MetaSTNet: Multimodal Meta-learning for Cellular Traffic Conformal Prediction

• URL: http://arxiv.org/abs/2505.21553v1
• Date: Mon, 26 May 2025 04:23:54 GMT
• Title: MetaSTNet: Multimodal Meta-learning for Cellular Traffic Conformal Prediction
• Authors: Hui Ma, Kai Yang,
• Abstract summary: We propose a deep learning model, entitled MetaSTNet, based on a multimodal meta-learning framework.<n>It is an end-to-end network architecture that trains the model in a simulator and transfers the meta-knowledge to a real-world environment.<n>It can quickly adapt and obtain accurate predictions on a new task with only a small amount of real-world training data.
• Score: 3.6308844286016133
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Network traffic prediction techniques have attracted much attention since they are valuable for network congestion control and user experience improvement. While existing prediction techniques can achieve favorable performance when there is sufficient training data, it remains a great challenge to make accurate predictions when only a small amount of training data is available. To tackle this problem, we propose a deep learning model, entitled MetaSTNet, based on a multimodal meta-learning framework. It is an end-to-end network architecture that trains the model in a simulator and transfers the meta-knowledge to a real-world environment, which can quickly adapt and obtain accurate predictions on a new task with only a small amount of real-world training data. In addition, we further employ cross conformal prediction to assess the calibrated prediction intervals. Extensive experiments have been conducted on real-world datasets to illustrate the efficiency and effectiveness of MetaSTNet.

Related papers

• Neural Conformal Control for Time Series Forecasting [54.96087475179419]
  We introduce a neural network conformal prediction method for time series that enhances adaptivity in non-stationary environments.<n>Our approach acts as a neural controller designed to achieve desired target coverage, leveraging auxiliary multi-view data with neural network encoders.<n>We empirically demonstrate significant improvements in coverage and probabilistic accuracy, and find that our method is the only one that combines good calibration with consistency in prediction intervals.
  arXiv  Detail & Related papers  (2024-12-24T03:56:25Z)
• Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification [50.28867343337997]
  This work presents a TinyML-based semantic communication framework for few-shot wireless image classification. We exploit split-learning to limit the computations performed by the end-users while ensuring privacy-preserving. meta-learning overcomes data availability concerns and speeds up training by utilizing similarly trained tasks.
  arXiv  Detail & Related papers  (2024-09-03T05:56:55Z)
• RACH Traffic Prediction in Massive Machine Type Communications [5.416701003120508]
  This paper presents a machine learning-based framework tailored for forecasting bursty traffic in ALOHA networks.<n>We develop a new low-complexity online prediction algorithm that updates the states of the LSTM network by leveraging frequently collected data from the mMTC network.<n>We evaluate the performance of the proposed framework in a network with a single base station and thousands of devices organized into groups with distinct traffic-generating characteristics.
  arXiv  Detail & Related papers  (2024-05-08T17:28:07Z)
• TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models [27.306180426294784]
  We introduce TPLLM, a novel traffic prediction framework leveraging Large Language Models (LLMs) In this framework, we construct a sequence embedding layer based on Conal Neural Networks (LoCNNs) and a graph embedding layer based on Graph Contemporalal Networks (GCNs) to extract sequence features and spatial features. Experiments on two real-world datasets demonstrate commendable performance in both full-sample and few-shot prediction scenarios.
  arXiv  Detail & Related papers  (2024-03-04T17:08:57Z)
• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• STDA-Meta: A Meta-Learning Framework for Few-Shot Traffic Prediction [5.502177196766933]
  We propose a novel-temporal domain adaptation (STDA) method that learns transferable meta-knowledge from data-sufficient cities in an adversarial manner. This learned meta-knowledge can improve prediction performance of data-scarce cities. Specifically, we train the STDA model using a Model-Atemporal Meta-Learning (MAML) based episode learning process.
  arXiv  Detail & Related papers  (2023-10-31T06:52:56Z)
• Unsupervised Representation Learning to Aid Semi-Supervised Meta Learning [16.534014215010757]
  We propose a one-shot unsupervised meta-learning to learn latent representation of training samples. A temperature-scaled cross-entropy loss is used in the inner loop of meta-learning to prevent overfitting. The proposed method is model agnostic and can aid any meta-learning model to improve accuracy.
  arXiv  Detail & Related papers  (2023-10-19T18:25:22Z)
• Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning [117.48444197402858]
  We propose ePisode cUrriculum inveRsion (ECI) during data-free meta training and invErsion calibRation following inner loop (ICFIL) during meta testing.<n>ECI adaptively increases the difficulty level of pseudo episodes according to the real-time feedback of the meta model.<n>We formulate the optimization process of meta training with ECI as an adversarial form in an end-to-end manner.
  arXiv  Detail & Related papers  (2023-03-20T15:10:41Z)
• Transfer Learning Based Efficient Traffic Prediction with Limited Training Data [3.689539481706835]
  Efficient prediction of internet traffic is an essential part of Self Organizing Network (SON) for ensuring proactive management. Deep sequence model in network traffic prediction with limited training data has not been studied extensively in the current works. We investigated and evaluated the performance of the deep transfer learning technique in traffic prediction with inadequate historical data.
  arXiv  Detail & Related papers  (2022-05-09T14:44:39Z)
• Transforming Model Prediction for Tracking [109.08417327309937]
  Transformers capture global relations with little inductive bias, allowing it to learn the prediction of more powerful target models. We train the proposed tracker end-to-end and validate its performance by conducting comprehensive experiments on multiple tracking datasets. Our tracker sets a new state of the art on three benchmarks, achieving an AUC of 68.5% on the challenging LaSOT dataset.
  arXiv  Detail & Related papers  (2022-03-21T17:59:40Z)
• Diversity inducing Information Bottleneck in Model Ensembles [73.80615604822435]
  In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction. We explicitly optimize a diversity inducing adversarial loss for learning latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data. Compared to the most competitive baselines, we show significant improvements in classification accuracy, under a shift in the data distribution.
  arXiv  Detail & Related papers  (2020-03-10T03:10:41Z)
• Meta-learning framework with applications to zero-shot time-series forecasting [82.61728230984099]
  This work provides positive evidence using a broad meta-learning framework. residual connections act as a meta-learning adaptation mechanism. We show that it is viable to train a neural network on a source TS dataset and deploy it on a different target TS dataset without retraining.
  arXiv  Detail & Related papers  (2020-02-07T16:39:43Z)

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.