Paper proposes scalable GP framework for cost-efficient wireless traffic prediction.
problem Wireless traffic prediction for C-RANs to improve spectrum and energy efficiency.
method Scalable Gaussian process framework with ADMM for parallel hyper-parameter optimization and cross-validation based optimal fusion strategy.
result Proposed scalable GP model outperforms state-of-the-art approaches in wireless traffic prediction.
Improved GP model forecasts wireless demand extremes with better uncertainty quantification.
problem Forecasting extreme wireless demand spikes and troughs for network optimization.
method Designed a feature embedding kernel for Gaussian Process models.
result 32% reduction in short-term extreme value prediction error vs. S-ARIMA.
Predicts user traffic for better resource management in cellular networks.
problem Lack of research on traffic prediction in cellular networks at the user level.
method Statistical, rule-based, and deep machine learning methods.
result Deep machine learning outperforms linear statistical learning after a threshold number of previous observations.
This study analyzes wireless network data using classification techniques.
problem Identify normal and abnormal traffic in wireless networks.
method Used WEKA software with predefined anomaly classes from the MAC layer.
result Classification algorithms show success in detecting anomalies.
This chapter deals with decentralized learning algorithms for in-network processing of graph-valued data. A generic learning problem is formulated and recast into a separable form, which is iteratively minimized using the alternating-direction method of multipliers (ADMM) so as to gain the desired degree of paralleliza…
A DRL-based strategy improves vehicle tracking accuracy while saving energy.
problem Enhancing vehicle tracking accuracy in WSNs without increasing energy consumption.
method Decentralized strategy with dynamic reinforcement learning to adjust sensing areas.
result Simulation results demonstrate superior performance of DRL-aided design.
Motivated by the prediction of cell loads in cellular networks, we formulate the following new, fundamental problem of statistical learning of geometric marks of point processes: An unknown marking function, depending on the geometry of point patterns, produces characteristics (marks) of the points. One aims at learnin…
Resource allocation improved using machine learning from terminal positions.
problem Optimizing resource allocation in next-gen wireless systems with fast-changing channel conditions.
method Supervised machine learning using position information of mobile terminals.
result Coordinates-based resource allocation performs similarly to traditional CSI-based methods.
Supporting ultra-reliable low-latency communications (URLLC) is a major challenge of 5G wireless networks. Stringent delay and reliability requirements need to be satisfied for both scheduled and non-scheduled URLLC traffic to enable a diverse set of 5G applications. Although physical and media access control layer sol…
This article improves communication efficiency in distributed ML over wireless networks.
problem Achieving high ML inference accuracy at scale with zero communication latency.
method Optimizing communication payload types, techniques, scheduling, and ML architectures.
result Communication-efficient and distributed learning frameworks are presented.
Proposes a method to predict content preferences for mobile users in decentralized caching networks.
problem Determining caching schemes for decentralized caching networks with mobile traffic.
method Formulates content preference learning as a DRMTL problem, integrates mobility prediction, and uses ADMM for optimization.
result Mobility-aware content preference learning provides more accurate predictions and improved hit ratios.
Proposes efficient calibration for indoor localization models.
problem Calibration data scarcity in wireless indoor localization.
method Uses synthetic labels and prediction sets to fine-tune a predictor and estimate bias.
result Yields rigorous coverage guarantees for prediction sets.
TM-CNN predicts lane-level traffic speeds considering volume impact.
problem Aggregated lane-level traffic speed prediction and volume impact.
method Two-stream multi-channel CNN, data conversion, two-stream deep neural network, loss function.
result TM-CNN outperforms existing models in multi-lane traffic speed prediction.
Model predicts traffic speed using urban incidents.
problem Accurately predicting traffic speed in urban areas.
method Deep Incident-Aware Graph Convolutional Network (DIGC-Net).
result Model outperforms competing benchmarks in traffic speed prediction.
Recursive filtering predicts wireless interference levels accurately.
problem Predicting interference in wireless networks.
method Designing a recursive predictor using Kalman filtering and ARMA model.
result Good accuracy of predicted interference values compared to true values.
Model predicts traffic incident duration and identifies key features.
problem Predict traffic incident duration and identify critical features.
method Multi-task learning framework with sparsity optimization and ADMM algorithm.
result Model predicts incident duration and identifies key features effectively.
