MoE-AMC: Enhancing Automatic Modulation Classification Performance Using Mixture-of-Experts

• URL: http://arxiv.org/abs/2312.02298v1
• Date: Mon, 4 Dec 2023 19:31:15 GMT
• Title: MoE-AMC: Enhancing Automatic Modulation Classification Performance Using Mixture-of-Experts
• Authors: Jiaxin Gao, Qinglong Cao, Yuntian Chen
• Abstract summary: MoE-AMC is a novel Mixture-of-Experts (MoE) based model crafted to address Automatic Modulation Classification (AMC) in a well-balanced manner. MoE-AMC seamlessly combines the strengths of LSRM for handling low SNR signals and HSRM for high SNR signals. Experiments show that MoE-AMC achieved an average classification accuracy of 71.76% across different SNR levels, surpassing the performance of previous SOTA models by nearly 10%.
• Score: 2.6764607949560593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic Modulation Classification (AMC) plays a vital role in time series analysis, such as signal classification and identification within wireless communications. Deep learning-based AMC models have demonstrated significant potential in this domain. However, current AMC models inadequately consider the disparities in handling signals under conditions of low and high Signal-to-Noise Ratio (SNR), resulting in an unevenness in their performance. In this study, we propose MoE-AMC, a novel Mixture-of-Experts (MoE) based model specifically crafted to address AMC in a well-balanced manner across varying SNR conditions. Utilizing the MoE framework, MoE-AMC seamlessly combines the strengths of LSRM (a Transformer-based model) for handling low SNR signals and HSRM (a ResNet-based model) for high SNR signals. This integration empowers MoE-AMC to achieve leading performance in modulation classification, showcasing its efficacy in capturing distinctive signal features under diverse SNR scenarios. We conducted experiments using the RML2018.01a dataset, where MoE-AMC achieved an average classification accuracy of 71.76% across different SNR levels, surpassing the performance of previous SOTA models by nearly 10%. This study represents a pioneering application of MoE techniques in the realm of AMC, offering a promising avenue for elevating signal classification accuracy within wireless communication systems.

Related papers

• Vaccinating Federated Learning for Robust Modulation Classification in Distributed Wireless Networks [0.0]
  We propose FedVaccine, a novel AMC model aimed at improving generalizability across signals with varying noise levels. FedVaccine overcomes the limitations of existing FL-based AMC models' linear aggregation by employing a split-learning strategy. These findings highlight FedVaccine's potential to enhance the reliability and performance of AMC systems in practical wireless network environments.
  arXiv  Detail & Related papers  (2024-10-16T17:48:47Z)
• BiLSTM and Attention-Based Modulation Classification of Realistic Wireless Signals [2.0650230600617534]
  The proposed model exploits multiple representations of the wireless signal as inputs to the network. An attention layer is used after the BiLSTM layer to emphasize the important temporal features. The experimental results on the recent and realistic RML22 dataset demonstrate the superior performance of the proposed model with an accuracy up to around 99%.
  arXiv  Detail & Related papers  (2024-08-14T01:17:19Z)
• Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion [56.38386580040991]
  Consistency Trajectory Model (CTM) is a generalization of Consistency Models (CM) CTM enables the efficient combination of adversarial training and denoising score matching loss to enhance performance. Unlike CM, CTM's access to the score function can streamline the adoption of established controllable/conditional generation methods.
  arXiv  Detail & Related papers  (2023-10-01T05:07:17Z)
• 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)
• AMC-Net: An Effective Network for Automatic Modulation Classification [22.871024969842335]
  We propose a novel AMC-Net that improves recognition by denoising the input signal in the frequency domain while performing multi-scale and effective feature extraction. Experiments on two representative datasets demonstrate that our model performs better in efficiency and effectiveness than the most current methods.
  arXiv  Detail & Related papers  (2023-04-02T04:26:30Z)
• Decision Forest Based EMG Signal Classification with Low Volume Dataset Augmented with Random Variance Gaussian Noise [51.76329821186873]
  We produce a model that can classify six different hand gestures with a limited number of samples that generalizes well to a wider audience. We appeal to a set of more elementary methods such as the use of random bounds on a signal, but desire to show the power these methods can carry in an online setting.
  arXiv  Detail & Related papers  (2022-06-29T23:22:18Z)
• 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)
• Momentum Pseudo-Labeling for Semi-Supervised Speech Recognition [55.362258027878966]
  We present momentum pseudo-labeling (MPL) as a simple yet effective strategy for semi-supervised speech recognition. MPL consists of a pair of online and offline models that interact and learn from each other, inspired by the mean teacher method. The experimental results demonstrate that MPL effectively improves over the base model and is scalable to different semi-supervised scenarios.
  arXiv  Detail & Related papers  (2021-06-16T16:24:55Z)
• SafeAMC: Adversarial training for robust modulation recognition models [53.391095789289736]
  In communication systems, there are many tasks, like modulation recognition, which rely on Deep Neural Networks (DNNs) models. These models have been shown to be susceptible to adversarial perturbations, namely imperceptible additive noise crafted to induce misclassification. We propose to use adversarial training, which consists of fine-tuning the model with adversarial perturbations, to increase the robustness of automatic modulation recognition models.
  arXiv  Detail & Related papers  (2021-05-28T11:29:04Z)
• Towards a Competitive End-to-End Speech Recognition for CHiME-6 Dinner Party Transcription [73.66530509749305]
  In this paper, we argue that, even in difficult cases, some end-to-end approaches show performance close to the hybrid baseline. We experimentally compare and analyze CTC-Attention versus RNN-Transducer approaches along with RNN versus Transformer architectures. Our best end-to-end model based on RNN-Transducer, together with improved beam search, reaches quality by only 3.8% WER abs. worse than the LF-MMI TDNN-F CHiME-6 Challenge baseline.
  arXiv  Detail & Related papers  (2020-04-22T19:08:33Z)
• ANN-Based Detection in MIMO-OFDM Systems with Low-Resolution ADCs [0.0]
  We propose a multi-layer artificial neural network (ANN) that is trained with the Levenberg-Marquardt algorithm for use in signal detection. We consider a blind detection scheme where data symbol estimation is carried out without knowing the channel state information at the receiver.
  arXiv  Detail & Related papers  (2020-01-31T03:38:42Z)

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.