pwc-archive/papers-with-abstracts huggingface dataset:
Partial-Information Multiple Access Protocol for Orthogonal Transmissions
Authors: Alberto Rech, Stefano Tomasin, Lorenzo Vangelista, Cristina Costa
Date: 2023-04-24 00:00:00

With the stringent requirements introduced by the new sixth-generation (6G) internet-of-things (IoT) use cases, traditional approaches to multiple access control have started to show their limitations. A new wave of grant-free (GF) approaches have been therefore proposed as a viable alternative. However, a definitive solution is still to be accomplished. In our work, we propose a new semi-GF coordinated random access (RA) protocol, denoted as partial-information multiple access (PIMA), to reduce packet loss and latency, particularly in the presence of sporadic activations. We consider a machine-type communications (MTC) scenario, wherein devices need to transmit data packets in the uplink to a base station (BS). When using PIMA, the BS can acquire partial information on the instantaneous traffic conditions and, using compute-over-the-air techniques, estimate the number of devices with packets waiting for transmission in their queue. Based on this knowledge, the BS assigns to each device a single slot for transmission. However, since each slot may still be assigned to multiple users, collisions may occur. Both the total number of allocated slots and the user assignments are optimized, based on the estimated number of active users, to reduce collisions and improve the efficiency of the multiple access scheme. To prove the validity of our solution, we compare PIMA to time-division multiple-access (TDMA) and slotted ALOHA (SALOHA) schemes, the ideal solutions for orthogonal multiple access (OMA) in the time domain in the case of low and high traffic conditions, respectively. We show that PIMA is able not only to adapt to different traffic conditions and to provide fewer packet drops regardless of the intensity of packet generations, but also able to merge the advantages of both TDMA and SALOHA schemes, thus providing performance improvements in terms of packet loss probability and latency.
On the Characterization of Regular Ring Lattices and their Relation with the Dirichlet Kernel
Authors: Marco Fabris
Date: 2023-04-23 00:00:00
Tasks: Relation

Regular ring lattices (RRLs) are defined as peculiar undirected circulant graphs constructed from a cycle graph, wherein each node is connected to pairs of neighbors that are spaced progressively in terms of vertex degree. This kind of network topology is extensively adopted in several graph-based distributed scalable protocols and their spectral properties often play a central role in the determination of convergence rates for such algorithms. In this work, basic properties of RRL graphs and the eigenvalues of the corresponding Laplacian and Randi\'{c} matrices are investigated. A deep characterization for the spectra of these matrices is given and their relation with the Dirichlet kernel is illustrated. Consequently, the Fiedler value of such a network topology is found analytically. With regard to RRLs, properties on the bounds for the spectral radius of the Laplacian matrix and the essential spectral radius of the Randi\'{c} matrix are also provided, proposing interesting conjectures on the latter quantities.
Model-Free Learning of Two-Stage Beamformers for Passive IRS-Aided Network Design
Authors: Hassaan Hashmi, Spyridon Pougkakiotis, Dionysios S. Kalogerias
Date: 2023-04-22 00:00:00

Electronically tunable metasurfaces, or Intelligent Reflective Surfaces (IRSs), are a popular technology for achieving high spectral efficiency in modern wireless systems by shaping channels using a multitude of tunable passive reflective elements. Capitalizing on key practical limitations of IRS-aided beamforming pertaining to system modeling and channel sensing/estimation, we propose a novel, fully data-driven Zeroth-order Stochastic Gradient Ascent (ZoSGA) algorithm for general two-stage (i.e., short/long-term), fully-passive IRS-aided stochastic utility maximization. ZoSGA learns long-term optimal IRS beamformers jointly with short-term optimal precoders (e.g., WMMSE-based) via minimal zeroth-order reinforcement and in a strictly model-free fashion, relying solely on the \textit{effective} compound channels observed at the terminals, while being independent of channel models or network/IRS configurations. Another remarkable feature of ZoSGA is being amenable to analysis, enabling us to establish a state-of-the-art (SOTA) convergence rate of the order of $\boldsymbol{O}(\sqrt{S}\epsilon^{-4})$ under minimal assumptions, where $S$ is the total number of IRS elements, and $\epsilon$ is a desired suboptimality target. Our numerical results on a standard MISO downlink IRS-aided sumrate maximization setting establish SOTA empirical behavior of ZoSGA as well, consistently and substantially outperforming standard fully model-based baselines. Lastly, we demonstrate that ZoSGA can in fact operate \textit{in the field}, by directly optimizing the capacitances of a varactor-based electromagnetic IRS model (unknown to ZoSGA) on a multiple user/IRS, compute-heavy network setting, with essentially no computational overheads or performance degradation.
On the Value of Online Learning for Radar Waveform Selection
Authors: Charles E. Thornton, R. Michael Buehrer
Date: 2023-04-21 00:00:00
Tasks: Decision Making

