pwc-archive/papers-with-abstracts huggingface dataset:
Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems
Authors: Pedro J. Freire, Yevhenii Osadchuk, Bernhard Spinnler, Antonio Napoli, Wolfgang Schairer, Nelson Costa, Jaroslaw E. Prilepsky, Sergei K. Turitsyn
Date: 2021-03-15 00:00:00

We present the results of the comparative analysis of the performance versus complexity for several types of artificial neural networks (NNs) used for nonlinear channel equalization in coherent optical communication systems. The comparison has been carried out using an experimental set-up with transmission dominated by the Kerr nonlinearity and component imperfections. For the first time, we investigate the application to the channel equalization of the convolution layer (CNN) in combination with a bidirectional long short-term memory (biLSTM) layer and the design combining CNN with a multi-layer perceptron. Their performance is compared with the one delivered by the previously proposed NN equalizer models: one biLSTM layer, three-dense-layer perceptron, and the echo state network. Importantly, all architectures have been initially optimized by a Bayesian optimizer. We present the derivation of the computational complexity associated with each NN type -- in terms of real multiplications per symbol so that these results can be applied to a large number of communication systems. We demonstrated that in the specific considered experimental system the convolutional layer coupled with the biLSTM (CNN+biLSTM) provides the highest Q-factor improvement compared to the reference linear chromatic dispersion compensation (2.9 dB improvement). We examine the trade-off between the computational complexity and performance of all equalizers and demonstrate that the CNN+biLSTM is the best option when the computational complexity is not constrained, while when we restrict the complexity to lower levels, the three-layer perceptron provides the best performance. Our complexity analysis for different NNs is generic and can be applied in a wide range of physical and engineering systems.
Echo State Network based Symbol Detection in Chaotic Baseband Wireless Communication
Authors: Hui-Ping Yin, Chao Bai, Hai-Peng Ren
Date: 2021-03-15 00:00:00

In some Internet of Things (IoT) applications, multi-path propagation is a main constraint of communication channel. Recently, the chaotic baseband wireless communication system (CBWCS) is promising to eliminate the inter-symbol interference (ISI) caused by multipath propagation. However, the current technique is only capable of removing the partial effect of ISI, due to only past decoded bits are available for the suboptimal decoding threshold calculation. However, the future transmitting bits also contribute to the threshold. The unavailable future information bits needed by the optimal decoding threshold are an obstacle to further improve the bit error rate (BER) performance. Different from the previous method using echo state network (ESN) to predict one future information bit, the proposed method in this paper predicts the optimal threshold directly using ESN. The proposed ESN-based threshold prediction method simplifies the symbol decoding operation by removing the threshold calculation from the transmitting symbols and channel information, which achieves better BER performance as compared to the previous method. The reason for this superior result lies in two folds, first, the proposed ESN is capable of using more future symbols information conveyed by the ESN input to get more accurate threshold; second, the proposed method here does not need to estimate the channel information using Least Square method, which avoids the extra error caused by inaccurate channel information estimation. By this way, the calculation complexity is decreased as compared to the previous method. Simulation results and experiment based on a wireless open-access research platform under a practical wireless channel, show the effectiveness and superiority of the proposed method.
Direct Symbol Decoding using GA-SVM in Chaotic Baseband Wireless Communication System
Authors: Hui-Ping Yin, Hai-Peng Ren
Date: 2021-03-15 00:00:00
Tasks: Binary Classification

