pwc-archive/papers-with-abstracts huggingface dataset:
Learning-Based Intermittent CSI Estimation with Adaptive Intervals in Integrated Sensing and Communication Systems
Authors: Jie Chen, Xianbin Wang
Date: 2024-05-23 00:00:00
Tasks: Integrated sensing and communication, ISAC

Due to the distinct objectives and multipath utilization mechanisms between the communication module and radar module, the system design of integrated sensing and communication (ISAC) necessitates two types of channel state information (CSI), i.e., communication CSI representing the whole channel gain and phase shifts, and radar CSI exclusively focused on target mobility and position information. However, current ISAC systems apply an identical mechanism to estimate both types of CSI at the same predetermined estimation interval, leading to significant overhead and compromised performances. Therefore, this paper proposes an intermittent communication and radar CSI estimation scheme with adaptive intervals for individual users/targets, where both types of CSI can be predicted using channel temporal correlations for cost reduction or re-estimated via training signal transmission for improved estimation accuracy. Specifically, we jointly optimize the binary CSI re-estimation/prediction decisions and transmit beamforming matrices for individual users/targets to maximize communication transmission rates and minimize radar tracking errors and costs in a multiple-input single-output (MISO) ISAC system. Unfortunately, this problem has causality issues because it requires comparing system performances under re-estimated CSI and predicted CSI during the optimization. Additionally, the binary decision makes the joint design a mixed integer nonlinear programming (MINLP) problem, resulting in high complexity when using conventional optimization algorithms. Therefore, we propose a deep reinforcement online learning (DROL) framework that first implements an online deep neural network (DNN) to learn the binary CSI updating decisions from the experiences. Given the learned decisions, we propose an efficient algorithm to solve the remaining beamforming design problem efficiently.
Multi-Objective Optimization-Based Waveform Design for Multi-User and Multi-Target MIMO-ISAC Systems
Authors: Peng Wang, Dongsheng Han, Yashuai Cao, Wanli Ni, Dusit Niyato
Date: 2024-05-22 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) opens up new service possibilities for sixth-generation (6G) systems, where both communication and sensing (C&S) functionalities co-exist by sharing the same hardware platform and radio resource. In this paper, we investigate the waveform design problem in a downlink multi-user and multi-target ISAC system under different C&S performance preferences. The multi-user interference (MUI) may critically degrade the communication performance. To eliminate the MUI, we employ the constructive interference mechanism into the ISAC system, which saves the power budget for communication. However, due to the conflict between C&S metrics, it is intractable for the ISAC system to achieve the optimal performance of C&S objective simultaneously. Therefore, it is important to strike a trade-off between C&S objectives. By virtue of the multi-objective optimization theory, we propose a weighted Tchebycheff-based transformation method to re-frame the C&S trade-off problem as a Pareto-optimal problem, thus effectively tackling the constraints in ISAC systems. Finally, simulation results reveal the trade-off relation between C&S performances, which provides insights for the flexible waveform design under different C&S performance preferences in MIMO-ISAC systems.
Boosted Neural Decoders: Achieving Extreme Reliability of LDPC Codes for 6G Networks
Authors: Hee-Youl Kwak, Dae-Young Yun, Yongjune Kim, Sang-Hyo Kim, Jong-Seon No
Date: 2024-05-22 00:00:00
Tasks: Data Augmentation, Decoder, Transfer Learning