This paper attempts to characterize the kinds of physical scenarios in which an online learning-based cognitive radar is expected to reliably outperform a fixed rule-based waveform selection strategy, as well as the converse. We seek general insights through an examination of two decision-making scenarios, namely dynamic spectrum access and multiple-target tracking. The radar scene is characterized by inducing a state-space model and examining the structure of its underlying Markov state transition matrix, in terms of entropy rate and diagonality. It is found that entropy rate is a strong predictor of online learning-based waveform selection, while diagonality is a better predictor of fixed rule-based waveform selection. We show that these measures can be used to predict first and second-order stochastic dominance relationships, which can allow system designers to make use of simple decision rules instead of more cumbersome learning approaches under certain conditions. We validate our findings through numerical results for each application and provide guidelines for future implementations.
Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach
Authors: Zhaoyuan Shi, Huabing Lu, Xianzhong Xie, Helin Yang, Chongwen Huang, Jun Cai, Zhiguo Ding
Date: 2023-04-11 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning

An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated, where non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH). The problem of joint control of the RIS's amplification matrix and phase shift matrix is formulated to maximize the communication success ratio with considering the quality of service (QoS) requirements of users, dynamic communication state, and dynamic available energy of RIS. To tackle this non-convex problem, a cascaded deep learning algorithm namely long short-term memory-deep deterministic policy gradient (LSTM-DDPG) is designed. First, an advanced LSTM based algorithm is developed to predict users' dynamic communication state. Then, based on the prediction results, a DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix of the RIS. Finally, simulation results verify the accuracy of the prediction of the proposed LSTM algorithm, and demonstrate that the LSTM-DDPG algorithm has a significant advantage over other benchmark algorithms in terms of communication success ratio performance.
Machine Learning-based Methods for Joint {Detection-Channel Estimation} in OFDM Systems
Authors: Wilson de Souza Junior, Taufik Abrao
Date: 2023-04-08 00:00:00

In this work, two machine learning (ML)-based structures for joint detection-channel estimation in OFDM systems are proposed and extensively characterized. Both ML architectures, namely Deep Neural Network (DNN) and Extreme Learning Machine (ELM), are developed {to provide improved data detection performance} and compared with the conventional matched filter (MF) detector equipped with the minimum mean square error (MMSE) and least square (LS) channel estimators. The bit-error-rate (BER) performance vs. computational complexity trade-off is analyzed, demonstrating the superiority of the proposed DNN-OFDM and ELM-OFDM detectors methodologies.
Recomputing Solutions to Perturbed Multi-Commodity Pickup and Delivery Vehicle Routing Problems using Monte Carlo Tree Search
Authors: Mithun Goutham, Stephanie Stockar
Date: 2023-04-22 00:00:00

