New methods optimize wireless base station tuning in dense networks.
problem Optimizing wireless base station parameters in dense cellular networks.
method Parallel contextual bandit approaches using Thompson sampling and deterministic UCB.
result Thompson sampling outperforms manual tuning and contextual UCB in real base station network data.
Paper proposes Q-learning for efficient aerial BS placement to improve fairness in mobile networks.
problem Optimal placement of aerial base stations to enhance fairness in a dynamic user mobility environment.
method Reinforcement learning approach to solve the NP-hard problem of 3D placement.
result Simulation results show increased fairness among users with a reasonable computing time and solution close to optimal.
Electric vehicles (EVs) have been gaining popularity due to their environmental friendliness and efficiency. EV charging station networks are scalable solutions for supporting increasing numbers of EVs within modern electric grid constraints, yet few tools exist to aid the physical configuration design of new networks.…
Proposes AtCoR for predicting bike station usage, improving station network reconfiguration.
problem Challenges in predicting new bike stations due to lack of historical data.
method AtCoR algorithm that predicts both existing and new bike stations using station-centered heatmaps and historical correlations.
result AtCoR outperforms existing models in predicting bike station usage.
FedSpace optimizes ML training on satellites and ground stations.
problem Training machine learning models on satellites with limited bandwidth.
method Federated Learning framework that dynamically schedules model aggregation based on satellite orbits.
result Reduces training time by 1.7 days over state-of-the-art algorithms.
AirRL uses RL to infer urban air quality from selected stations.
problem Inferring fine-grained urban air quality from limited monitoring stations.
method Reinforcement learning model with a dynamic station selector and air quality regressor.
result AirRL achieves highest performance in air quality inference experiments.
Two approaches detect EV charging patterns at stations.
problem Identify charging patterns at electric vehicle charging stations.
method Two approaches: rule-based and hierarchical clustering.
result Hierarchical clustering revealed unexpected charging patterns.
Paper proposes efficient UAV placement for aerial base stations.
problem Efficient placement of UAVs as aerial base stations to serve varying traffic demand.
method Modeling UAV deployment as a non-cooperative game and using a learning-based algorithm to update UAV locations.
result Significant performance gains up to 52% and 74% in terms of throughput and dropped users compared to an optimized baseline algorithm.
A new method uses machine learning to optimize user pairing and association in multicell NOMA networks.
problem Optimizing user pairing and association in multicell non-orthogonal multiple access (NOMA) systems.
method Formulated as a combinatorial optimization problem, solved using a Pointer Network (PtrNet) trained with deep reinforcement learning.
result Achieves near-optimal performance in terms of aggregate data rate, outperforming random heuristics by up to 30%.
Hybrid model improves wind speed prediction accuracy using MLP and WOA.
problem Improving wind speed prediction accuracy for renewable energy control.
method Combining MLP with Whale Optimization Algorithm (WOA) for data preprocessing and model optimization.
result The hybrid MLP-WOA model outperformed standalone MLP model in wind speed prediction accuracy.
Model infers street-level air quality using mobile station data.
problem Inferring air quality from limited mobile station data.
method Variational Graph Autoencoder for matrix completion on graph-based data.
result Model outperforms state-of-the-art approaches in air quality inference.
Kernel-based algorithm optimizes cellular network configuration through multi-task learning.
problem Optimizing network configuration based on field experience and minimizing exploration cost.
method Kernel-based multi-BS contextual bandit algorithm leveraging conditional kernel embedding for multi-task learning.
result The proposed algorithm reduces exploration cost and improves network performance.
This paper tackles non-linear reward optimization in resource allocation problems.
problem Optimizing a non-linear function of long-term average rewards in resource allocation problems.
method Proposes model-based and model-free algorithms to learn optimal policies.
result Model-based algorithm achieves a regret of $\Tilde{O}\left(LKDS\sqrt{\frac{A}{T}}
ight)$ for K objectives combined with a concave L-Lipschitz function. Predict bike flow at station-level with multi-graph CNNs.
problem Fine-grained bike flow prediction for station-level management.
method Multi-graph convolutional neural network model.
result Reduces 25.1% and 17.0% prediction error in New York City and Chicago.
With the rising number of interconnected devices and sensors, modeling distributed sensor networks is of increasing interest. Recurrent neural networks (RNN) are considered particularly well suited for modeling sensory and streaming data. When predicting future behavior, incorporating information from neighboring senso…
The densification of small-cell base stations in a 5G architecture is a promising approach to enhance the coverage area and facilitate the ever increasing capacity demand of end users. However, the bottleneck is an intelligent management of a backhaul/fronthaul network for these small-cell base stations. This involves …
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.