Ensuring extremely high reliability in channel coding is essential for 6G networks. The next-generation of ultra-reliable and low-latency communications (xURLLC) scenario within 6G networks requires frame error rate (FER) below $10^{-9}$. However, low-density parity-check (LDPC) codes, the standard in 5G new radio (NR), encounter a challenge known as the error floor phenomenon, which hinders to achieve such low rates. To tackle this problem, we introduce an innovative solution: boosted neural min-sum (NMS) decoder. This decoder operates identically to conventional NMS decoders, but is trained by novel training methods including: i) boosting learning with uncorrected vectors, ii) block-wise training schedule to address the vanishing gradient issue, iii) dynamic weight sharing to minimize the number of trainable weights, iv) transfer learning to reduce the required sample count, and v) data augmentation to expedite the sampling process. Leveraging these training strategies, the boosted NMS decoder achieves the state-of-the art performance in reducing the error floor as well as superior waterfall performance. Remarkably, we fulfill the 6G xURLLC requirement for 5G LDPC codes without a severe error floor. Additionally, the boosted NMS decoder, once its weights are trained, can perform decoding without additional modules, making it highly practical for immediate application. The source code is available at https://github.com/ghy1228/LDPC_Error_Floor.
Communication-Efficient Federated Learning via Clipped Uniform Quantization
Authors: Zavareh Bozorgasl, Hao Chen
Date: 2024-05-22 00:00:00
Tasks: Federated Learning, Quantization

This paper presents a novel approach to enhance communication efficiency in federated learning through clipped uniform quantization. By leveraging optimal clipping thresholds and client-specific adaptive quantization schemes, the proposed method significantly reduces bandwidth and memory requirements for model weight transmission between clients and the server while maintaining competitive accuracy. We investigate the effects of symmetric clipping and uniform quantization on model performance, emphasizing the role of stochastic quantization in mitigating artifacts and improving robustness. Extensive simulations demonstrate that the method achieves near-full-precision performance with substantial communication savings. Moreover, the proposed approach facilitates efficient weight averaging based on the inverse of the mean squared quantization errors, effectively balancing the trade-off between communication efficiency and model accuracy. Moreover, in contrast to federated averaging, this design obviates the need to disclose client-specific data volumes to the server, thereby enhancing client privacy. Comparative analysis with conventional quantization methods further confirms the efficacy of the proposed scheme.
Wealth inequality and utility: Effect evaluation of redistribution and consumption morals using the macro-econophysical coupled approach
Authors: Takeshi Kato, Yosuke Tanabe, Mohammad Rezoanul Hoque
Date: 2024-05-22 00:00:00
Tasks: Philosophy

Reducing wealth inequality and increasing utility are critical issues. This study reveals the effects of redistribution and consumption morals on wealth inequality and utility. To this end, we present a novel approach that couples the dynamic model of capital, consumption, and utility in macroeconomics with the interaction model of joint business and redistribution in econophysics. With this approach, we calculate the capital (wealth), the utility based on consumption, and the Gini index of these inequality using redistribution and consumption thresholds as moral parameters. The results show that: under-redistribution and waste exacerbate inequality; conversely, over-redistribution and stinginess reduce utility; and a balanced moderate moral leads to achieve both reduced inequality and increased utility. These findings provide renewed economic and numerical support for the moral importance known from philosophy, anthropology, and religion. The revival of redistribution and consumption morals should promote the transformation to a human mutual-aid economy, as indicated by philosopher and anthropologist, instead of the capitalist economy that has produced the current inequality. The practical challenge is to implement bottom-up social business, on a foothold of worker coops and platform cooperatives as a community against the state and the market, with moral consensus and its operation.
HR-INR: Continuous Space-Time Video Super-Resolution via Event Camera
Authors: Yunfan Lu, Zipeng Wang, Yusheng Wang, Hui Xiong
Date: 2024-05-22 00:00:00
Tasks: Space-time Video Super-resolution, Super-Resolution, Video Restoration, Video Super-Resolution

