pwc-archive/papers-with-abstracts huggingface dataset:
End-to-End Learning of Transmitter and Receiver Filters in Bandwidth Limited Fiber Optic Communication Systems
Authors: Søren Føns Nielsen, Francesco Da Ros, Mikkel N. Schmidt, Darko Zibar
Date: 2024-09-18 00:00:00

This paper investigates the application of end-to-end (E2E) learning for joint optimization of pulse-shaper and receiver filter to reduce intersymbol interference (ISI) in bandwidth-limited communication systems. We investigate this in two numerical simulation models: 1) an additive white Gaussian noise (AWGN) channel with bandwidth limitation and 2) an intensity modulated direct detection (IM/DD) link employing an electro-absorption modulator. For both simulation models, we implement a wavelength division multiplexing (WDM) scheme to ensure that the learned filters adhere to the bandwidth constraints of the WDM channels. Our findings reveal that E2E learning greatly surpasses traditional single-sided transmitter pulse-shaper or receiver filter optimization methods, achieving significant performance gains in terms of symbol error rate with shorter filter lengths. These results suggest that E2E learning can decrease the complexity and enhance the performance of future high-speed optical communication systems.
Atmospheric Turbulence-Immune Free Space Optical Communication System based on Discrete-Time Analog Transmission
Authors: Hongyu Huang, Zhenming Yu, Yi Lei, Wei zhang, Yongli Zhao, Shanguo Huang, Kun Xu
Date: 2024-09-18 00:00:00

To effectively mitigate the influence of atmospheric turbulence, a novel discrete-time analog transmission free-space optical (DTAT-FSO) communication scheme is proposed. It directly maps information sources to discrete-time analog symbols via joint source-channel coding and modulation. Differently from traditional digital free space optical (TD-FSO) schemes, the proposed DTAT-FSO approach can automatically adapt to the variation of the channel state, with no need to adjust the specific modulation and coding scheme. The performance of the DTAT-FSO system was evaluated in both intensity modulation/direct detection (IM/DD) and coherent FSO systems for high-resolution image transmission. The results show that the DTAT-FSO reliably transmits images at low received optical powers (ROPs) and automatically enhances quality at high ROPs, while the TD-FSO experiences cliff and leveling effects when the channel state varies. With respect to the TD-FSO scheme, the DTAT-FSO scheme improved receiver sensitivity by 2.5 dB in the IM/DD FSO system and 0.8 dB in the coherent FSO system, and it achieved superior image fidelity under the same ROP. The automatic adaptation feature and improved performance of the DTAT-FSO suggest its potential for terrestrial, airborne, and satellite optical networks, addressing challenges posed by atmospheric turbulence.
User Subgrouping and Power Control for Multicast Massive MIMO over Spatially Correlated Channels
Authors: Alejandro de la Fuente, Giovanni Interdonato, Giuseppe Araniti
Date: 2024-09-18 00:00:00
Tasks: Fairness

Massive multiple-input-multiple-output (MIMO) is unquestionably a key enabler of the fifth-generation (5G) technology for mobile systems, enabling to meet the high requirements of upcoming mobile broadband services. Physical-layer multicasting refers to a technique for simultaneously serving multiple users, demanding for the same service and sharing the same radio resources, with a single transmission. Massive MIMO systems with multicast communications have been so far studied under the ideal assumption of uncorrelated Rayleigh fading channels. In this work, we consider a practical multicast massive MIMO system over spatially correlated Rayleigh fading channels, investigating the impact of the spatial channel correlation on the favorable propagation, hence on the performance. We propose a subgrouping strategy for the multicast users based on their channel correlation matrices' similarities. The proposed subgrouping approach capitalizes on the spatial correlation to enhance the quality of the channel estimation, and thereby the effectiveness of the precoding. Moreover, we devise a max-min fairness (MMF) power allocation strategy that makes the spectral efficiency (SE) among different multicast subgroups uniform. Lastly, we propose a novel power allocation for uplink (UL) pilot transmission to maximize the SE among the users within the same multicast subgroup. Simulation results show a significant SE gain provided by our user subgrouping and power allocation strategies. Importantly, we show how spatial channel correlation can be exploited to enhance multicast massive MIMO communications.
User Subgrouping in Scalable Cell-Free Massive MIMO Multicasting Systems
Authors: Alejandro de la Fuente, Guillem Femenias, Felip Riera-Palou, Giovanni Interdonato
Date: 2024-09-18 00:00:00

