pwc-archive/papers-with-abstracts huggingface dataset:
Conceptual Design of LiFi Audio Transmission Using Pre-Programmed Modules
Authors: Auwal Tijjani Amshi
Date: 2021-01-05 00:00:00

We all know that Wi-Fi is presently the most commonly used technology for data transmission and connecting devices to the Internet, at the same time due to much reasonable concern, (such as Wi-Fi can be vulnerable when it comes to hacking, health concern, and low latency, etc.) the concept of Li-Fi is becoming very popular as a new way of data transmission that use light waves to transmit data rather than radio waves. Light-emitting diodes LED are used when transmitting the data in the visible light spectrum. Li-fi uses visible light communication and it has a promising future. Unlike Wi-fi, Li-Fi has low latency, high efficiency, accessible spectrum, and high data can be achieved. It is highly secured so the data cannot be hacked. In this paper, we design a concept of Li-fi audio signal transmission by reusing and repurposing pre-programmed modules to simplify and discuss visible light communication (VLC) in other to give a new researcher the idea on how the concept of LiFi and VLC. In addition to designing the concept we experiment to test the concept and we illustrated the result within this paper.
Intelligent reflecting surface assisted multi-cell multi-band wireless networks
Authors: Wenhao Cai, Rang Liu, Yang Liu, Ming Li, Qian Liu
Date: 2021-01-05 00:00:00

Intelligent reflecting surface (IRS) is deemed as a promising and revolutionizing technology for future wireless communication systems owing to its capability to intelligently change the propagation environment and introduce a new dimension into wireless communication optimization. Most existing studies on IRS are based on an ideal reflection model. However, it is difficult to implement an IRS which can simultaneously realize any adjustable phase shift for the signals with different frequencies. Therefore, the practical phase shift model, which can describe the difference of IRS phase shift responses for the signals with different frequencies, should be utilized in the IRS optimization for wideband and multi-band systems. In this paper, we consider an IRS-assisted multi-cell multi-band system, in which different base stations (BSs) operate at different frequency bands. We aim to jointly design the transmit beamforming of BSs and the reflection beamforming of the IRS to minimize the total transmit power subject to signal to interference-plus-noise ratio (SINR) constraints of individual user and the practical IRS reflection model. With the aid of the practical phase shift model, the influence between the signals with different frequencies is taken into account during the design of IRS. Simulation results illustrate the importance of considering the practical communication scenario on the IRS designs and validate the effectiveness of our proposed algorithm.
Highly-Efficient Single-Switch-Regulated Resonant Wireless Power Receiver with Hybrid Modulation
Authors: Kerui Li, Albert Ting Leung Lee, Siew-Chong Tan, Ron Shu Yuen Hui
Date: 2020-04-09 00:00:00

In this paper, a highly-efficient single-switch-regulated resonant wireless power receiver with hybrid modulation is proposed. To achieve both high efficiency and good output voltage regulation, phase shift and pulse width hybrid modulation are simultaneously applied. The soft switching operation in this topology is achieved by the cycle-by-cycle phase shift adjustment between the input current and the gate drive signal and also attributed to the reactive components such as the series-compensated secondary coil and the parasitic capacitor of the active switch . The soft switching operation also leads to high efficiency and low EMI. By adjusting the duty ratio of the switch, tight regulation of the output voltage can be attained. The steady-state and dynamic models of the resonant receiver with hybrid modulation are analytically derived in order to properly design the feedback controller. An experimental setup of a two-coil wireless power transfer system, including the hardware prototype of the proposed receiver, is constructed for experimental verification. The experimental results show the effectiveness of the soft-switching operation in the receiver with high efficiency while maintaining good regulation of the output voltage, regardless of line and load variations.
Reconfigurable Intelligent Surface Enabled Full-Duplex/Half-Duplex Cooperative Non-Orthogonal Multiple Access
Authors: Mohamed Elhattab, Mohamed Amine Arfaoui, Chadi Assi, Ali Ghrayeb
Date: 2021-01-05 00:00:00