To retrieve the information from the serious distorted received signal is the key challenge of communication signal processing. The chaotic baseband communication promises theoretically to eliminate the inter-symbol interference (ISI), however, it needs complicated calculation, if it is not impossible. In this paper, a genetic algorithm support vector machine (GA-SVM) based symbol detection method is proposed for chaotic baseband wireless communication system (CBWCS), by this way, treating the problem from a different viewpoint, the symbol decoding process is converted to be a binary classification through GA-SVM model. A trained GA-SVM model is used to decode the symbols directly at the receiver, so as to improve the bit error rate (BER) performance of the CBWCS and simplify the symbol detection process by removing the channel identification and the threshold calculation process as compared to that using the calculated threshold to decode symbol in the traditional methods. The simulation results show that the proposed method has better BER performance in both the static and time-varying wireless channels. The experimental results, based on the wireless open-access research platform, indicate that the BER of the proposed GA-SVM based symbol detection approach is superior to the other counterparts under a practical wireless multipath channel.
Pilot-tone assisted 16-QAM photonic wireless bridge operating at 250 GHz
Authors: Luis Gonzalez-Guerrero, Haymen Shams, Irshaad Fatadin, John Edward Wu, Martyn J. Fice, Mira Naftaly, Alwyn J. Seeds, Cyril C. Renaud
Date: 2019-06-25 00:00:00

A photonic wireless bridge operating at a carrier frequency of 250 GHz is proposed and demonstrated. To mitigate the phase noise of the free-running lasers present in such a link, the tone-assisted carrier recovery is used. Compared to the blind phase noise compensation (PNC) algorithm, this technique exhibited penalties of 0.15 dB and 0.46 dB when used with aggregated Lorentzian linewidths of 28 kHz and 359 kHz, respectively, and 20 GBd 16-quadrature amplitude modulation (QAM) signals. The wireless bridge is also demonstrated in a wavelength division multiplexing (WDM) scenario, where 5 optical channels are generated and sent to the Tx remote antenna unit (RAU). In this configuration, the full band from 224 GHz to 294 GHz is used. Finally, a 50 Gbit/s transmission is achieved with the proposed wireless bridge in single channel configuration. The wireless transmission distance is limited to 10 cm due to the low power emitted by the uni-travelling carrier photodiode used in the experiments. However, link budget calculations based on state-of-the-art THz technology show that distances >1000 m can be achieved with this approach.
Heterogeneity in Neuronal Calcium Spike Trains based on Empirical Distance
Authors: Sathish Ande, Jayanth R Regatti, Neha Pandey, Ajith Karunarathne, Lopamudra Giri, Soumya Jana
Date: 2021-03-13 00:00:00
Tasks: Clustering

Statistical similarities between neuronal spike trains could reveal significant information on complex underlying processing. In general, the similarity between synchronous spike trains is somewhat easy to identify. However, the similar patterns also potentially appear in an asynchronous manner. However, existing methods for their identification tend to converge slowly, and cannot be applied to short sequences. In response, we propose Hellinger distance measure based on empirical probabilities, which we show to be as accurate as existing techniques, yet faster to converge for synthetic as well as experimental spike trains. Further, we cluster pairs of neuronal spike trains based on statistical similarities and found two non-overlapping classes, which could indicate functional similarities in neurons. Significantly, our technique detected functional heterogeneity in pairs of neuronal responses with the same performance as existing techniques, while exhibiting faster convergence. We expect the proposed method to facilitate large-scale studies of functional clustering, especially involving short sequences, which would in turn identify signatures of various diseases in terms of clustering patterns.
Efficient Precoding for Single Carrier Modulation in Multi-User Massive MIMO Networks
Authors: Brent A. Kenney, Arslan J. Majid, Hussein Moradi, Behrouz Farhang-Boroujeny
Date: 2021-03-13 00:00:00

By processing in the frequency domain (FD), massive MIMO systems can approach the theoretical per-user capacity using a single carrier modulation (SCM) waveform with a cyclic prefix. Minimum mean squared error (MMSE) detection and zero forcing (ZF) precoding have been shown to effectively cancel multi-user interference while compensating for inter-symbol interference. In this paper, we present a modified downlink precoding approach in the FD based on regularized zero forcing (RZF), which reuses the matrix inverses calculated as part of the FD MMSE uplink detection. By reusing these calculations, the computational complexity of the RZF precoder is drastically lowered, compared to the ZF precoder. Introduction of the regularization in RZF leads to a bias in the detected data symbols at the user terminals. We show this bias can be removed by incorporating a scaling factor at the receiver. Furthermore, it is noted that user powers have to be optimized to strike a balance between noise and interference seen at each user terminal. The resulting performance of the RZF precoder exceeds that of the ZF precoder for low and moderate input signal-to-noise ratio (SNR) conditions, and performance is equal for high input SNR. These results are established and confirmed by analysis and simulation.
Efficient Android Based Invisible Broken Wire Detector
Authors: V. Jegathesan, T. Jemima Jebaseeli, D. Jasmine David
Date: 2021-03-13 00:00:00