Cell-free massive multiple-input multiple-output (CF-mMIMO) is a breakthrough technology for beyond-5G systems, designed to significantly boost the energy and spectral efficiencies of future mobile networks while ensuring a consistent quality of service for all users. Additionally, multicasting has gained considerable attention recently because physical-layer multicasting offers an efficient method for simultaneously serving multiple users with identical service demands by sharing radio resources. Typically, multicast services are delivered either via unicast transmissions or a single multicast transmission. This work, however, introduces a novel subgroup-centric multicast CF-mMIMO framework that divides users into several multicast subgroups based on the similarities in their spatial channel characteristics. This approach allows for efficient sharing of the pilot sequences used for channel estimation and the precoding filters used for data transmission. The proposed framework employs two scalable precoding strategies: centralized improved partial MMSE (IP-MMSE) and distributed conjugate beam-forming (CB). Numerical results show that for scenarios where users are uniformly distributed across the service area, unicast transmissions using centralized IP-MMSE precoding are optimal. However, in cases where users are spatially clustered, multicast subgrouping significantly improves the sum spectral efficiency (SE) of the multicast service compared to both unicast and single multicast transmission. Notably, in clustered scenarios, distributed CB precoding outperforms IP-MMSE in terms of per-user SE, making it the best solution for delivering multicast content.
Covert Communications Without Pre-Sharing of Side Information and Channel Estimation Over Quasi-Static Fading Channels
Authors: Hiroki Fukada, Hiroki Iimori, Chandan Pradhan, Szabolcs Malomsoky, Naoki Ishikawa
Date: 2024-09-18 00:00:00

We propose a new covert communication scheme that operates without pre-sharing side information and channel estimation, utilizing a Gaussian-distributed Grassmann constellation for noncoherent detection. By designing constant-amplitude symbols on the Grassmann manifold and multiplying them by random variables, we generate signals that follow an arbitrary probability distribution, such as Gaussian or skew-normal distributions. The mathematical property of the manifold enables the transmitter's random variables to remain unshared with the receiver, and the elimination of pilot symbols that could compromise covertness. The proposed scheme achieved higher covertness and achievable rates compared to conventional coherent Gaussian signaling schemes, without any penalty in terms of complexity.
Accelerated Algorithms for Source Orientation Detection (AORI) and Spatiotemporal LCMV (ALCMV) Beamforming in EEG Source Localization
Authors: Ava Yektaeian Vaziri, Bahador Makkiabadi
Date: 2024-09-18 00:00:00
Tasks: EEG

This paper illustrates the development of two efficient source localization algorithms for electroencephalography (EEG) data, aimed at enhancing real-time brain signal reconstruction while addressing the computational challenges of traditional methods. Accurate EEG source localization is crucial for applications in cognitive neuroscience, neurorehabilitation, and brain-computer interfaces (BCIs). To make significant progress toward precise source orientation detection and improved signal reconstruction, we introduce the Accelerated Linear Constrained Minimum Variance (ALCMV) beamforming toolbox and the Accelerated Brain Source Orientation Detection (AORI) toolbox. The ALCMV algorithm speeds up EEG source reconstruction by utilizing recursive covariance matrix calculations, while AORI simplifies source orientation detection from three dimensions to one, reducing computational load by 66% compared to conventional methods. Using both simulated and real EEG data, we demonstrate that these algorithms maintain high accuracy, with orientation errors below 0.2% and signal reconstruction accuracy within 2%. These findings suggest that the proposed toolboxes represent a substantial advancement in the efficiency and speed of EEG source localization, making them well-suited for real-time neurotechnological applications.
Real-Time Sound Event Localization and Detection: Deployment Challenges on Edge Devices
Authors: Jun Wei Yeow, Ee-Leng Tan, Jisheng Bai, Santi Peksi, Woon-Seng Gan
Date: 2024-09-18 00:00:00
Tasks: Raspberry Pi 3, Sound Event Localization and Detection