The paper compares LSTM and ARIMA for predicting and classifying network traffic.
problem Predicting and classifying network traffic in cellular networks.
method Employed LSTM and ARIMA for time series prediction and classification.
result LSTM outperforms ARIMA in general, especially with longer training series and optimal feature selection.
MSCT predicts post-crash traffic speed using causal inference.
problem Time-varying confounding bias in post-crash traffic prediction.
method Marginal Structural Causal Transformer (MSCT) incorporating Marginal Structural Models and balanced loss function.
result MSCT outperforms state-of-the-art models in multi-step-ahead prediction.
DistPre predicts traffic speeds efficiently for large networks.
problem Fine-grained, accurate speed prediction for large-scale transportation networks.
method Customizes LSTM models on a cluster, sharing trained models between detectors.
result Efficient and accurate fine-grained traffic-speed prediction.
Deep learning predicts V2I channel responses for high-mobility wireless communications.
problem Efficient channel estimation for high-mobility V2I communications.
method Developed a deep neural network-based channel prediction method.
result Deep neural networks can learn and predict V2I channel properties in real-time.
Study uncovers uncertainty in traffic prediction models across cities.
problem Lack of interpretability in deep learning models for traffic prediction.
method Investigated uncertainty quantification methods for image-based traffic prediction.
result Meaningful uncertainty estimates can be recovered for traffic prediction.
Graphs model traffic interactions, reducing prediction error by 30%.
problem Traffic prediction accuracy with complex interactions.
method Graph Neural Networks (GNN) for modeling vehicle interactions.
result Prediction error decreases by 30% with GNNs compared to non-interaction models.
Wi-GATr learns to simulate wireless signals with high accuracy and speed.
problem Inaccurate wireless signal propagation models limit modern communication system design.
method Wi-GATr uses a Geometric Algebra Transformer to learn from scene primitives.
result Wi-GATr achieves more accurate predictions than existing methods.
Traffic actors' future motion predicted using a hybrid graph model.
problem Predicting long-term behaviors of traffic actors in complex scenes.
method A hybrid graph model with nodes for actors and traffic elements, and edges for interaction types.
result TrafficGraphNet achieves state-of-the-art trajectory prediction accuracy.
Study estimates and predicts dynamic traffic OD flows for improved DTA models.
problem Estimating and predicting time-varying OD trip tables for dynamic traffic assignment.
method Bi-level optimisation for OD flow estimation and time series prediction for OD demand.
result High capability of proposed OD demand estimation method to reduce DTA model error.
ATFM predicts traffic flow using attention mechanism and neural networks.
problem Predicting traffic flow with diverse factors integration.
method Unified neural network (ATFM) with attention mechanism and ConvLSTM units.
result ATFM outperforms in predicting citywide short-term/long-term traffic flow.
Deep learning predicts traffic flow on Sydney motorways.
problem Challenging traffic flow prediction due to inter-dependencies.
method Advanced deep learning framework using CNN-LSTM.
result Deep learning models outperform traditional methods.
CONTINA provides adaptive confidence intervals for traffic demand prediction.
problem Uncertainty in future traffic demand predictions and the need for valid confidence intervals.
method Adaptive confidence interval method that adjusts based on deployment errors.
result Valid confidence intervals with shorter lengths and theoretical coverage guarantee.
Bayesian model improves traffic prediction with uncertainty estimates.
problem Lack of uncertainty estimates in deep-learning traffic models.
method Proposes a Bayesian recurrent neural network with spectral normalization.
result Spectral normalization improves uncertainty estimates and generalizability.
In its simplest form, the traffic flow prediction problem is restricted to predicting a single time-step into the future. Multi-step traffic flow prediction extends this set-up to the case where predicting multiple time-steps into the future based on some finite history is of interest. This problem is significantly mor…
New techniques improve channel prediction in noisy wireless systems.
problem Predicting channels in wireless communication systems from noisy observations.
method Adapted sequence-to-sequence models and transformers with reverse positional encoding and reversed encoder outputs.
result Improved robustness and relationship capture in channel prediction models.
Federated learning optimizes task and resource allocation in balloon networks.
problem Minimizing energy and time consumption in task computation and transmission.
method SVM-based federated learning algorithm to dynamically adjust user associations, service sequences, and task allocations.
result Reduces the weighted sum of energy and time consumption by up to 16.1%.