The system to include an android based underground broken wire detection which works by detecting the electromagnetic field around a live underground cable is proposed. The transmission cables undergo stress and strain as they are under the ground. This may lead to short circuits or various kinds of snapping in the wire. If these faults are not treated, it may cause an interruption in the power supply and permanent damage. The proposed method distinguishes the short circuit fault in the underground links. The existing and traditional techniques for detection are reviewed and only the methods for spotting the short circuit error are included. Thus the proposed system provides a cost-efficient way of detecting the short circuit shortcomings in underground cables.
Signal Processing Techniques to Reduce the Limit of Detection for Thin Film Biosensors
Authors: Simon J. Ward, Rabeb Layouni, Sofia Arshavsky-Graham, Ester Segal, Sharon M. Weiss
Date: 2021-03-12 00:00:00

The ultimate detection limit of optical biosensors is often limited by various noise sources, including those introduced by the optical measurement setup. While sophisticated modifications to instrumentation may reduce noise, a simpler approach that can benefit all sensor platforms is the application of signal processing to minimize the deleterious effects of noise. In this work, we show that applying complex Morlet wavelet convolution to Fabry-P\'erot interference fringes characteristic of thin film reflectometric biosensors effectively filters out white noise and low frequency reflectance variations. Subsequent calculation of an average difference in phase between the filtered analyte and reference signals enables a significant reduction in the limit of detection (LOD) enabling closer competition with current state-of-the-art techniques. This method is applied on experimental data sets of thin film porous silicon sensors (PSi) in buffered solution and complex media obtained from two different laboratories. The demonstrated improvement in LOD achieved using wavelet convolution and average phase difference paves the way for PSi optical biosensors to operate with clinically relevant detection limits for medical diagnostics, environmental monitoring, and food safety.
Low-complexity graph-based traveling wave models for HVDC grids with hybrid transmission lines: Application to fault identification
Authors: Michel Kieffer, Paul Verrax, Nathan Alglave, Alberto Bertinato, Bertrand Raison
Date: 2021-03-12 00:00:00

The fast protection of meshed HVDC grids requires the modeling of the transient phenomena affecting the grid after a fault. In the case of hybrid lines comprising both overhead and underground parts, the numerous generated traveling waves may be difficult to describe and evaluate. This paper proposes a representation of the grid as a graph, allowing to take into account any waves traveling through the grid. A relatively compact description of the waves is then derived, based on a combined physical and behavioral modeling approach. The obtained model depends explicitly on the characteristics of the grid as well as on the fault parameters. An application of the model to the identification of the faulty portion of an hybrid line is proposed. The knowledge of the faulty portion is profitable as faults in overhead lines, generally temporary, can lead to the reclosing of the line.
Two Mirroring And Interpolating Methods To Estimate Peak Position For Symmetric Signals With Single Peak
Authors: Wei Chen
Date: 2021-03-12 00:00:00
Tasks: Position

Signals with single peak and symmetry property are very common in various fields, such as probability density function of normal distribution. Among the information contained in such signals, peak position is the most important, sometimes even the only parameter concerned. Current methods for peak position estimation are always with low precision and bad noise resistance, perform badly for sparse spectrum. This manuscript proposes two new algorithms that take advantage of symmetry property, conduct mirroring and interpolating operations to condense signal spectrum. From tests done in this paper, these two algorithms indicate outstanding advantages compared with current methods.
An Adaptive Receiver for Underwater Acoustic Full-Duplex Communication with Joint Tracking of the Remote and Self-Interference Channels
Authors: Mohammad Towliat, Zheng Guo, Leonard J. Cimini, Xiang-Gen Xia, Aijun Song
Date: 2021-03-11 00:00:00