Continuous space-time video super-resolution (C-STVSR) aims to simultaneously enhance video resolution and frame rate at an arbitrary scale. Recently, implicit neural representation (INR) has been applied to video restoration, representing videos as implicit fields that can be decoded at an arbitrary scale. However, the highly ill-posed nature of C-STVSR limits the effectiveness of current INR-based methods: they assume linear motion between frames and use interpolation or feature warping to generate features at arbitrary spatiotemporal positions with two consecutive frames. This restrains C-STVSR from capturing rapid and nonlinear motion and long-term dependencies (involving more than two frames) in complex dynamic scenes. In this paper, we propose a novel C-STVSR framework, called HR-INR, which captures both holistic dependencies and regional motions based on INR. It is assisted by an event camera, a novel sensor renowned for its high temporal resolution and low latency. To fully utilize the rich temporal information from events, we design a feature extraction consisting of (1) a regional event feature extractor - taking events as inputs via the proposed event temporal pyramid representation to capture the regional nonlinear motion and (2) a holistic event-frame feature extractor for long-term dependence and continuity motion. We then propose a novel INR-based decoder with spatiotemporal embeddings to capture long-term dependencies with a larger temporal perception field. We validate the effectiveness and generalization of our method on four datasets (both simulated and real data), showing the superiority of our method.
Convergence analysis of kernel learning FBSDE filter
Authors: Yunzheng Lyu, Feng Bao
Date: 2024-05-22 00:00:00

Kernel learning forward backward SDE filter is an iterative and adaptive meshfree approach to solve the nonlinear filtering problem. It builds from forward backward SDE for Fokker-Planker equation, which defines evolving density for the state variable, and employs KDE to approximate density. This algorithm has shown more superior performance than mainstream particle filter method, in both convergence speed and efficiency of solving high dimension problems. However, this method has only been shown to converge empirically. In this paper, we present a rigorous analysis to demonstrate its local and global convergence, and provide theoretical support for its empirical results.
Autonomous Algorithm for Training Autonomous Vehicles with Minimal Human Intervention
Authors: Sang-Hyun Lee, Daehyeok Kwon, Seung-Woo Seo
Date: 2024-05-22 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Reinforcement Learning (RL)

Recent reinforcement learning (RL) algorithms have demonstrated impressive results in simulated driving environments. However, autonomous vehicles trained in simulation often struggle to work well in the real world due to the fidelity gap between simulated and real-world environments. While directly training real-world autonomous vehicles with RL algorithms is a promising approach to bypass the fidelity gap problem, it presents several challenges. One critical yet often overlooked challenge is the need to reset a driving environment between every episode. This reset process demands significant human intervention, leading to poor training efficiency in the real world. In this paper, we introduce a novel autonomous algorithm that enables off-the-shelf RL algorithms to train autonomous vehicles with minimal human intervention. Our algorithm reduces unnecessary human intervention by aborting episodes to prevent unsafe states and identifying informative initial states for subsequent episodes. The key idea behind identifying informative initial states is to estimate the expected amount of information that can be obtained from under-explored but reachable states. Our algorithm also revisits rule-based autonomous driving algorithms and highlights their benefits in safely returning an autonomous vehicle to initial states. To evaluate how much human intervention is required during training, we implement challenging urban driving tasks that require an autonomous vehicle to reset to initial states on its own. The experimental results show that our autonomous algorithm is task-agnostic and achieves competitive driving performance with much less human intervention than baselines.
Pure Planning to Pure Policies and In Between with a Recursive Tree Planner
Authors: A. Norman Redlich
Date: 2024-05-21 00:00:00
Tasks: MuJoCo

A recursive tree planner (RTP) is designed to function as a pure planner without policies at one extreme and run a pure greedy policy at the other. In between, the RTP exploits policies to improve planning performance and improve zero-shot transfer from one class of planning problem to another. Policies are learned through imitation of the planner. These are then used by the planner to improve policies in a virtuous cycle. To improve planning performance and zero-shot transfer, the RTP incorporates previously learned tasks as generalized actions (GA) at any level of its hierarchy, and can refine those GA by adding primitive actions at any level too. For search, the RTP uses a generalized Dijkstra algorithm [Dijkstra 1959] which tries the greedy policy first and then searches over near-greedy paths and then farther away as necessary. The RPT can return multiple sub-goals from lower levels as well as boundary states near obstacles, and can exploit policies with background and object-number invariance. Policies at all levels of the hierarchy can be learned simultaneously or in any order or come from outside the framework. The RTP is tested here on a variety of Box2d [Cato 2022] problems, including the classic lunar lander [Farama 2022], and on the MuJoCo [Todorov et al 2012] inverted pendulum.
System Safety Monitoring of Learned Components Using Temporal Metric Forecasting
Authors: Sepehr Sharifi, Andrea Stocco, Lionel C. Briand
Date: 2024-05-21 00:00:00
Tasks: Autonomous Driving, Probabilistic Time Series Forecasting, Time Series Forecasting