Proposes a dynamic model for urban traffic volume prediction.
problem Urban traffic volume prediction for better traffic management and driver planning.
method Combines bidirectional LSTM, attention mechanism, and external features.
result Improves prediction precision by 3-7 percent on NYC-Taxi and NYC-Bike datasets.
Paper develops deep neural networks for wireless tasks with reduced complexity.
problem Reducing training complexity for deep neural networks in wireless systems.
method Develops permutation invariant DNNs (PINNs) leveraging wireless task properties.
result Demonstrates dramatic reduction in training complexity for PINNs.
Proposes a neural network for estimating traffic density uncertainty.
problem Lack of uncertainty estimates in deep learning traffic prediction models.
method Quantile Graph Wavenet, a Spatio-Temporal neural network trained to estimate density.
result Produces uncertainty estimates efficiently without sampling.
Improved LSTM and ARIMA model for traffic flow forecasting.
problem Poor stability, high data requirements, and adaptability issues in existing traffic flow prediction methods.
method Combination prediction method based on improved LSTM and ARIMA models.
result The SDLSTM-ARIMA model achieves higher accuracy in traffic flow prediction.
Deep neural network predicts traffic flow on city maps.
problem Short-term traffic flow prediction on high-resolution city maps.
method UNet-based deep convolutional neural network with densely connected layers.
result Best performance on the Traffic4cast challenge 2019.
In industrial environments, an increasing amount of wireless devices are used, which utilize license-free bands. As a consequence of these mutual interferences of wireless systems might decrease the state of coexistence. Therefore, a central coexistence management system is needed, which allocates conflict-free resourc…
Predicts traffic flow using reinforcement learning and sensor data.
problem Accurately predict expanding and evolving long-term streaming traffic networks.
method Formulates the problem as a continuous reinforcement learning task, where the agent predicts future traffic based on sensor data.
result The approach improves accuracy in predicting traffic flow by updating the agent's state representation over time.
A new DNN structure reduces complexity for wireless tasks.
problem Reducing complexity in training deep neural networks for wireless tasks.
method Proposes a DNN with special structure using permutation invariant a priori information.
result The proposed DNN structure reduces training complexity and model parameters.
Study predicts traffic congestion based on population mobility data.
problem Predicting traffic congestion in multimodal transport networks.
method Machine learning methods applied to population mobility data.
result Likely prediction of congestion based on population movements.
Neural networks predict traffic flow in smart cities.
problem Forecasting stochastic and nonlinear traffic flow.
method Various recurrent neural networks trained on intersection data.
result Vector output model with gated recurrent units performed best.
QTIP improves traffic prediction in sudden disruptions.
problem Traffic models fail during sudden disruptions.
method Simulation-based framework for real-time adaptation.
result QTIP improves traffic prediction in critical minutes of incidents.
Improved traffic forecasting model handles missing data.
problem Short-term traffic forecasting with missing values.
method Proposed SBU-LSTM architecture with bidirectional and unidirectional LSTM.
result Superior performance in accuracy and robustness for network-wide traffic prediction.
Proposes a feature transformation for spatio-temporal traffic models to improve performance and transferability.
problem Limited transferability of deep learning models for traffic flow prediction across different locations.
method Integrates Newell's traffic flow estimators to capture broader dynamics and incorporates spatial dependencies.
result Improves model performance in predicting traffic flows over different horizons.
Paper uses machine learning to optimize UAV deployment for traffic offloading.
problem Optimizing UAV deployment for efficient traffic offloading from ground BSs.
method LSTM for traffic prediction, KEG algorithm for service area determination, multi-access techniques comparison.
result RSMA reduces up to 24% total power consumption compared to conventional methods.
Paper proposes a new tensor imputation method for spatiotemporal traffic data with missing patterns.
problem Imputation of corrupted or incomplete traffic data.
method Truncated tensor Schatten p-norm (TSpN) for spatiotemporal traffic data imputation.
result The proposed method outperforms other state-of-the-art tensor-based imputation models in various missing cases.
Hybrid model improves traffic flow prediction accuracy.
problem Predicting traffic flow with high accuracy in short-term future.
method A hybrid model combining hidden Markov model and LSTM.
result Significant performance gains over conventional methods.