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.
QFlow learns to prioritize video streaming to improve quality of experience.
problem Inconsistent video streaming quality due to network inefficiency.
method Develops a learning approach to dynamically allocate resources for video streaming.
result Demonstrates improved video quality for all clients at a wireless access point.
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.
Deep learning improves SCMA decoding and codebook construction for 5G wireless networks.
problem Designing low complexity high accuracy decoding algorithms and constructing optimal SCMA codebooks.
method Proposed a deep neural network (DNN) called DL-SCMA and an autoencoder (AE-SCMA) to learn and reconstruct SCMA modulated signals.
result Deep learning SCMA decoder outperforms conventional algorithms in BER, SER, and computational complexity.
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.
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.
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.
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.
Wireless sensor networks are composed of distributed sensors that can be used for signal detection or classification. The likelihood functions of the hypotheses are often not known in advance, and decision rules have to be learned via supervised learning. A specific such algorithm is Fisher discriminant analysis (FDA),…
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.
Graph neural networks optimize radio resource management policies for wireless networks.
problem Optimizing user selection and power control in wireless networks with fairness constraints.
method Formulated as a Lagrangian dual problem, RRM policies are parameterized by a GNN architecture trained on channel conditions.
result The method achieves superior tradeoff between average and 5th percentile rates, demonstrating fairness.
Meta-DRL improves resource allocation in O-RAN networks.
problem Dynamic resource allocation in O-RAN networks.
method Meta Deep Reinforcement Learning (Meta-DRL) inspired by MAML.
result 19.8% improvement in network management performance.
RFML systems are vulnerable to adversarial attacks, especially in OTA transmissions.
problem Vulnerability of RFML systems to adversarial machine learning attacks.
method Differentiated adversarial threats, developed methodology for evaluating vulnerabilities, used Fast Gradient Sign Method.
result RFML systems are vulnerable to adversarial examples, even in OTA attacks.
Meta-learning improves drone trajectory design for dynamic wireless networks.
problem Designing optimal trajectories for energy-constrained drones in dynamic network environments.
method Proposes a meta-learning algorithm to adaptively tune a reinforcement learning solution for trajectory design.
result Meta-tuned RL yields faster convergence and improved communication performance compared to baseline algorithms.
Paper tackles structure learning of sparse GGMs over multiple access networks.
problem Estimating sparse Gaussian Graphical Model structure from multiple local machines with limited communication.
method Proposes Signs and Uncoded methods for reliable structure learning under power and bandwidth limitations.
result Both methods can recover the structure with high probability for large enough sample size.
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.
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.
Scheduling and power allocation improve federated learning efficiency in NOMA networks.
problem Efficiently scheduling and allocating power for federated learning in bandwidth-limited wireless networks.
method Proposed a scheduling policy and power allocation scheme using NOMA to maximize data rate and convergence speed.
result Simulation results show improved federated learning accuracy in NOMA networks.
Study investigates FL performance over a noisy downlink, showing analog approach outperforms digital.
problem Impact of bandwidth-limited downlink on federated learning performance.
method Modelled downlink and uplink channels; proposed digital and analog downlink approaches; analyzed convergence behavior.
result Analog downlink approach provides significant improvement over digital approach, especially with biased data distribution.
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.
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.
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.
An adversarial machine learning approach is introduced to launch jamming attacks on wireless communications and a defense strategy is presented. A cognitive transmitter uses a pre-trained classifier to predict the current channel status based on recent sensing results and decides whether to transmit or not, whereas a j…
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.
A novel Gradient-Based Multiple Access algorithm for distributed learning over fading channels.
problem Distributed learning over multiple access fading channels.
method Gradient-Based Multiple Access (GBMA) algorithm, using analog gradients and common shaping waveforms.
result GBMA can approach the convergence rate of centralized gradient descent in large networks.
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.
This article reviews ML applications in wireless networks over 30 years.
problem Achieving high-rate, low-latency, and reliable wireless services in complex networks.
method Exploration of various ML algorithms including supervised, unsupervised, reinforcement, and deep learning.
result Machine learning algorithms have been successfully applied to wireless networks.
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.
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.
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.
Deep RL for uncoordinated cognitive radios finds near-optimal policies.
problem Resource allocation in uncoordinated cognitive radio networks.
method Distributed deep reinforcement learning algorithm.
result Algorithm converges to near-optimal policies in finite 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.
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.
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…
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.