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.
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.
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.
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.
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.
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.
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…
New framework uses MABs for better WLAN performance.
problem Maximizing enterprise WLAN performance with complex AP and station interactions.
method Empowered APs and stations with agents using Thompson sampling to learn optimal channel and AP selection.
result Adaptive framework using MABs outperforms static configurations in various scenarios.
Owing to the ever-increasing demand in wireless spectrum, Cognitive Radio (CR) was introduced as a technique to attain high spectral efficiency. As the number of secondary users (SUs) connecting to the cognitive radio network is on the rise, there is an imminent need for centralized algorithms that provide high through…
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%.
ViWi dataset framework tackles wireless communication problems with visual data.
problem Leveraging visual sensory information in wireless communications.
method Developed a parametric, systematic, and scalable data generation framework using 3D modeling and ray-tracing.
result Offers a way to generate training and testing datasets for assessing machine learning solutions.
Paper proposes a graph model for optimal AP deployment in indoor optical wireless networks.
problem Challenges in deploying optical wireless networks due to LoS requirement and limited range.
method Graph modeling approach to identify minimum number of APs and their optimal locations.
result Optimal deployment of APs ensures connectivity and minimizes interference in indoor environments.
The paper discusses scalable learning for wireless data-driven systems.
problem Expanding data volume and model complexity limit centralized learning solutions.
method Discusses scalable architecture and local learning strategies.
result Promising research directions in scalable data-driven wireless communications.
Federated learning enables ML in wireless without central data.
problem Centralized ML in wireless is impractical due to data privacy and overhead.
method Federated learning, where data stays local and ML is done remotely.
result Federated learning is suitable for 5G and other wireless applications.
Paper addresses FL over wireless networks, optimizing learning and resource allocation.
problem Training FL algorithms over wireless networks with limited resources and errors.
method Formulated as an optimization problem to minimize FL loss function, derived expected convergence rate, derived optimal transmit power, optimized user selection and RB allocation.
result Joint framework reduces FL loss by up to 10% and 16% compared to alternatives.
Complex-valued neural networks improve wireless fingerprinting robustness.
problem Distinguish between devices sending the same message, robust against spoofing.
method Used complex-valued neural networks for supervised learning of fingerprints from wireless signals.
result Noise augmentation by adding white Gaussian noise leads to significant performance gains.
Generative model learns wireless channel distributions efficiently.
problem Learning precise wireless channel distributions for optimal communication.
method Physics-informed sparse Bayesian generative modeling (SBGM) with compressed data.
result Model learns channel parameters from compressed AP observations, is physically interpretable, and generalizes across different systems.
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.
Optimizes wireless power control using graph neural networks and counterfactual optimization.
problem Mitigating interference in wireless networks with multiple transmitter-receiver pairs.
method Graph neural network architecture combined with unsupervised primal-dual counterfactual optimization.
result Guarantees a minimum rate constraint that adapts to network size, balancing user rates.
A novel method for efficient CDRL over wireless networks.
problem Challenges in collaborative deep reinforcement learning over wireless networks.
method Semantic-aware heterogeneous federated deep reinforcement learning (HFDRL) algorithm.
result Superior performance compared to state-of-the-art baselines.
DRL-DPT improves energy efficiency in wireless networks with deterministic power control.
problem Severe performance degradation in traditional ICIC schemes with complex interference patterns.
method Deep Reinforcement Learning with Deterministic Policy and Target (DRL-DPT) framework.
result Consistently outperforms existing schemes in terms of energy efficiency and throughput.
Edge devices learn a global model collaboratively over wireless channels.
problem Learning a global model from edge devices with imperfect channel state information.
method Proposed analog aggregation scheme, receive beamforming at PS, and convergence analysis.
result Performance improvement with more PS antennas, even with imperfect CSI.
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.
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.
CHOOSE enhances shallow Transformers for wireless symbol detection.
problem Improving wireless symbol detection with shallow Transformers.
method Introducing autoregressive latent reasoning steps within hidden space.
result Lightweight Transformers achieve comparable performance to deep models.
Study compares different levels of supervision for training graph embeddings in wireless networks.
problem Improving power control in wireless interference networks.
method Training graph neural networks (GNNs) with different levels of supervision (supervised, unsupervised, self-supervised).
result Different levels of supervision impact system-level throughput, convergence, and generalization.