The Multi-Commodity Pickup and Delivery Vehicle Routing Problem aims to optimize the pickup and delivery of multiple unique commodities using a fleet of several agents with limited payload capacities. This paper addresses the challenge of quickly recomputing the solution to this NP-hard problem when there are unexpected perturbations to the nominal task definitions, likely to occur under real-world operating conditions. The proposed method first decomposes the nominal problem by constructing a search tree using Monte Carlo Tree Search for task assignment, and uses a rapid heuristic for routing each agent. When changes to the problem are revealed, the nominal search tree is rapidly updated with new costs under the updated problem parameters, generating solutions quicker and with a reduced optimality gap, as compared to recomputing the solution as an entirely new problem. Computational experiments are conducted by varying the locations of the nominal problem and the payload capacity of an agent to demonstrate the effectiveness of utilizing the nominal search tree to handle perturbations for real-time implementation.
Data-Driven Robust Optimization for Energy-Aware and Safe Navigation of Electric Vehicles
Authors: Simran Kumari, Ashish R. Hota, Siddhartha Mukhopadhyay
Date: 2023-04-25 00:00:00
Tasks: Collision Avoidance

In this paper, we simultaneously tackle the problem of energy optimal and safe navigation of electric vehicles in a data-driven robust optimization framework. We consider a dynamic model of the electric vehicle which includes kinematic variables in both inertial and body coordinate systems in order to capture both longitudinal and lateral motion as well as state-of-energy of the battery. We leverage past data of obstacle motion to construct a future occupancy set with probabilistic guarantees, and formulate robust collision avoidance constraints with respect to such an occupancy set using convex programming duality. Consequently, we present the finite horizon optimal control problem subject to robust collision avoidance constraints while penalizing resulting energy consumption. Finally, we show the effectiveness of the proposed approach in reducing energy consumption and ensuring safe navigation via extensive simulations involving curved roads and multiple obstacles.
Vehicle State Estimation and Prediction
Authors: Xinchen Li, Levent Guvenc, Bilin Aksun-Guvenc
Date: 2023-04-23 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Change Point Detection, Prediction, State Estimation

This paper presents methods for vehicle state estimation and prediction for autonomous driving. A roundabout is chosen to apply the methods and illustrate the results as autonomous vehicles have difficulty in handling roundabouts. State estimation based on the unscented Kalman filter (UKF) is introduced first with application to a roundabout. The microscopic traffic simulator SUMO is used to generate realistic traffic in the roundabout for the simulation experiments. Change point detection based driving behavior prediction using a multi policy approach is then introduced and evaluated for the round intersection example. Finally, these methods are combined for vehicle trajectory estimation based on UKF and policy prediction and demonstrated using the roundabout example.
Experimental Flight Testing of an Adaptive Autopilot with Parameter Drift Mitigation
Authors: Yin Yong Chee, Parham Oveissi, Siyuan Shao, Joonghyun Lee, Juan A. Paredes, Dennis S. Bernstein, Ankit Goel
Date: 2023-04-20 00:00:00

This paper modifies an adaptive multicopter autopilot to mitigate instabilities caused by adaptive parameter drift and presents simulation and experimental results to validate the modified autopilot. The modified adaptive controller is obtained by including a static nonlinearity in the adaptive loop, updated by the retrospective cost adaptive control algorithm. It is shown in simulation and physical test experiments that the adaptive autopilot with proposed modifications can continually improve the fixed-gain autopilot as well as prevent the drift of the adaptive parameters, thus improving the robustness of the adaptive autopilot.
Self-regularised Minimum Latency Training for Streaming Transformer-based Speech Recognition
Authors: Mohan Li, Rama Doddipatla, Catalin Zorila
Date: 2023-04-24 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper proposes a self-regularised minimum latency training (SR-MLT) method for streaming Transformer-based automatic speech recognition (ASR) systems. In previous works, latency was optimised by truncating the online attention weights based on the hard alignments obtained from conventional ASR models, without taking into account the potential loss of ASR accuracy. On the contrary, here we present a strategy to obtain the alignments as a part of the model training without external supervision. The alignments produced by the proposed method are dynamically regularised on the training data, such that the latency reduction does not result in the loss of ASR accuracy. SR-MLT is applied as a fine-tuning step on the pre-trained Transformer models that are based on either monotonic chunkwise attention (MoChA) or cumulative attention (CA) algorithms for online decoding. ASR experiments on the AIShell-1 and Librispeech datasets show that when applied on a decent pre-trained MoChA or CA baseline model, SR-MLT can effectively reduce the latency with the relative gains ranging from 11.8% to 39.5%. Furthermore, we also demonstrate that under certain accuracy levels, the models trained with SR-MLT can achieve lower latency when compared to those supervised using external hard alignments.
Towards Controllable Audio Texture Morphing
Authors: Chitralekha Gupta, Purnima Kamath, Yize Wei, Zhuoyao Li, Suranga Nanayakkara, Lonce Wyse
Date: 2023-04-23 00:00:00
Tasks: Generative Adversarial Network

