mahdi Safari
Date: 2020-09-13 00:00:00

In this paper, we investigate energy-efficient power allocation for the downlink of the massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) systems. In our proposed scheme, we divide a cell into two zones. The first area is for users whose distance from the base station (BS) is less than half of the radius of cell and the second area is for users whose distance from BS is more than half of the radius of cell. Based on distance of users from BS and the number of users in each area, we dedicate an amount of power for each user. We also use standard interference function (SIF) to propose a new iterative algorithm to solve the optimization problem and obtain the optimal power allocation scheme. Simulation results show that the proposed algorithm outperforms other algorithms from the energy efficiency (EE) point of view.