Deep Learning-Based Frequency Offset Estimation

• URL: http://arxiv.org/abs/2311.16155v1
• Date: Wed, 8 Nov 2023 13:56:22 GMT
• Title: Deep Learning-Based Frequency Offset Estimation
• Authors: Tao Chen, Shilian Zheng, Jiawei Zhu, Qi Xuan, and Xiaoniu Yang
• Abstract summary: We show the utilization of deep learning for CFO estimation by employing a residual network (ResNet) to learn and extract signal features. In comparison to the commonly used traditional CFO estimation methods, our proposed IQ-ResNet method exhibits superior performance across various scenarios.
• Score: 7.143765507026541
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In wireless communication systems, the asynchronization of the oscillators in the transmitter and the receiver along with the Doppler shift due to relative movement may lead to the presence of carrier frequency offset (CFO) in the received signals. Estimation of CFO is crucial for subsequent processing such as coherent demodulation. In this brief, we demonstrate the utilization of deep learning for CFO estimation by employing a residual network (ResNet) to learn and extract signal features from the raw in-phase (I) and quadrature (Q) components of the signals. We use multiple modulation schemes in the training set to make the trained model adaptable to multiple modulations or even new signals. In comparison to the commonly used traditional CFO estimation methods, our proposed IQ-ResNet method exhibits superior performance across various scenarios including different oversampling ratios, various signal lengths, and different channels

Related papers

• Deep OFDM Channel Estimation: Capturing Frequency Recurrence [10.76835122839777]
  We propose a deep-learning-based channel estimation scheme in an OFDM system. We employ recurrent neural network techniques within a single OFDM slot, thus overcoming the latency and memory constraints. The proposed SisRafNet delivers superior estimation performance compared to existing deep-learning-based channel estimation techniques.
  arXiv  Detail & Related papers  (2024-01-07T14:13:08Z)
• An ML-assisted OTFS vs. OFDM adaptable modem [1.8492669447784602]
  OTFS and OFDM waveforms enjoy the benefits of the reuse of legacy architectures, simplicity of receiver design, and low-complexity detection. We propose a deep neural network (DNN)-based adaptation scheme to switch between using either an OTFS or OFDM signal processing chain at the transmitter and receiver for optimal mean-squared-error (MSE) performance.
  arXiv  Detail & Related papers  (2023-09-04T02:33:44Z)
• Modulation Classification Through Deep Learning Using Resolution Transformed Spectrograms [3.9511559419116224]
  We propose a scheme for Automatic Modulation Classification (AMC) using modern architectures of Convolutional Neural Networks (CNN) We perform resolution transformation of spectrograms that results up to 99.61% of computational load reduction and 8x faster conversion from the received I/Q data. The performance is evaluated on existing CNN models including SqueezeNet, Resnet-50, InceptionResnet-V2, Inception-V3, VGG-16 and Densenet-201.
  arXiv  Detail & Related papers  (2023-06-06T16:14:15Z)
• 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)
• Deep Reinforcement Learning for IRS Phase Shift Design in Spatiotemporally Correlated Environments [93.30657979626858]
  We propose a deep actor-critic algorithm that accounts for channel correlations and destination motion. We show that, when channels aretemporally correlated, the inclusion of the SNR in the state representation with function approximation in ways that inhibit convergence.
  arXiv  Detail & Related papers  (2022-11-02T22:07:36Z)
• 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)
• Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems [61.78590389147475]
  This paper proposes a model-driven deep learning (MDDL)-based channel estimation and feedback scheme for millimeter-wave (mmWave) systems. To reduce the uplink pilot overhead for estimating the high-dimensional channels from a limited number of radio frequency (RF) chains, we propose to jointly train the phase shift network and the channel estimator as an auto-encoder. Numerical results show that the proposed MDDL-based channel estimation and feedback scheme outperforms the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-04-22T13:34:53Z)
• FedRec: Federated Learning of Universal Receivers over Fading Channels [92.15358738530037]
  We propose a neural network-based symbol detection technique for downlink fading channels. Multiple users collaborate to jointly learn a universal data-driven detector, hence the name FedRec. The performance of the resulting receiver is shown to approach the MAP performance in diverse channel conditions without requiring knowledge of the fading statistics.
  arXiv  Detail & Related papers  (2020-11-14T11:29:55Z)
• Learning from Heterogeneous EEG Signals with Differentiable Channel Reordering [51.633889765162685]
  CHARM is a method for training a single neural network across inconsistent input channels. We perform experiments on four EEG classification datasets and demonstrate the efficacy of CHARM.
  arXiv  Detail & Related papers  (2020-10-21T12:32:34Z)
• Fine Timing and Frequency Synchronization for MIMO-OFDM: An Extreme Learning Approach [8.432859469083951]
  We propose a novel scheme leveraging extreme learning machine (ELM) to achieve high-precision synchronization. The proposed method is robust in terms of the choice of channel parameters and also in terms of "generalization ability" from a machine learning standpoint.
  arXiv  Detail & Related papers  (2020-07-17T21:41:38Z)
• Time-Frequency Analysis based Blind Modulation Classification for Multiple-Antenna Systems [6.011027400738812]
  Blind modulation classification is an important step to implement cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Traditional likelihood-based and feature-based approaches cannot be applied in these scenarios.
  arXiv  Detail & Related papers  (2020-04-01T12:27:29Z)

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.