In learning-enabled autonomous systems, safety monitoring of learned components is crucial to ensure their outputs do not lead to system safety violations, given the operational context of the system. However, developing a safety monitor for practical deployment in real-world applications is challenging. This is due to limited access to internal workings and training data of the learned component. Furthermore, safety monitors should predict safety violations with low latency, while consuming a reasonable amount of computation. To address the challenges, we propose a safety monitoring method based on probabilistic time series forecasting. Given the learned component outputs and an operational context, we empirically investigate different Deep Learning (DL)-based probabilistic forecasting to predict the objective measure capturing the satisfaction or violation of a safety requirement (safety metric). We empirically evaluate safety metric and violation prediction accuracy, and inference latency and resource usage of four state-of-the-art models, with varying horizons, using autonomous aviation and autonomous driving case studies. Our results suggest that probabilistic forecasting of safety metrics, given learned component outputs and scenarios, is effective for safety monitoring. Furthermore, for both case studies, Temporal Fusion Transformer (TFT) was the most accurate model for predicting imminent safety violations, with acceptable latency and resource consumption.
Understanding Sarcoidosis Using Large Language Models and Social Media Data
Authors: Nan Miles Xi, Hong-Long Ji, Lin Wang
Date: 2024-05-12 00:00:00
Tasks: Diagnostic, Language Modelling, Large Language Model, Prognosis, Sentiment Analysis

Sarcoidosis is a rare inflammatory disease characterized by the formation of granulomas in various organs. The disease presents diagnostic and treatment challenges due to its diverse manifestations and unpredictable nature. In this study, we employed a Large Language Model (LLM) to analyze sarcoidosis-related discussions on the social media platform Reddit. Our findings underscore the efficacy of LLMs in accurately identifying sarcoidosis-related content. We discovered a wide array of symptoms reported by patients, with fatigue, swollen lymph nodes, and shortness of breath as the most prevalent. Prednisone was the most prescribed medication, while infliximab showed the highest effectiveness in improving prognoses. Notably, our analysis revealed disparities in prognosis based on age and gender, with women and younger patients experiencing good and polarized outcomes, respectively. Furthermore, unsupervised clustering identified three distinct patient subgroups (phenotypes) with unique symptom profiles, prognostic outcomes, and demographic distributions. Finally, sentiment analysis revealed a moderate negative impact on patients' mental health post-diagnosis, particularly among women and younger individuals. Our study represents the first application of LLMs to understand sarcoidosis through social media data. It contributes to understanding the disease by providing data-driven insights into its manifestations, treatments, prognoses, and impact on patients' lives. Our findings have direct implications for improving personalized treatment strategies and enhancing the quality of care for individuals living with sarcoidosis.
Total Radiated Power Measurements of a mmWave Phased Array in a Reverberation Chamber
Authors: Alejandro Antón Ruiz, Samar Hosseinzadegan, John Kvarnstrand, Klas Arvidsson, Andrés Alayón Glazunov
Date: 2024-05-23 00:00:00

This paper explores the use of reverberation chambers for TRP measurements of beamformed radiation by phased arrays at mmWave frequencies. First, the received power was verified by the one-sample K-S GoF test to follow the exponential probability distribution. Different numbers of samples and stirrers' positions were considered. Second, we showed that the effective number of independent samples is different depending on the number of samples and stirrers' positions. Third, the beamforming TRP estimates are presented for all beams, analyzing the statistical significance of the observed differences with a selection of samplings.
K-factor Evaluation in a Hybrid Reverberation Chamber plus CATR OTA Testing Setup
Authors: Alejandro Antón Ruiz, Samar Hosseinzadegan, John Kvarnstrand, Klas Arvidsson, Andrés Alayón Glazunov
Date: 2024-05-23 00:00:00