Full-duplex (FD) communication is a promising candidate to address the data rate limitations in underwater acoustic (UWA) channels. Because of transmission at the same time and on the same frequency band, the signal from the local transmitter creates self-interference (SI) that contaminates the signal from the remote transmitter. At the local receiver, channel state information for both the SI and remote channels is required to remove the SI and equalize the SI-free signal, respectively. However, because of the rapid time-variations of the UWA environment, real-time tracking of the channels is necessary. In this paper, we propose a receiver for UWA-FD communication in which the variations of the SI and remote channels are jointly tracked by using a recursive least squares (RLS) algorithm fed by feedback from the previously detected data symbols. Because of the joint channel estimation, SI cancellation is more successful compared to UWA-FD receivers with separate channel estimators. In addition, due to providing a real-time channel tracking without the need for frequent training sequences, the bandwidth efficiency is preserved in the proposed receiver.
Heuristics for optimizing 3D mapping missions over swarm-powered ad hoc clouds
Authors: Leandro R. Costa, Daniel Aloise, Luca G. Gianoli, Andrea Lodi
Date: 2021-03-11 00:00:00
Tasks: 3D Reconstruction, Humanitarian

Drones have been getting more and more popular in many economy sectors. Both scientific and industrial communities aim at making the impact of drones even more disruptive by empowering collaborative autonomous behaviors -- also known as swarming behaviors -- within fleets of multiple drones. In swarming-powered 3D mapping missions, unmanned aerial vehicles typically collect the aerial pictures of the target area whereas the 3D reconstruction process is performed in a centralized manner. However, such approaches do not leverage computational and storage resources from the swarm members.We address the optimization of a swarm-powered distributed 3D mapping mission for a real-life humanitarian emergency response application through the exploitation of a swarm-powered ad hoc cloud. Producing the relevant 3D maps in a timely manner, even when the cloud connectivity is not available, is crucial to increase the chances of success of the operation. In this work, we present a mathematical programming heuristic based on decomposition and a variable neighborhood search heuristic to minimize the completion time of the 3D reconstruction process necessary in such missions. Our computational results reveal that the proposed heuristics either quickly reach optimality or improve the best known solutions for almost all tested realistic instances comprising up to 1000 images and fifteen drones.
Development of Multifractal Models for Self-Similar Traffic Flows
Authors: G. Millán, G. Lefranc
Date: 2021-03-11 00:00:00

This paper presents a simple technique of multifractal traffic modeling. It proposes a method of fitting model to a given traffic trace. A comparison of simulation results obtained for an exemplary trace, multifractal model and Markov Modulated Poisson Process models has been performed.
Age of Information Optimization in a RIS-Assisted Wireless Network
Authors: Ali Muhammad, Mohamed Elhattab, Mohamed Amine Arfaoui, Ahmed Al-Hilo, Chadi Assi
Date: 2021-03-11 00:00:00
Tasks: Bilevel Optimization, Deep Reinforcement Learning, Edge-computing, Scheduling

In this paper, we consider a wireless network consisting of a base station (BS) that is serving multiple real-time traffic streams forwarding information updates to their destinations in order to sustain the freshness of information for time-critical applications. Since the wireless channels may be unreliable due to the impurities of the propagation environments, such as deep fading, blockages, etc., we integrate a reconfigurable intelligent surface (RIS) to the wireless system in order to mitigate the propagation-induced impairments, enhance the quality of the wireless links, and ensure that the required freshness of information is achieved for these real-time applications. For this network set-up, we investigate the joint optimization of the traffic streams scheduling and the RIS phase-shift matrix with the goal of minimizing the long-term average Age of Information (AoI). The formulated optimization problem is a mixed-integer non-convex optimization problem, which is difficult to solve. To circumvent the high-coupled optimization variables, and with the aid of bilevel optimization, we decompose the original problem into an outer traffic stream scheduling problem and an inner RIS phase-shift matrix problem. For the outer problem, owing to its complexity and stochastic nature of packet arrivals, we resort to deep reinforcement learning (DRL) solution where the traffic stream scheduling is modeled as a Markov Decision Process (MDP), and Proximal Policy Optimization (PPO) is invoked to solve it. Whereas, the inner problem that determines the RIS configuration is solved through semi-definite relaxation (SDR). Finally, we show through extensive simulations that our approach evaluates the combined impact of scheduling policy and RIS configuration on the long-term average AoI, where we demonstrate its superiority against other baseline schemes.
A High-Throughput Multi-Mode LDPC Decoder for 5G NR
Authors: Sina Pourjabar, Gwan S. Choi
Date: 2021-02-25 00:00:00
Tasks: Decoder, Vocal Bursts Intensity Prediction