This paper investigates the downlink transmission of reconfigurable intelligent surface (RIS)-aided cooperative non-orthogonal-multiple-access (C-NOMA), where both half-duplex (HD) and full-duplex (FD) relaying modes are considered. The system model consists of one base station (BS), two users and one RIS. The goal is to minimize the total transmit power at both the BS and at the user-cooperating relay for each relaying mode by jointly optimizing the power allocation coefficients at the BS, the transmit power coefficient at the relay user, and the passive beamforming at the RIS, subject to power budget constraints, the successive interference cancellation constraint and the minimum required quality-of-service at both cellular users. To address the high-coupled optimization variables, an efficient algorithm is proposed by invoking an alternating optimization approach that decomposes the original problem into a power allocation sub-problem and a passive beamforming sub-problem, which are solved alternately. For the power allocation sub-problem, the optimal closed-form expressions for the power allocation coefficients are derived. Meanwhile, the semi-definite relaxation approach is exploited to tackle the passive beamforming sub-problem. The simulation results validate the accuracy of the derived power control closed-form expressions and demonstrate the gain in the total transmit power brought by integrating the RIS in C-NOMA networks.
Prior Knowledge Input to Improve LSTM Auto-encoder-based Characterization of Vehicular Sensing Data
Authors: Nima Taherifard, Murat Simsek, Charles Lascelles, Burak Kantarci
Date: 2021-01-04 00:00:00
Tasks: Autonomous Driving, Denoising, Time Series, Time Series Analysis

Precision in event characterization in connected vehicles has become increasingly important with the responsive connectivity that is available to the modern vehicles. Event characterization via vehicular sensors are utilized in safety and autonomous driving applications in vehicles. While characterization systems have been shown to be capable of predicting the risky driving patterns, precision of such systems still remains an open issue. The major issues against the driving event characterization systems need to be addressed in connected vehicle settings, which are the heavy imbalance and the event infrequency of the driving data and the existence of the time-series detection systems that are optimized for vehicular settings. To overcome the problems, we introduce the application of the prior-knowledge input method to the characterization systems. Furthermore, we propose a recurrent-based denoising auto-encoder network to populate the existing data for a more robust training process. The results of the conducted experiments show that the introduction of knowledge-based modelling enables the existing systems to reach significantly higher accuracy and F1-score levels. Ultimately, the combination of the two methods enables the proposed model to attain 14.7\% accuracy boost over the baseline by achieving an accuracy of 0.96.
CFAR-Based Interference Mitigation for FMCW Automotive Radar Systems
Authors: JianPing Wang
Date: 2021-01-04 00:00:00

In this paper, constant false alarm rate (CFAR) detector-based approaches are proposed for interference mitigation of Frequency modulated continuous wave (FMCW) radars. The proposed methods exploit the fact that after dechirping and low-pass filtering operations the targets' beat signals of FMCW radars are composed of exponential sinusoidal components while interferences exhibit short chirp waves within a sweep. The spectra of interferences in the time-frequency ($t$-$f$) domain are detected by employing a 1-D CFAR detector along each frequency bin and then the detected map is dilated as a mask for interference suppression. They are applicable to the scenarios in the presence of multiple interferences. Compared to the existing methods, the proposed methods reduce the power loss of useful signals and are very computationally efficient. Their interference mitigation performances are demonstrated through both numerical simulations and experimental results.
Explainable Neural Network-based Modulation Classification via Concept Bottleneck Models
Authors: Lauren J. Wong, Sean McPherson
Date: 2021-01-04 00:00:00
Tasks: Classification, General Classification, Zero-Shot Learning

While RFML is expected to be a key enabler of future wireless standards, a significant challenge to the widespread adoption of RFML techniques is the lack of explainability in deep learning models. This work investigates the use of CB models as a means to provide inherent decision explanations in the context of DL-based AMC. Results show that the proposed approach not only meets the performance of single-network DL-based AMC algorithms, but provides the desired model explainability and shows potential for classifying modulation schemes not seen during training (i.e. zero-shot learning).
Modeling RIS Empowered Outdoor-to-Indoor Communication in mmWave Cellular Networks
Authors: Mahyar Nemati, Behrouz Maham, Shiva Raj Pokhrel, Jinho Choi
Date: 2021-01-04 00:00:00