Sound event localization and detection (SELD) is critical for various real-world applications, including smart monitoring and Internet of Things (IoT) systems. Although deep neural networks (DNNs) represent the state-of-the-art approach for SELD, their significant computational complexity and model sizes present challenges for deployment on resource-constrained edge devices, especially under real-time conditions. Despite the growing need for real-time SELD, research in this area remains limited. In this paper, we investigate the unique challenges of deploying SELD systems for real-world, real-time applications by performing extensive experiments on a commercially available Raspberry Pi 3 edge device. Our findings reveal two critical, often overlooked considerations: the high computational cost of feature extraction and the performance degradation associated with low-latency, real-time inference. This paper provides valuable insights and considerations for future work toward developing more efficient and robust real-time SELD systems
Electromagnetic Property Sensing and Channel Reconstruction Based on Diffusion Schrödinger Bridge in ISAC
Authors: Yuhua Jiang, Feifei Gao, Shi Jin
Date: 2024-09-18 00:00:00
Tasks: ISAC

Integrated sensing and communications (ISAC) has emerged as a transformative paradigm for next-generation wireless systems. In this paper, we present a novel ISAC scheme that leverages the diffusion Schrodinger bridge (DSB) to realize the sensing of electromagnetic (EM) property of a target as well as the reconstruction of the wireless channel. The DSB framework connects EM property sensing and channel reconstruction by establishing a bidirectional process: the forward process transforms the distribution of EM property into the channel distribution, while the reverse process reconstructs the EM property from the channel. To handle the difference in dimensionality between the high-dimensional sensing channel and the lower-dimensional EM property, we generate latent representations using an autoencoder network. The autoencoder compresses the sensing channel into a latent space that retains essential features, which incorporates positional embeddings to process spatial context. The simulation results demonstrate the effectiveness of the proposed DSB framework, which achieves superior reconstruction of the targets shape, relative permittivity, and conductivity. Moreover, the proposed method can also realize high-fidelity channel reconstruction given the EM property of the target. The dual capability of accurately sensing the EM property and reconstructing the channel across various positions within the sensing area underscores the versatility and potential of the proposed approach for broad application in future ISAC systems.
Self-Supervised Learning via VICReg Enables Training of EMG Pattern Recognition Using Continuous Data with Unclear Labels
Authors: Shriram Tallam Puranam Raghu, Dawn T. MacIsaac, Erik J. Scheme
Date: 2024-09-18 00:00:00
Tasks: Self-Supervised Learning

In this study, we investigate the application of self-supervised learning via pre-trained Long Short-Term Memory (LSTM) networks for training surface electromyography pattern recognition models (sEMG-PR) using dynamic data with transitions. While labeling such data poses challenges due to the absence of ground-truth labels during transitions between classes, self-supervised pre-training offers a way to circumvent this issue. We compare the performance of LSTMs trained with either fully-supervised or self-supervised loss to a conventional non-temporal model (LDA) on two data types: segmented ramp data (lacking transition information) and continuous dynamic data inclusive of class transitions. Statistical analysis reveals that the temporal models outperform non-temporal models when trained with continuous dynamic data. Additionally, the proposed VICReg pre-trained temporal model with continuous dynamic data significantly outperformed all other models. Interestingly, when using only ramp data, the LSTM performed worse than the LDA, suggesting potential overfitting due to the absence of sufficient dynamics. This highlights the interplay between data type and model choice. Overall, this work highlights the importance of representative dynamics in training data and the potential for leveraging self-supervised approaches to enhance sEMG-PR models.
NirvaWave: An Accurate and Efficient Near Field Wave Propagation Simulator for 6G and Beyond
Authors: Vahid Yazdnian, Atsutse Kludze, Yasaman Ghasempour
Date: 2024-09-17 00:00:00