Study models extreme skew surges along French Atlantic coast.
problem Appropriate modelling of extreme skew surges for coastal risk management.
method Peak-over-threshold framework, multivariate generalized Pareto distribution, extreme regression framework.
result Reconstructed historical skew surge time series at stations with limited data.
PeakWeather provides Swiss weather station data for machine learning.
problem Accurate weather forecasting for various activities and decision-making.
method High-quality dataset of surface weather observations from 8 years of Swiss stations.
result Dataset supports a wide range of spatiotemporal tasks.
Challenge forecasts EV charging station usage accurately.
problem Better understanding of EV charging behaviors needed.
method Hierarchical forecasting of EV charging station occupancy.
result Hierarchical forecasting improves EV charging station occupancy predictions.
Develops a combinatorial semi-bandit method for electric vehicle charging station selection.
problem Long-distance navigation for BEVs with unknown charging station availability and performance.
method Combinatorial semi-bandit framework, pre-processing road network, Bayesian modeling, Thompson Sampling, BayesUCB, Epsilon-greedy.
result Demonstrates improved navigation performance on long-distance BEV charging station selection.
Paper presents a simple method for accurate user localization in urban areas.
problem Poor GPS performance in dense urban environments.
method Localization based on pathloss using base station signal strengths and RadioUNet approximations.
result Accurate user location extraction in urban environments.
This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations between stations to predict station-level hourly demand in a large-scale bike-sharing network. Two architectures of the GCNN-DDGF model are explored; G…
New RL approach for coordinating EV charging stations.
problem Uncertainty in EV customer behavior models.
method Markov decision process (MDP) formulation, batch reinforcement learning.
result Optimal charging policy learned with batch reinforcement learning.
System predicts ice formation to improve road safety.
problem Improving road safety through accurate ice formation prediction.
method Combining physical models and machine learning for data correction.
result Machine learning effectively corrects incorrect data in forecasts.
Develops deep learning for optimizing 5G radio resource allocation.
problem Optimizing 5G base station radio resources for diverse QoS requirements.
method Cascaded neural network structure with deep transfer learning for non-stationary conditions.
result Cascaded neural networks outperform fully connected neural networks in QoS guarantee.
This paper optimizes UAV and FeICIC locations in a three-tier LTE-Advanced network.
problem Optimizing UAV and FeICIC locations in a three-tier LTE-Advanced network.
method Integrates UAVs as both UE and BS in LTE-Advanced HetNet, uses CRE, ICIC, 3D beamforming, and genetic algorithms for optimization.
result Heuristic algorithms outperform brute-force techniques in achieving better 5pSE and coverage probability.
Joint sensing and communication network improves target localization and reduces communication load.
problem Efficiently localize multiple targets with reduced communication overhead.
method Multi-base station cooperative sensing with AI-aided clustering and tracking.
result Optimal sub-pattern assignment (OSPA) error less than 60 cm with reduced communication capacity.
This paper shows how differential privacy can be achieved naturally in federated learning over fading channels without artificial noise.
problem Achieving differential privacy in federated learning over fading channels without artificial noise.
method Study of AirFL over multiple-access fading channels with a multi-antenna base station, deriving novel bounds on differential privacy.
result DP can be achieved naturally in federated learning over fading channels without artificial noise, revealing convergence-privacy trade-offs.
A framework assesses the quality of crowdsourced weather data.
problem Quality control and assessment of crowdsourced weather data from third-party stations.
method Proposes a simple, scalable, and interpretable AI/Stats/ML framework to assess TPAWS data.
result Demonstrates the performance of the framework using synthetic and real data.
Develops a neural network for MIMO-OFDM systems with low-resolution ADCs.
problem Low-resolution ADCs make channel information blind to the BS, complicating equalization.
method Supervised learning framework with a new activation function and unsupervised loss.
result Proposed equalizer outperforms existing ones, especially in unclear channel conditions.
Paper proposes RL algorithms for optimizing cellular network performance.
problem Improving cellular network performance against wireless impairments.
method Formulated as a reinforcement learning problem and developed two algorithms: PC and SON.
result RL algorithms outperform industry standards in simulated environments.