GAN-based spoofing attacks improve wireless signal authentication.
problem Improving wireless signal authentication against sophisticated spoofing attacks.
method Generative Adversarial Network (GAN) for generating synthetic signals.
result GAN-based spoofing attacks significantly increase the success probability of wireless signal spoofing.
State-augmented algorithm optimizes wireless network resource management.
problem Optimizing resource allocation in multi-user wireless networks.
method Proposes a state-augmented algorithm using dual variables.
result Feasible and near-optimal resource decisions achieved.
Paper proposes DRL for unsupervised IoT localization.
problem Challenges in unsupervised localization of IoT devices.
method Modeling localization as MDP, using DRL with reward-setting and RSS measurements.
result Demonstrated effectiveness of DRL in wireless localization.
The paper develops a learning algorithm for distributed training and inference in wireless networks.
problem Challenges of leveraging machine learning in highly distributed wireless networks.
method Developed a learning algorithm and architecture for distributed training and inference.
result Inference propagates and fuses across a network, with benefits over state-of-the-art techniques.
AutoTune learns wireless identifiers for facial recognition in real-world settings.
problem Facial recognition requires extensive user training, making it impractical for widespread deployment.
method Uses ambient wireless identifiers to train deep neural networks for facial recognition without user effort.
result Demonstrates a system that continuously refines facial recognition using wireless identifiers over time.
Survey on ML for wireless network optimization across PHY, MAC, and network layers.
problem Improving wireless network performance using machine learning.
method Comprehensive review of ML-based techniques for wireless network optimization.
result Machine learning can significantly enhance wireless network QoS and QoE across all layers.
Paper presents a WiFi-based indoor sensor localization technique.
problem Indoor localization in wireless sensor networks.
method Zoning-based localization using statistical learning.
result Efficient sensor zone determination in indoor environments.
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.
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.
This paper tackles efficient resource control in IoT edge computing using deep reinforcement learning.
problem Efficient allocation and scheduling of limited resources in IoT edge computing systems.
method Formulated as a CTMDP model, used deep reinforcement learning (RL) to approximate value functions and solve the MDP problem.
result Significant performance improvement over baseline algorithms and RL algorithms based on other architectures.
Improves forecasting accuracy and uncertainty characterization for spatio-temporal data.
problem Lack of uncertainty characterization in classical and deep learning models for spatio-temporal data.
method Bayesian inference using particle flow for approximating the posterior distribution of hidden states.
result Our approach provides better uncertainty characterization while maintaining comparable accuracy.
Improved DL models robust against adversarial attacks for wireless signal classification.
problem Adversarial attacks on deep learning-based wireless signal classifiers.
method Knowledge distillation and network pruning followed by adversarial training.
result Proposed models achieve better robustness and higher accuracy than standard models.
Autoencoders improve wireless protocol classification with fewer parameters and higher accuracy.
problem Classifying wireless protocols with high accuracy and low computational complexity.
method Training FC deep learning networks using multiple denoising autoencoders.
result AE-trained networks achieve higher accuracy than reference FC networks for various SNR values.
Machine learning enhances wireless network authentication for diverse devices.
problem Complex dynamic wireless environments challenge conventional authentication methods.
method Intelligent authentication using machine learning for diverse physical layer attributes.
result Machine learning-based authentication provides cost-effective, reliable, and situation-aware security.
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.
Optimizes convergence time of federated learning over wireless networks.
problem Limited resource blocks in wireless networks affect federated learning convergence time and performance.
method Formulates an optimization problem to minimize convergence time while optimizing performance, proposes a probabilistic user selection scheme and uses ANNs for estimation.
result Improves convergence time and performance of federated learning over wireless networks.
Critical to evaluating the capacity, scalability, and availability of web systems are realistic web traffic generators. Web traffic generation is a classic research problem, no generator accounts for the characteristics of web robots or crawlers that are now the dominant source of traffic to a web server. Administrator…
A-FADMM improves FL scalability and privacy via wireless channel perturbations and interference.
problem Challenges in model training due to wireless channel randomness and interference.
method Formulated a novel constrained optimization problem and proposed A-FADMM framework.
result Proves convergence and privacy guarantees for A-FADMM under time-varying channels.