With the increasing adoption of millimeter-waves (mmWave) over cellular networks, outdoor-to-indoor (O2I) communication has been one of the challenging research problems due to high penetration loss of buildings. To address this, we investigate the practicability of utilizing reconfigurable intelligent surfaces (RISs) for assisting such O2I communication. We propose a new notion of prefabricated RIS-empowered wall consisting of a large number of chipless radio frequency identification (RFID) sensors. Each sensor maintains its own bank of delay lines. These sensors which are built within the building walls can potentially be controlled by a main integrated circuit (IC) to regulate the phase of impinging signals. To evaluate our idea, we develop a thorough performance analysis of the RIS-based O2I communication in the mmWave network using stochastic-geometry tools for blockage models. Our analysis facilitates two closed-form approximations of the downlink signal-to-noise ratio (SNR) coverage probability for RIS-based O2I communication. We perform extensive simulations to evaluate the accuracy of the derived expressions, thus providing new observations and findings.
Multiuser Full-Duplex Two-Way Communications via Intelligent Reflecting Surface
Authors: Zhangjie Peng, Zhenkun Zhang, Cunhua Pan, Li Li, A. Lee Swindlehurst
Date: 2020-06-09 00:00:00
Tasks: Fairness, Vocal Bursts Valence Prediction

Low-cost passive intelligent reflecting surfaces (IRSs) have recently been envisioned as a revolutionary technology capable of reconfiguring the wireless propagation environment through carefully tuning reflection elements. This paper proposes deploying an IRS to cover the dead zone of cellular multiuser full-duplex (FD) two-way communication links while suppressing user-side self-interference (SI) and co-channel interference (CI). Based on information exchanged by the base station (BS) and all users, this approach can potentially double the spectral efficiency. To ensure network fairness, we jointly optimize the precoding matrix of the BS and the reflection coefficients of the IRS to maximize the weighted minimum rate (WMR) of all users, subject to maximum transmit power and unit-modulus constraints. We reformulate this non-convex problem and decouple it into two subproblems. Then the optimization variables in the equivalent problem are alternately optimized by adopting the block coordinate descent (BCD) algorithm. In order to further reduce the computational complexity, we propose the minorization-maximization (MM) algorithm for optimizing the precoding matrix and the reflection coefficient vector by defining minorizing functions in the surrogate problems. Finally, simulation results confirm the convergence and efficiency of our proposed algorithm, and validate the advantages of introducing IRS to improve coverage in blind areas.
In-Ear SpO2 for Classification of Cognitive Workload
Authors: Harry J. Davies, Ian Williams, Ghena Hammour, Metin Yarici, Barry M. Seemungal, Danilo P. Mandic
Date: 2021-01-03 00:00:00
Tasks: Brain Computer Interface, Classification, General Classification

Classification of cognitive workload promises immense benefit in diverse areas ranging from driver safety to augmenting human capability through closed loop brain computer interface. The brain is the most metabolically active organ in the body and increases its metabolic activity and thus oxygen consumption with increasing cognitive demand. In this study, we explore the feasibility of in-ear SpO2 cognitive workload tracking. To this end, we preform cognitive workload assessment in 8 subjects, based on an N-back task, whereby the subjects are asked to count and remember the number of odd numbers displayed on a screen in 5 second windows. The 2 and 3-back tasks lead to either the lowest median absolute SpO2 or largest median decrease in SpO2 in all of the subjects, indicating a robust and measurable decrease in blood oxygen in response to increased cognitive workload. Using features derived from in-ear pulse oximetry, including SpO2, pulse rate and respiration rate, we were able to classify the 4 N-back task categories, over 5 second epochs, with a mean accuracy of 94.2%. Moreover, out of 21 total features, the 9 most important features for classification accuracy were all SpO2 related features. The findings suggest that in-ear SpO2 measurements provide valuable information for classification of cognitive workload over short time windows, which together with the small form factor promises a new avenue for real time cognitive workload tracking.
Airplane-Aided Integrated Next-Generation Networking
Authors: Muralikrishnan Srinivasan, Sarath Gopi, Sheetal Kalyani, Xiaojing Huang, Lajos Hanzo
Date: 2021-01-03 00:00:00

