This paper uses reinforcement learning to predict and allocate resources for coexisting wireless systems.
problem Coexistence of wireless systems in industrial environments leads to resource conflicts.
method Reinforcement learning, specifically deep Q-networks and double deep Q-networks, for resource allocation prediction.
result A prediction accuracy of at least 98% can be achieved in coexistence scenarios.
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.
Deep RL optimizes power control for wireless multicast systems.
problem Optimal power control is intractable due to a large state space.
method Deep reinforcement learning with function approximation via neural networks.
result Optimal power control can be learned for large systems.
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.
Deep RL improves power control and scheduling for wireless multicast systems.
problem Scalable power control and scheduling for wireless multicast networks.
method Deep reinforcement learning with function approximation using a deep neural network.
result Deep RL can learn optimal power control policies for large systems.
The paper develops methods to optimize resource allocation in wireless systems using deep neural networks.
problem Designing optimal resource allocation policies in wireless communication systems with stochastic constraints.
method Developed learning methodologies to solve optimization problems in the dual domain using deep neural networks (DNNs).
result Demonstrated strong performance of the proposed approach on various wireless resource allocation problems.
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.
New algorithm learns optimal resource allocation in wireless systems without models.
problem Learning optimal resource allocation in wireless systems without system models.
method Developed a model-free primal-dual algorithm using smoothed surrogates of constrained problems.
result The algorithm can make the gap between optimal values and dual values arbitrarily small.
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.
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.
Reinforcement learning improves wireless systems' rate adaptation.
problem Optimizing rate adaptation in 4G/5G systems using ACK/NACK feedback.
method Formulated as a Multi-Armed Bandit problem, proposed binary search algorithm with PAC guarantees.
result Achieved PAC solution for OLLA with binary search, outperforming UCB methods.
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.
Designs adaptive controller for networked control systems with wireless data transmission.
problem Adaptive control in networked systems with unreliable wireless channels.
method Upper Confidence Bounds for Networked Control Systems (UCB-NCS) learning rule.
result Non-asymptotic performance guarantees with a regret bound of O(C√T).
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.
This letter tackles channel assignment in uplink wireless communication systems.
problem Maximizing the sum rate of all users in uplink wireless communication systems with integer channel assignment constraints.
method A convex optimization based algorithm is used to find the optimal channel assignment. Machine learning approaches, including CNNs, FNNs, random forest, and GRUs, are employed to reduce computation time.
result Machine learning methods largely reduce computation time with slightly compromised prediction accuracy.
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.
Deep RL tackles mmWave backhaul resource allocation challenges.
problem Limited backhaul capacity and dynamic data rates in mmWave systems.
method Deep reinforcement learning (DRL) to predict and allocate backhaul resources.
result Efficient utilization of backhaul resources through DRL.
Con-TS optimizes wireless link throughput with latency constraints.
problem Optimizing rate selection for wireless links with latency constraints.
method Proposes Con-TS, a constrained Thompson sampling algorithm for stochastic MAB problems.
result Con-TS achieves upper bounds on expected constraint violations and throughput loss.
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 paper reduces labeling costs for meta-learning in wireless systems.
problem Expensive labeling in meta-learning for wireless systems.
method Bayesian active task selection mechanism.
result Reduces the number of labeling steps required for meta-learning.
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…
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.
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.
New system preserves message meaning in wireless networks, improving data rate.
problem Efficiently transmitting message meaning in wireless networks.
method Modeling semantics as hidden random variables, using Information Bottleneck for compression.
result 20 dB SNR improvement for semantic communication.
Optimizes wireless systems using deep learning without supervision.
problem Optimizing resource allocation and transceivers in wireless networks.
method Introduces unsupervised and reinforced-unsupervised learning frameworks for variable and functional optimization problems.
result Demonstrates the effectiveness of the learning frameworks through a user association problem.
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.