In this paper, we propose a data-driven approach to train a Generative Adversarial Network (GAN) conditioned on "soft-labels" distilled from the penultimate layer of an audio classifier trained on a target set of audio texture classes. We demonstrate that interpolation between such conditions or control vectors provides smooth morphing between the generated audio textures, and shows similar or better audio texture morphing capability compared to the state-of-the-art methods. The proposed approach results in a well-organized latent space that generates novel audio outputs while remaining consistent with the semantics of the conditioning parameters. This is a step towards a general data-driven approach to designing generative audio models with customized controls capable of traversing out-of-distribution regions for novel sound synthesis.
Heart Rate Extraction from Abdominal Audio Signals
Authors: Jake Stuchbury-Wass, Erika Bondareva, Kayla-Jade Butkow, Sanja Scepanovic, Zoran Radivojevic, Cecilia Mascolo
Date: 2023-04-21 00:00:00
Tasks: Denoising

Abdominal sounds (ABS) have been traditionally used for assessing gastrointestinal (GI) disorders. However, the assessment requires a trained medical professional to perform multiple abdominal auscultation sessions, which is resource-intense and may fail to provide an accurate picture of patients' continuous GI wellbeing. This has generated a technological interest in developing wearables for continuous capture of ABS, which enables a fuller picture of patient's GI status to be obtained at reduced cost. This paper seeks to evaluate the feasibility of extracting heart rate (HR) from such ABS monitoring devices. The collection of HR directly from these devices would enable gathering vital signs alongside GI data without the need for additional wearable devices, providing further cost benefits and improving general usability. We utilised a dataset containing 104 hours of ABS audio, collected from the abdomen using an e-stethoscope, and electrocardiogram as ground truth. Our evaluation shows for the first time that we can successfully extract HR from audio collected from a wearable on the abdomen. As heart sounds collected from the abdomen suffer from significant noise from GI and respiratory tracts, we leverage wavelet denoising for improved heart beat detection. The mean absolute error of the algorithm for average HR is 3.4 BPM with mean directional error of -1.2 BPM over the whole dataset. A comparison to photoplethysmography-based wearable HR sensors shows that our approach exhibits comparable accuracy to consumer wrist-worn wearables for average and instantaneous heart rate.
Non-autoregressive End-to-end Approaches for Joint Automatic Speech Recognition and Spoken Language Understanding
Authors: Mohan Li, Rama Doddipatla
Date: 2023-04-21 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, speech-recognition, Speech Recognition, Spoken Language Understanding

This paper presents the use of non-autoregressive (NAR) approaches for joint automatic speech recognition (ASR) and spoken language understanding (SLU) tasks. The proposed NAR systems employ a Conformer encoder that applies connectionist temporal classification (CTC) to transcribe the speech utterance into raw ASR hypotheses, which are further refined with a bidirectional encoder representations from Transformers (BERT)-like decoder. In the meantime, the intent and slot labels of the utterance are predicted simultaneously using the same decoder. Both Mask-CTC and self-conditioned CTC (SC-CTC) approaches are explored for this study. Experiments conducted on the SLURP dataset show that the proposed SC-Mask-CTC NAR system achieves 3.7% and 3.2% absolute gains in SLU metrics and a competitive level of ASR accuracy, when compared to a Conformer-Transformer based autoregressive (AR) model. Additionally, the NAR systems achieve 6x faster decoding speed than the AR baseline.
Exact Worst-Case Execution-Time Analysis for Implicit Model Predictive Control
Authors: Daniel Arnström, David Broman, Daniel Axehill
Date: 2023-04-23 00:00:00
Tasks: Model Predictive Control