A high-rate yet low-cost air-to-ground (A2G) communication backbone is conceived for integrating the space and terrestrial network by harnessing the opportunistic assistance of the passenger planes or high altitude platforms (HAPs) as mobile base stations (BSs) and millimetre wave communication. The airliners act as the network-provider for the terrestrial users while relying on satellite backhaul. Three different beamforming techniques relying on a large-scale planar array are used for transmission by the airliner/HAP for achieving a high directional gain, hence minimizing the interference among the users. Furthermore, approximate spectral efficiency (SE) and area spectral efficiency (ASE) expressions are derived and quantified for diverse system parameters.
Statistical Channel Modeling for Long-Range Ground-to-Air FSO Links
Authors: Hossein Safi, Akbar Dargahi
Date: 2021-01-02 00:00:00

To provide high data rate aerial links for 5G and beyond wireless networks, the integration of free-space optical (FSO) communications and aerial platforms has been recently suggested as a practical solution. To fully reap the benefit of aerial-based FSO systems, in this paper, an analytical channel model for a long-range ground-to-air FSO link under the assumption of plane wave optical beam profile at the receiver is derived. Particularly, the model includes the combined effects of transmitter divergence angle, random wobbling of the receiver, jitter due to beam wander, attenuation loss, and atmospheric turbulence. Furthermore, a closed-form expression for the outage probability of the considered link is derived which makes it possible to evaluate the performance of such systems. Numerical results are then provided to corroborate the accuracy of the proposed analytical expressions and to prove the superiority of the proposed channel model over the previous models in long-range aerial FSO links.
Deep Reinforcement Learning-based Anti-jamming Power Allocation in a Two-cell NOMA Network
Authors: Sina Yousefzadeh Marandy, Mohammad Ali Amirabadi, Mohammad Hossein Kahaei, Seyed Mohammad Razavizadeh
Date: 2021-01-01 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, Reinforcement Learning (RL)

The performance of Non-orthogonal Multiple Access (NOMA) system dramatically decreases in the presence of inter-cell interference. This condition gets more challenging if a smart jammer is interacting in a network. In this paper, the NOMA power allocation of two independent Base Stations (BSs) against a smart jammer is, modeled as a sequential game. In this game, at first, each BS as a leader independently chooses its power allocation strategy. Then, the smart jammer as the follower selects its optimal strategy based on the strategies of the BSs. The solutions of this game are, derived under different conditions. Based on the game-theoretical analysis, three new schemes are proposed for anti-jamming NOMA power allocation in a two-cell scenario called a) Q-Learning based Unselfish (QLU) NOMA power allocation scheme, b) Deep Q-Learning based Unselfish (DQLU) NOMA power allocation scheme, and c) Hot Booting Deep Q-Learning based Unselfish (HBDQLU) NOMA power allocation scheme. In these methods the BSs do not coordinate with each other. But our analysis theoretically predicts that with high probability, the proposed methods will converge to the optimal strategy from the total network point of view. Simulation results show the convergence of the proposed schemes and also their outperformance with respect to the Q-Learning-based Selfish (QLS) NOMA power allocation method.
Energy-Efficient Resource Allocation for 5G Cognitive Radio NOMA Using Game Theory
Authors: Shaima S. Abidrabbu, Huseyin Arslan
Date: 2021-01-01 00:00:00

Cognitive radio non-orthogonal multiple access (CR-NOMA) networks promise improved spectrum utilization and capacity in 5G networks. In this work, we aim to investigate efficient power allocation for the secondary users (SUs) in underlay CR-NOMA networks using a game-theoretic approach. We present a novel power allocation to CR-NOMA network from a game-theoretic perspective. First, we specify the utility function of the primary users (PUs) and SUs, and formulate the game as a non-cooperative game. Then, the existence and uniqueness of the Nash equilibrium (NE) are investigated. Finally, the sum utilities of SUs is maximized by optimal power allocation at the NE point. Simulation results provided that the proposed scheme outperforms the conventional method, providing up to 37.5\% increase in sum utilities of the SUs.
Inferring ECG from PPG for Continuous Cardiac Monitoring Using Lightweight Neural Network
Authors: Yuenan Li, Xin Tian, Qiang Zhu, Min Wu
Date: 2020-12-09 00:00:00
Tasks: Model Compression

