A Novel Approach to WaveNet Architecture for RF Signal Separation with Learnable Dilation and Data Augmentation

• URL: http://arxiv.org/abs/2402.09461v1
• Date: Thu, 8 Feb 2024 06:36:29 GMT
• Title: A Novel Approach to WaveNet Architecture for RF Signal Separation with Learnable Dilation and Data Augmentation
• Authors: Yu Tian, Ahmed Alhammadi, Abdullah Quran, Abubakar Sani Ali
• Abstract summary: We present a novel adaptation of the WaveNet architecture that introduces learnable dilation parameters, significantly enhancing signal separation in dense RF spectrums. This paper details our comprehensive methodology, including the refined model architecture, data preparation techniques, and the strategic training strategy. Our model achieved first place in the challenge citedatadrivenrf2024, demonstrating its superior performance and establishing a new standard for machine learning applications.
• Score: 4.3301675903966625
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, we address the intricate issue of RF signal separation by presenting a novel adaptation of the WaveNet architecture that introduces learnable dilation parameters, significantly enhancing signal separation in dense RF spectrums. Our focused architectural refinements and innovative data augmentation strategies have markedly improved the model's ability to discern complex signal sources. This paper details our comprehensive methodology, including the refined model architecture, data preparation techniques, and the strategic training strategy that have been pivotal to our success. The efficacy of our approach is evidenced by the substantial improvements recorded: a 58.82\% increase in SINR at a BER of $10^{-3}$ for OFDM-QPSK with EMI Signal 1, surpassing traditional benchmarks. Notably, our model achieved first place in the challenge \cite{datadrivenrf2024}, demonstrating its superior performance and establishing a new standard for machine learning applications within the RF communications domain.

Related papers

• RSSI Estimation for Constrained Indoor Wireless Networks using ANN [0.0]
  This research establishes two distinct LP-IoT wireless channel estimation models using Artificial Neural Networks (ANN) Both models have been constructed to enhance LP-IoT communication by lowering the estimation error in the LP-IoT wireless channel. The findings demonstrate that our suggested approaches attain remarkable precision in channel estimation, with an improvement in MSE of $88.29%$ of the Feature-based model and $97.46%$ of the Sequence-based model over existing research.
  arXiv  Detail & Related papers  (2024-04-10T02:48:13Z)
• Enhancing Automatic Modulation Recognition through Robust Global Feature Extraction [12.868218616042292]
  Modulated signals exhibit long temporal dependencies. Human experts analyze patterns in constellation diagrams to classify modulation schemes. Classical convolutional-based networks excel at extracting local features but struggle to capture global relationships.
  arXiv  Detail & Related papers  (2024-01-02T06:31:24Z)
• ASR: Attention-alike Structural Re-parameterization [56.88192537044364]
  We propose a simple-yet-effective attention-alike structural re- parameterization (ASR) that allows us to achieve SRP for a given network while enjoying the effectiveness of the attention mechanism. In this paper, we conduct extensive experiments from a statistical perspective and discover an interesting phenomenon Stripe Observation, which reveals that channel attention values quickly approach some constant vectors during training.
  arXiv  Detail & Related papers  (2023-04-13T08:52:34Z)
• On Neural Architectures for Deep Learning-based Source Separation of Co-Channel OFDM Signals [104.11663769306566]
  We study the single-channel source separation problem involving frequency-division multiplexing (OFDM) signals. We propose critical domain-informed modifications to the network parameterization, based on insights from OFDM structures.
  arXiv  Detail & Related papers  (2023-03-11T16:29:13Z)
• Unsupervised Learning for Pilot-free Transmission in 3GPP MIMO Systems [6.352264764099531]
  This paper introduces a new downlink data structure that is free from demodulation reference signals (DM-RS) Exploiting the repetition structure at the user side, it is shown that reliable recovery is possible via canonical correlation analysis. This paper also proposes two effective mechanisms for boosting the CCA performance in OFDM systems.
  arXiv  Detail & Related papers  (2023-02-04T16:32:40Z)
• Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation [98.95383921866096]
  We study the problem of single-channel source separation (SCSS) We focus on cyclostationary signals, which are particularly suitable in a variety of application domains. We propose a deep learning approach using a U-Net architecture, which is competitive with the minimum MSE estimator.
  arXiv  Detail & Related papers  (2022-08-22T14:04:56Z)
• Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access [65.21117658030235]
  This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
  arXiv  Detail & Related papers  (2022-05-02T12:23:55Z)
• Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compression [1.218340575383456]
  Future communication networks must address the scarce spectrum to accommodate growth of heterogeneous wireless devices. We exploit the potential of deep neural networks based multi-task learning framework to simultaneously learn modulation and signal classification tasks. We provide a comprehensive heterogeneous wireless signals dataset for public use.
  arXiv  Detail & Related papers  (2022-02-26T14:51:02Z)
• Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command Recognition [71.96870151495536]
  We propose a novel adversarial reprogramming (AR) approach for low-resource spoken command recognition (SCR) The AR procedure aims to modify the acoustic signals (from the target domain) to repurpose a pretrained SCR model. We evaluate the proposed AR-SCR system on three low-resource SCR datasets, including Arabic, Lithuanian, and dysarthric Mandarin speech.
  arXiv  Detail & Related papers  (2021-10-08T05:07:35Z)
• LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers [104.01415343139901]
  We propose a deep detector entitled LoRD-Net for recovering information symbols from one-bit measurements. LoRD-Net has a task-based architecture dedicated to recovering the underlying signal of interest. We evaluate the proposed receiver architecture for one-bit signal recovery in wireless communications.
  arXiv  Detail & Related papers  (2021-02-05T04:26:05Z)

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.