The extended near-field range in future mm-Wave and sub-THz wireless networks demands a precise and efficient near-field channel simulator for understanding and optimizing wireless communications in this less-explored regime. This paper presents NirvaWave, a novel near-field channel simulator, built on scalar diffraction theory and Fourier principles, to provide precise wave propagation response in complex wireless mediums under custom user-defined transmitted EM signals. NirvaWave offers an interface for investigating novel near-field wavefronts, e.g., Airy beams, Bessel beams, and the interaction of mmWave and sub-THz signals with obstructions, reflectors, and scatterers. The simulation run-time in NirvaWave is orders of magnitude lower than its EM software counterparts that directly solve Maxwell Equations. Hence, NirvaWave enables a user-friendly interface for large-scale channel simulations required for developing new model-driven and data-driven techniques. We evaluated the performance of NirvaWave through direct comparison with EM simulation software. Finally, we have open-sourced the core codebase of NirvaWave in our GitHub repository (https://github.com/vahidyazdnian1378/NirvaWave).
Linear Jamming Bandits: Learning to Jam 5G-based Coded Communications Systems
Authors: Zachary Schutz, Daniel J. Jakubisin, Charles E. Thornton, R. Michael Buehrer
Date: 2024-09-17 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

We study jamming of an OFDM-modulated signal which employs forward error correction coding. We extend this to leverage reinforcement learning with a contextual bandit to jam a 5G-based system implementing some aspects of the 5G protocol. This model introduces unreliable reward feedback in the form of ACK/NACK observations to the jammer to understand the effect of how imperfect observations of errors can affect the jammer's ability to learn. We gain insights into the convergence time of the jammer and its ability to jam a victim 5G waveform, as well as insights into the vulnerabilities of wireless communications for reinforcement learning-based jamming.
Time-Varying Graph Signal Estimation among Multiple Sub-Networks
Authors: Tsutahiro Fukuhara, Junya Hara, Hiroshi Higashi, Yuichi Tanaka
Date: 2024-09-17 00:00:00

This paper presents an estimation method for time-varying graph signals among multiple sub-networks. In many sensor networks, signals observed are associated with nodes (i.e., sensors), and edges of the network represent the inter-node connectivity. For a large sensor network, measuring signal values at all nodes over time requires huge resources, particularly in terms of energy consumption. To alleviate the issue, we consider a scenario that a sub-network, i.e., cluster, from the whole network is extracted and an intra-cluster analysis is performed based on the statistics in the cluster. The statistics are then utilized to estimate signal values in another cluster. This leads to the requirement for transferring a set of parameters of the sub-network to the others, while the numbers of nodes in the clusters are typically different. In this paper, we propose a cooperative Kalman filter between two sub-networks. The proposed method alternately estimates signals in time between two sub-networks. We formulate a state-space model in the source cluster and transfer it to the target cluster on the basis of optimal transport. In the signal estimation experiments of synthetic and real-world signals, we validate the effectiveness of the proposed method.
Channel Correlation Matrix Extrapolation Based on Roughness Calibration of Scatterers
Authors: Heling Zhang, Xiujun Zhang, Xiaofeng Zhong, Shidong Zhou
Date: 2024-09-17 00:00:00