This paper presents a computational solution that enables continuous cardiac monitoring through cross-modality inference of electrocardiogram (ECG). While some smartwatches now allow users to obtain a 30-second ECG test by tapping a built-in bio-sensor, these short-term ECG tests often miss intermittent and asymptomatic abnormalities of cardiac functions. It is also infeasible to expect persistently active user participation for long-term continuous cardiac monitoring in order to capture these and other types of cardiac abnormalities. To alleviate the need for continuous user attention and active participation, we design a lightweight neural network that infers ECG from the photoplethysmogram (PPG) signal sensed at the skin surface by a wearable optical sensor. We also develop a diagnosis-oriented training strategy to enable the neural network to capture the pathological features of ECG, aiming to increase the utility of reconstructed ECG signals for screening cardiovascular diseases (CVDs). We also leverage model interpretation to obtain insights from data-driven models, for example, to reveal some associations between CVDs and ECG/PPG and to demonstrate how the neural network copes with motion artifacts in the ambulatory application. The experimental results on three datasets demonstrate the feasibility of inferring ECG from PPG, achieving a high fidelity of ECG reconstruction with only about 40K parameters.
Coherent optical communications using coherence-cloned Kerr soliton microcombs
Authors: Yong Geng, Heng Zhou, Wenwen Cui, Xinjie Han, Qiang Zhang, Boyuan Liu, Guangwei Deng, Qiang Zhou, Kun Qiu
Date: 2021-01-01 00:00:00

Dissipative Kerr soliton microcomb has been recognized as a promising on-chip multi-wavelength laser source for fiber optical communications, as its comb lines possess frequency and phase stability far beyond independent lasers. In the scenarios of coherent optical transmission and interconnect, a highly beneficial but rarely explored target is to re-generate a Kerr soliton microcomb at the receiver side as local oscillators that conserve the frequency and phase property of the incoming data carriers, so that to enable coherent detection with minimized optical and electrical compensations. Here, by using the techniques of pump laser conveying and two-point locking, we implement re-generation of a Kerr soliton microcomb that faithfully clones the frequency and phase coherence of another microcomb sent from 50 km away. Moreover, leveraging the coherence-cloned soliton microcombs as carriers and local oscillators, we demonstrate terabit coherent data interconnect, wherein traditional digital processes for frequency offset estimation is totally dispensed with, and carrier phase estimation is substantially simplified via slowed-down phase estimation rate per channel and joint phase estimation among multiple channels. Our work reveals that, in addition to providing a multitude of laser tones, regulating the frequency and phase of Kerr soliton microcombs among transmitters and receivers can significantly improve coherent communication in terms of performance, power consumption, and simplicity.
Audio Content Analysis
Authors: Alexander Lerch
Date: 2021-01-01 00:00:00
Tasks: Emotion Recognition, General Classification, Genre classification, Information Retrieval, Key Detection, Music Classification, Music Emotion Recognition, Music Genre Classification, Music Information Retrieval, Music Transcription, Retrieval

Preprint for a book chapter introducing Audio Content Analysis. With a focus on Music Information Retrieval systems, this chapter defines musical audio content, introduces the general process of audio content analysis, and surveys basic approaches to audio content analysis. The various tasks in Audio Content Analysis are categorized into three classes: music transcription, music performance analysis, and music identification and categorization. The examples for music transcription systems include music key detection, fundamental frequency detection, and music structure detection. Music performance analysis systems feature an overview of beat and tempo detection approaches as well as music performance assessment. The covered music classification systems are audio fingerprinting, music genre classification, and music emotion recognition. The chapter concludes with a discussion and current challenges in the field and a speculation on future perspectives.