Optimizes deep reinforcement learning for energy-efficient video streaming.
problem Minimizing energy consumption in video streaming over mobile networks.
method Integrates DDPG algorithm with partially known model to reduce signaling overhead and improve convergence speed.
result Proposed policy converges to optimal policy with improved convergence speed.
A federated graph learning approach improves EV charging demand forecasting while protecting against cyberattacks.
problem Cybersecurity risk and data heterogeneity in EV charging demand forecasting.
method Federated Graph Neural Network (GNN) model with global attention mechanism and credit-based function.
result Enhanced robustness and prediction accuracy in EV charging demand forecasting.
Sparse point observations can provide useful local constraints, but their benefit for radar-like fields depends on the training loss, uncertainty representation, and how observation support is encoded in the model.
problem Improving dense radar-field forecasts with sparse point observations
method A multimodal graph neural network nowcasting system over the Nordic radar domain
result Each source improves a different part of the forecast problem
Paper proposes ICWT method to balance sewer flow and WWTP capacity.
problem Balancing spatial mismatch of sewer flow and WWTP capacity.
method Hydraulic model and LSTM for multi-step ahead water level prediction.
result LSTM outperforms other models in predicting water levels.
India runs the fourth largest railway transport network size carrying over 8 billion passengers per year. However, the travel experience of passengers is frequently marked by delays, i.e., late arrival of trains at stations, causing inconvenience. In a first, we study the systemic delays in train arrivals using n-order…
Deep learning models are growing, posing new mathematical challenges.
problem Mathematical challenges in training, inference, generalization, and optimization of deep models.
method Formal mathematical analysis and communication with mathematicians, statisticians, and computer scientists.
result A set of new mathematical challenges in deep learning.
Gaussian Process Regression accurately models daily pan evaporation in humid climates.
problem Precise estimation of pan evaporation in humid climates using data-based methods.
method Gaussian Process Regression and other machine learning techniques were used to estimate pan evaporation.
result GPR models with specific meteorological parameters performed best in estimating pan evaporation.
Deep RL methods improve resource allocation in uncertain environments.
problem Optimizing resource allocation in dynamic, uncertain environments.
method Developed three DDPG-based approaches to handle constraints and combinatorial action spaces.
result Demonstrated improved performance over existing methods on real and semi-real data.
GraphSVR forecasts urban air pollution robustly across stations and seasons.
problem Nonlinear, nonstationary, spatiotemporally dependent urban air pollution forecasting challenges.
method Combines graph convolutional learning and support vector regression.
result GraphSVR improves predictive accuracy and maintains stable performance across seasons and outlier-prone episodes.
Surveying low-cost sensors for air quality monitoring and calibration.
problem Limited spatial resolution due to expensive environmental monitoring stations.
method Low-cost sensors with machine learning for calibration.
result Machine learning improves sensor accuracy over time.
Integrating wind power into the grid is challenging because of its random nature. Integration is facilitated with accurate short-term forecasts of wind power. The paper presents a spatio-temporal wind speed forecasting algorithm that incorporates the time series data of a target station and data of surrounding stations…
Hierarchical FL reduces latency in HCNs by sharing model updates.
problem Latency and privacy issues in federated learning across heterogeneous cellular networks.
method Hierarchical federated learning, gradient sparsification, periodic averaging.
result Significant reduction in communication latency without compromising model accuracy.
Low-cost water-level tracking using LTE power metrics and wavelet analysis.
problem Real-time water-level monitoring across many locations with fixed instruments.
method Extracts per-antenna RSRP, RSSI, and RSRQ, applies CWT to RSRP, and uses a neural network to track water-level changes.
result Achieves root-mean-square and mean-absolute errors of 0.8 cm and 0.5 cm, respectively, under line-of-sight conditions.
Framework predicts and prepares for rain-induced microwave link attenuation.
problem Severe signal attenuation due to weather conditions degrades network performance.
method Predictive Network Reconfiguration (PNR) framework using LSTM for attenuation prediction and MSNR for dynamic routing.
result Framework improves network utilization by more than 200% compared to reactive algorithms.
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.
Artificial Neural Network predicts PM2.5 pollution with low-cost sensors.
problem Costly and bulky PM2.5 monitoring instruments limit real-time, high-resolution data.
method Analytical equations derived using Artificial Neural Network (ANN).
result RMSE of 1.7973 ug/m3 and R2 of 0.9986 for eight predictors; 7.5372 ug/m3 and 0.9708 for three predictors.