To estimate the channel correlation matrix (CCM) in areas where channel information cannot be collected in advance, this paper proposes a way to spatially extrapolate CCM based on the calibration of the surface roughness parameters of scatterers in the propagation scene. We calibrate the roughness parameters of scene scatters based on CCM data in some specific areas. From these calibrated roughness parameters, we are able to generate a good prediction of the CCM for any other area in the scene by performing ray tracing. Simulation results show that the channel extrapolation method proposed in this paper can effectively realize the extrapolation of the CCM between different areas in frequency domain, or even from one domain to another.
On the normalized signal to noise ratio in covariance estimation
Authors: Tzvi Diskin, Ami Wiesel
Date: 2024-09-17 00:00:00

We address the Normalized Signal to Noise Ratio (NSNR) metric defined in the seminal paper by Reed, Mallett and Brennan on adaptive detection. The setting is detection of a target vector in additive correlated noise. NSNR is the ratio between the SNR of a linear detector which uses an estimated noise covariance and the SNR of clairvoyant detector based on the exact unknown covariance. It is not obvious how to evaluate NSNR since it is a function of the target vector. To close this gap, we consider the NSNR associated with the worst target. Using the Kantorovich Inequality, we provide a closed form solution for the worst case NSNR. Then, we prove that the classical Gaussian Kullback Leibler (KL) divergence bounds it. Numerical experiments with different true covariances and various estimates also suggest that the KL metric is more correlated with the NSNR metric than competing norm based metrics.
Multi-Explainable TemporalNet: An Interpretable Multimodal Approach using Temporal Convolutional Network for User-level Depression Detection
Authors: Anas Zafar, Danyal Aftab, Rizwan Qureshi, Yaofeng Wang, Hong Yan
Date: 2024-04-22 00:00:00
Proceeding: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 4
Tasks: Depression Detection

Multimodal depression detection through internet-based data such as social media platforms has been an important problem in the research community aiming to predict human mental states for ensuring wellbeing of the society. Recently attention-based networks have gained significant popularity for depression detection. However existing multimodal methods primarily rely on images and text assuming no correlation between temporal aspects such as relative time of different posts or tweets which is a crucial factor in deriving depression related behavior patterns. Moreover they lack model interpretability resulting in limited understanding of how different features are contributing to the model's final prediction. In this paper we propose Multi-Explainable TemporalNet (METN) a Temporal Convolution Network (TCN) based multi-modal transformer network with relative timestamp embeddings. We leverage pretrained foundation models for text and image embeddings and attention maps for model interpretability. We perform extensive experiments and ablation studies to validate the performance of METN for user-level depression detection task. Our model shows state-of-the-art results on various benchmarks such as 0.945 F1 score on multimodal Twitter dataset and 0.913 F1 score on multimodal Reddit dataset. We further demonstrate that our model enhances the accuracy of identifying depression in individuals who publicly post messages on social media platforms with enhanced interpretable compatibility.
Temporal and Spacial Studies of Infectious Diseases: Mathematical Models and Numerical Solvers
Authors: Md Abu Talha, Yongjia Xu, Shan Zhao, Weihua Geng
Date: 2024-09-05 00:00:00

The SIR model is a classical model characterizing the spreading of infectious diseases. This model describes the time-dependent quantity changes among Susceptible, Infectious, and Recovered groups. By introducing space-depend effects such as diffusion and creation in addition to the SIR model, the Fisher's model is in fact a more advanced and comprehensive model. However, the Fisher's model is much less popular than the SIR model in simulating infectious disease numerically due to the difficulties from the parameter selection, the involvement of 2-d/3-d spacial effects, the configuration of the boundary conditions, etc. This paper aim to address these issues by providing numerical algorithms involving space and time finite difference schemes and iterative methods, and its open-source Python code for solving the Fisher's model. This 2-D Fisher's solver is second order in space and up to the second order in time, which is rigorously verified using test cases with analytical solutions. Numerical algorithms such as SOR, implicit Euler, Staggered Crank-Nicolson, and ADI are combined to improve the efficiency and accuracy of the solver. It can handle various boundary conditions subject to different physical descriptions. In addition, real-world data of Covid-19 are used by the model to demonstrate its practical usage in providing prediction and inferences.