This paper investigates achieving diverse K-factors using a Reverberation Chamber (RC) with a Compact Antenna Test Range (CATR) system. It explores six hybrid "RC plus CATR" configurations involving different excitations of the Rich Isotropic Multipath (RIMP) field and CATR-generated plane waves, with some setups including absorbers. A fixed horn antenna points towards the CATR in all configurations. The study found that the null hypothesis of Rayleigh or Rician probability distributions for the received signal envelope could not be rejected, with RIMP setups primarily conforming to Rayleigh distribution and all setups showing Rician distribution. Various K-factors were obtained, but no generalizable method for achieving the desired K-factor was identified. The paper also estimates the K-factor as a function of frequency in the 24.25-29.5 GHz band. Smaller K-factors exhibit larger fluctuations, while larger K-factors remain relatively stable, with consistent fluctuations across the frequency range.
Design and Development of a Roaming Wireless Safety Emergency Stop
Authors: Henry Beuster, Thomas Doebbert, Christoph Cammin, Dmytro Krush, Gerd Scholl
Date: 2024-05-23 00:00:00

Modern manufacturing is characterized by a high degree of automation, with autonomous systems also frequently being used. In such environments human intervention in the event of malfunctions or maintenance becomes a rare but also necessary task. When human workers are no longer an integral part of the production process, but only intervene when necessary, e.g., in the case of unexpected machine behavior, appropriate safety solutions will become even more important. This work describes a wireless communication system enabling a flexible and safe emergency stop function for multiple automation cells. A portable emergency stop switch allows seamless transition between different wireless cells, ensuring functional safety. The communication protocol combines IO-Link Wireless features with the safety requirements already implemented in IO-Link Safety. Security requirements are fulfilled through encryption and authentication. The IO-Link Wireless roaming functionality is used to extend the system across several manufacturing cells. An experimental setup confirms the suitability of the system for various applications. The results demonstrate the effectiveness of the handover mechanism and evaluate the potential of the system to improve flexibility, availability and security in dynamic production environments. Future extensions could include the use of AI based evaluation of the radio signals for an intelligent cell handover.
Advanced Safety Filter for Smooth Transient Operation of a Battery Energy Storage System
Authors: Michael Schneeberger, Florian Dörfler, Silvia Mastellone
Date: 2024-05-23 00:00:00

In this paper, we implement an advanced safety filter to smoothly limit the current of an inverter-based Battery Energy Storage System. The task involves finding suitable Control Barrier Function and Control Lyapunov Function via Sum-of-Squares optimization to certify the system's safety during grid transients. In contrast to the conventional safety filter, the advanced safety filter not only provides a safety certificate but also achieves finite-time convergence to a nominal region. Within this region, the action of the nominal control, i.e. the Enhanced Direct Power Control, remains unaltered by the safety filter. The advanced safety filter is implemented using a Quadratically Constrained Quadratic Program, providing the capability to also encode quadratic input constraints. Finally, we showcase the effectiveness of the implementation through simulations involving a load step at the Point of Common Coupling, and we compare the outcomes with those obtained using a standard vector current controller.
A hybrid systems framework for data-based adaptive control of linear time-varying systems
Authors: Andrea Iannelli, Romain Postoyan
Date: 2024-05-23 00:00:00

We consider the data-driven stabilization of discrete-time linear time-varying systems. The controller is defined as a linear state-feedback law whose gain is adapted to the plant changes through a data-based event-triggering rule. To do so, we monitor the evolution of a data-based Lyapunov function along the solution. When this Lyapunov function does not satisfy a designed desirable condition, an episode is triggered to update the controller gain and the corresponding Lyapunov function using the last collected data. The resulting closed-loop dynamics hence exhibits both physical jumps, due to the system dynamics, and episodic jumps, which naturally leads to a hybrid discrete-time system. We leverage the inherent robustness of the controller and provide general conditions under which various stability notions can be established for the system. Two notable cases where these conditions are satisfied are treated, and numerical results illustrating the relevance of the approach are discussed.