Novel beamforming method reduces errors in wireless networks.
problem Mitigating channel errors in wireless networks with relays.
method Low-rank and cross-correlation techniques for robust distributed beamforming.
result The proposed LRCC-RDB technique significantly improves SINR performance.
FLORAS uses orthogonal sequences for SISO FL, offering both DP and convergence guarantees.
problem Privacy-preserving wireless federated learning in SISO systems.
method Leverages orthogonal sequences to eliminate CSIT requirement and provide DP guarantees.
result FLORAS achieves a smooth tradeoff between convergence rate and DP levels.
Deep actor-critic learning optimizes power control in mobile networks.
problem Optimizing power control in large-scale wireless mobile networks.
method Multi-agent deep reinforcement learning with deep deterministic policy gradient.
result The algorithm maximizes a global utility function in a distributed manner.
Unified Siamese network for wireless positioning and channel charting.
problem Wireless positioning and channel charting using CSI.
method Unified Siamese neural network architecture for both supervised and unsupervised learning.
result Siamese networks achieve similar or better performance than existing methods.
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.
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 develops DL methods for signal demodulation in wireless comms.
problem Signal demodulation in wireless communications.
method Proposes DBN-SVM and AdaBoost demodulators using real modulated signals.
result Proposed DBN-SVM and AdaBoost demodulators outperform traditional methods.
Two clustering algorithms optimize edge controller placement in wireless networks.
problem Optimizing edge controller placement in wireless edge networks.
method Deterministic annealing based clustering algorithms ECP-LL and ECP-LB.
result The algorithms achieve better balance between synchronization and delay costs.
Paper tackles energy efficiency in FL over wireless networks.
problem Energy efficient transmission and computation resource allocation for FL over wireless networks.
method Formulated as an optimization problem, iterative algorithm derived with closed-form solutions for time, bandwidth, power, and accuracy.
result Proposed algorithms reduce up to 59.5% energy consumption compared to conventional FL methods.
Deep reinforcement learning optimizes power allocation in wireless networks.
problem Challenges in optimizing power allocation in large wireless networks.
method Distributively executed dynamic power allocation scheme using deep Q-learning.
result Achieves near-optimal power allocation in real-time with delayed CSI.
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.
Automated image annotation improves model training accuracy.
problem Training deep learning models with labeled data.
method Combining wireless localization and cameras for automatic image annotation.
result Demonstrated feasibility and benefits of automatic annotation.
Adversarial attacks are ineffective when the surrogate models are trained with different channel effects.
problem Adversarial attacks against wireless signal classifiers are ineffective when the adversary's surrogate model differs from the transmitter's classifier.
method Investigated different topologies to analyze how channel effects influence the performance of adversarial attacks.
result Surrogate models trained with different channel-induced inputs severely limit the attack 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.
Generative diffusion models improve channel sampling from limited data.
problem Challenges in channel modelling and data collection for wireless systems.
method Diffusion model with U-Net architecture for frequency domain synthesis.
result Stable training and diverse high-fidelity samples generated from true channel distribution.
This paper addresses privacy in federated learning with wireless clients and base stations.
problem Privacy of clients' data in federated learning with hierarchical wireless architecture.
method Derives communication cost limits and introduces private aggregation schemes tailored for hierarchical wireless systems.
result Private aggregation schemes reduce communication costs by multiplicative factors compared to information-theoretic limits.
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.
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.
The paper studies calibration in ML models for wireless networks, showing key theoretical and practical insights.
problem Ensuring ML models in wireless networks deliver well-calibrated confidence scores for reliable decision-making.
method Theoretical analysis and simulation-based experiments using Platt scaling and isotonic regression.
result Well-calibrated models improve the system's minimum achievable OP and are part of a broader class of predictors.
The paper explores the trade-off between recommendation system performance and bandwidth usage.
problem Balancing recommendation system performance with wireless bandwidth constraints.
method Analyzes two scenarios: multi-armed bandit with context and latent structure exploitation.
result Demonstrates a tradeoff between regret and bandwidth usage, with tight bounds for some instances.