OFDM-Standard Compatible SC-NOFS Waveforms for Low-Latency and Jitter-Tolerance Industrial IoT Communications

• URL: http://arxiv.org/abs/2406.04776v1
• Date: Fri, 7 Jun 2024 09:20:30 GMT
• Title: OFDM-Standard Compatible SC-NOFS Waveforms for Low-Latency and Jitter-Tolerance Industrial IoT Communications
• Authors: Tongyang Xu, Shuangyang Li, Jinhong Yuan,
• Abstract summary: This work proposes a spectrally efficient irregular Sinc (irSinc) shaping technique, revisiting the traditional Sinc back to 1924. irSinc yields a signal with increased spectral efficiency without sacrificing error performance. Our signal achieves faster data transmission within the same spectral bandwidth through 5G standard signal configuration.
• Score: 53.398544571833135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional communications focus on regular and orthogonal signal waveforms for simplified signal processing and improved spectral efficiency. In contrast, the next-generation communications would aim for irregular and non-orthogonal signal waveforms to introduce new capabilities. This work proposes a spectrally efficient irregular Sinc (irSinc) shaping technique, revisiting the traditional Sinc back to 1924, with the aim of enhancing performance in industrial Internet of things (IIoT). In time-critical IIoT applications, low-latency and time-jitter tolerance are two critical factors that significantly impact the performance and reliability. Recognizing the inevitability of latency and jitter in practice, this work aims to propose a waveform technique to mitigate these effects via reducing latency and enhancing the system robustness under time jitter effects. The utilization of irSinc yields a signal with increased spectral efficiency without sacrificing error performance. Integrating the irSinc in a two-stage framework, a single-carrier non-orthogonal frequency shaping (SC-NOFS) waveform is developed, showcasing perfect compatibility with 5G standards, enabling the direct integration of irSinc in existing industrial IoT setups. Through 5G standard signal configuration, our signal achieves faster data transmission within the same spectral bandwidth. Hardware experiments validate an 18% saving in timing resources, leading to either reduced latency or enhanced jitter tolerance.

Related papers

• STAF: Sinusoidal Trainable Activation Functions for Implicit Neural Representation [7.2888019138115245]
  Implicit Neural Representations (INRs) have emerged as a powerful framework for modeling continuous signals. The spectral bias of ReLU-based networks is a well-established limitation, restricting their ability to capture fine-grained details in target signals. We introduce Sinusoidal Trainable Functions Activation (STAF) STAF inherently modulates its frequency components, allowing for self-adaptive spectral learning.
  arXiv  Detail & Related papers  (2025-02-02T18:29:33Z)
• Graph Neural Network Based Hybrid Beamforming Design in Wideband Terahertz MIMO-OFDM Systems [11.887537452826622]
  6G wireless technology is projected to adopt higher and wider frequency bands, enabled by highly directional beamforming. The vast bandwidths available also make the impact of beam squint in massive multiple input and multiple output (MIMO) systems non-negligible. Traditional approaches such as adding a true-time-delay line (TTD) on each antenna are costly due to the massive antenna arrays required. This paper puts forth a signal processing alternative, specifically adapted to the multicarrier structure of OFDM systems, through an innovative application of Graph Neural Networks (GNNs) to optimize hybrid beamforming.
  arXiv  Detail & Related papers  (2025-01-27T18:45:54Z)
• Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks [55.467288506826755]
  Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks. Most existing communication-efficient FL algorithms fail to reduce the significant inter-device variance. We propose a novel communication-efficient FL algorithm, named FedQVR, which relies on a sophisticated variance-reduced scheme.
  arXiv  Detail & Related papers  (2025-01-20T04:26:21Z)
• PCA-Featured Transformer for Jamming Detection in 5G UAV Networks [0.559239450391449]
  Jamming attacks pose a threat to Unmanned Aerial Vehicle (UAV) wireless communication systems. Current detection approaches struggle with sophisticated artificial intelligence (AI) jamming techniques. We present a novel transformer-based deep learning framework for jamming detection.
  arXiv  Detail & Related papers  (2024-12-19T16:13:04Z)
• Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning [68.63990729719369]
  The wireless spectrum is becoming scarce, resulting in low spectral efficiency for D2D communications. This paper aims to integrate the ambient backscatter communication technology into D2D devices to allow them to backscatter ambient RF signals. We develop a novel quantum reinforcement learning (RL) algorithm that can achieve a faster convergence rate with fewer training parameters.
  arXiv  Detail & Related papers  (2024-10-23T15:36:43Z)
• Hierarchical Decoupling Capacitor Optimization for Power Distribution Network of 2.5D ICs with Co-Analysis of Frequency and Time Domains Based on Deep Reinforcement Learning [0.95046743487433]
  We introduce a novel two-phase optimization flow using deep reinforcement learning to tackle both the on-chip small signal noise and SSN. To the best of our knowledge, this is the first dual-domain optimization strategy that simultaneously addresses both the small signal noise and SSN propagation.
  arXiv  Detail & Related papers  (2024-07-02T06:12:55Z)
• Fourier Space Losses for Efficient Perceptual Image Super-Resolution [131.50099891772598]
  We show that it is possible to improve the performance of a recently introduced efficient generator architecture solely with the application of our proposed loss functions. We show that our losses' direct emphasis on the frequencies in Fourier-space significantly boosts the perceptual image quality. The trained generator achieves comparable results with and is 2.4x and 48x faster than state-of-the-art perceptual SR methods RankSRGAN and SRFlow respectively.
  arXiv  Detail & Related papers  (2021-06-01T20:34:52Z)
• Digital Beamforming Robust to Time-Varying Carrier Frequency Offset [21.18926642388997]
  We present novel beamforming algorithms that are robust to signal corruptions arising from a time-variant carrier frequency offset. We propose two atomic-norm-minimization (ANM)-based methods to design a weight vector that can be used to cancel interference when there exist unknown time-varying frequency drift in the pilot and interferer signals.
  arXiv  Detail & Related papers  (2021-03-08T18:08:56Z)
• EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference [82.1584439276834]
  Transformer-based language models such as BERT provide significant accuracy improvement for a multitude of natural language processing (NLP) tasks. We present EdgeBERT, an in-depth algorithm- hardware co-design for latency-aware energy optimization for multi-task NLP.
  arXiv  Detail & Related papers  (2020-11-28T19:21:47Z)
• Interference Distribution Prediction for Link Adaptation in Ultra-Reliable Low-Latency Communications [71.0558149440701]
  Link adaptation (LA) is considered to be one of the bottlenecks to realize URLLC. In this paper, we focus on predicting the signal to interference plus noise ratio at the user to enhance the LA. We show that exploiting time correlation of the interference is an important enabler of URLLC.
  arXiv  Detail & Related papers  (2020-07-01T07:59:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.