This paper presents a partially parallel low-density parity-check (LDPC) decoder designed for the 5G New Radio (NR) standard. The design is using a multi-block parallel architecture with a flooding schedule. The decoder can support any code rates and code lengths up to the lifting size Zmax= 96. To compensate for the dropped throughput associated with the smaller Z values, the design can double and quadruple its parallelism when lifting sizes Z<= 48 and Z<= 24 are selected respectively. Therefore, the decoder can process up to eight frames and restore the throughput to the maximum. To simplify the design's architecture, a new variable node for decoding the extended parity bits present in the lower code rates is proposed. The FPGA implementation of the decoder results in a throughput of 2.1 Gbps decoding the 11/12 code rate. Additionally, the synthesized decoder using the 28 nm TSMC technology, achieves a maximum clock frequency of 526 MHz and a throughput of 13.46 Gbps. The core decoder occupies 1.03 mm2, and the power consumption is 229 mW.
Adaptive Subband Compression for Streaming of Continuous Point-on-Wave and PMU Data
Authors: Xinyi Wang, Yilu Liu, Lang Tong
Date: 2020-08-23 00:00:00
Tasks: Data Compression

A data compression system capable of providing real-time streaming of high-resolution continuous point-on-wave (CPOW) and phasor measurement unit (PMU) measurements is proposed. Referred to as adaptive subband compression (ASBC), the proposed technique partitions the signal space into subbands and adaptively compresses subband signals based on each subband's active bandwidth. The proposed technique conforms to existing industry phasor measurement standards, making it suitable for streaming high-resolution CPOW and PMU data either in continuous or burst on-demand/event-triggered modes. Experiments on synthetic and real data show that ASBC reduces the CPOW sampling rates by several orders of magnitude for real-time streaming while maintaining the precision required by industry standards.
UAV Swarm-Enabled Aerial Reconfigurable Intelligent Surface
Authors: Bodong Shang, Rubayet Shafin, Lingjia Liu
Date: 2021-03-10 00:00:00

Reconfigurable intelligent surface (RIS) offers tremendous spectrum and energy efficiency in wireless networks by adjusting the amplitudes and/or phases of passive reflecting elements to optimize signal reflection. With the agility and mobility of unmanned aerial vehicles (UAVs), RIS can be mounted on UAVs to enable three-dimensional signal reflection. Compared to the conventional terrestrial RIS (TRIS), the aerial RIS (ARIS) enjoys higher deployment flexibility, reliable air-to-ground links, and panoramic full-angle reflection. However, due to UAV's limited payload and battery capacity, it is difficult for a UAV to carry a RIS with a large number of reflecting elements. Thus, the scalability of the aperture gain could not be guaranteed. In practice, multiple UAVs can form a UAV swarm to enable the ARIS cooperatively. In this article, we first present an overview of the UAV swarm-enabled ARIS (SARIS), including its motivations and competitive advantages compared to TRIS and ARIS, as well as its new transformative applications in wireless networks. We then address the critical challenges of designing the SARIS by focusing on the beamforming design, SARIS channel estimation, and SARIS's deployment and movement. Next, the potential performance enhancement of SARIS is showcased and discussed with preliminary numerical results. Finally, open research opportunities are illustrated.