We propose the first method that determines the exact worst-case execution time (WCET) for implicit linear model predictive control (MPC). Such WCET bounds are imperative when MPC is used in real time to control safety-critical systems. The proposed method applies when the quadratic programming solver in the MPC controller belongs to a family of well-established active-set solvers. For such solvers, we leverage a previously proposed complexity certification framework to generate a finite set of archetypal optimization problems; we prove that these archetypal problems form an execution-time equivalent cover of all possible problems; that is, that they capture the execution time for solving any possible optimization problem that can be encountered online. Hence, by solving just these archetypal problems on the hardware on which the MPC is to be deployed, and by recording the execution times, we obtain the exact WCET. In addition to providing formal proofs of the methods efficacy, we validate the method on an MPC example where an inverted pendulum on a cart is stabilized. The experiments highlight the following advantages compared with classical WCET methods: (i) in contrast to classical static methods, our method gives the exact WCET; (ii) in contrast to classical measurement-based methods, our method guarantees a correct WCET estimate and requires fewer measurements on the hardware.
What is the Expected Transient Behavior of Opinion Evolution for Two Communities?
Authors: Yu Xing, Karl H. Johansson
Date: 2023-04-24 00:00:00

We study the transient behavior of a gossip model, in which agents randomly interact pairwise over a weighted graph with two communities. Edges within each community have identical weights, different from the weights between communities. It is shown that, at the early stage of the opinion evolution, the expected agent states in the same community have identical sign, despite influence of stubborn agents. Moreover, it is shown that the expected states of the agents in the same community concentrate around the initial average opinion of that community, if the weights within communities are larger than between. In contrast, if the edge weights between communities are larger, then the expected states of all agents concentrate around everyone's initial average opinion. Different from the traditional asymptotic analysis in the opinion dynamics literature, these results focus on the initial phase of opinion evolution and establish a correspondence between community structure and transient behavior of the gossip model. The results are illustrated by numerical examples.
Profiling the news spreading barriers using news headlines
Authors: Abdul Sittar, Dunja Mladenic, Marko Grobelnik
Date: 2023-04-07 00:00:00
Tasks: text-classification, Text Classification

News headlines can be a good data source for detecting the news spreading barriers in news media, which may be useful in many real-world applications. In this paper, we utilize semantic knowledge through the inference-based model COMET and sentiments of news headlines for barrier classification. We consider five barriers including cultural, economic, political, linguistic, and geographical, and different types of news headlines including health, sports, science, recreation, games, homes, society, shopping, computers, and business. To that end, we collect and label the news headlines automatically for the barriers using the metadata of news publishers. Then, we utilize the extracted commonsense inferences and sentiments as features to detect the news spreading barriers. We compare our approach to the classical text classification methods, deep learning, and transformer-based methods. The results show that the proposed approach using inferences-based semantic knowledge and sentiment offers better performance than the usual (the average F1-score of the ten categories improves from 0.41, 0.39, 0.59, and 0.59 to 0.47, 0.55, 0.70, and 0.76 for the cultural, economic, political, and geographical respectively) for classifying the news-spreading barriers.
A Semi-Decentralized Tikhonov-based Algorithm for Optimal Generalized Nash Equilibrium Selection
Authors: Emilio Benenati, Wicak Ananduta, Sergio Grammatico
Date: 2023-04-25 00:00:00

To optimally select a generalized Nash equilibrium, in this paper, we propose a semi-decentralized algorithm based on a double-layer Tikhonov regularization method. Technically, we extend the Tikhonov method for equilibrium selection in non-generalized games to the generalized case by coupling it with the preconditioned forward-backward splitting, which guarantees linear convergence to the solutions of the inner layer problem and allows for a semi-decentralized implementation. We then establish a conceptual connection and draw a comparison between the proposed algorithm and the hybrid steepest descent method, the other known distributed framework for solving the selection problem.