Two FFT-based detectors improve GPS multipath detection.
problem Improving GPS positioning accuracy by detecting multipath errors.
method Developed two FFT-based detectors for binary hypothesis tests.
result Detectors can exclude multipath contaminated satellites from navigation solutions.
CNN detects multipath in GNSS signals using correlator output.
problem Multipath contamination errors in GNSS signals.
method Deep Convolutional Neural Networks (CNN) applied to GNSS correlator output.
result CNN accurately detects multipath in GNSS signals.
This research improves multitask learning by creating task-specific pathways.
problem Creating task-specific representations for efficient learning.
method Developed generalization bounds for learning multiple tasks over multiple pathways.
result Proves the superiority of multipath representation over traditional shallow supernets.
CNN improves AMC accuracy under multipath fading channels.
problem Difficulty in designing robust AMC classifiers under multipath fading.
method Proposes a CNN classifier for AMC, robust to channel impairments.
result CNN outperforms existing methods in accuracy and training time.
In this paper, we present a Bayesian channel estimation algorithm for multicarrier receivers based on pilot symbol observations. The inherent sparse nature of wireless multipath channels is exploited by modeling the prior distribution of multipath components' gains with a hierarchical representation of the Bessel K pro…
MultiPath predicts multi-modal future trajectories for better motion planning.
problem Predicting human behavior in uncertain real-world domains like autonomous driving.
method Leverages fixed future state-sequence anchors and regresses offsets with uncertainties.
result Achieves more accurate predictions with an order of magnitude fewer trajectories.
The paper proposes a system to accurately locate devices using millimeter wave radiation.
problem Accurate positioning of devices in urban environments with millimeter wave communications.
method Beamformed fingerprint learning using deep learning and pre-established beamforming patterns.
result Average estimation errors below 10 meters achievable in outdoor scenarios.
A nonlinear channel estimator using complex Least Square Support Vector Machines (LS-SVM) is proposed for pilot-aided OFDM system and applied to Long Term Evolution (LTE) downlink under high mobility conditions. The estimation algorithm makes use of the reference signals to estimate the total frequency response of the …
Extends ESGVI for UWB localization with skewed noise, improving state estimation accuracy.
problem Improving state estimation accuracy in UWB localization with skewed noise.
method Generalizes ESGVI to matrix Lie groups and introduces non-Gaussian factors.
result Improved accuracy in UWB localization with NLOS and multipath effects.
Indoor localization based on SIngle Of Fingerprint (SIOF) is rather susceptible to the changing environment, multipath, and non-line-of-sight (NLOS) propagation. Building SIOF is also a very time-consuming process. Recently, we first proposed a GrOup Of Fingerprints (GOOF) to improve the localization accuracy and reduc…
Adversarial perturbations and RIS interaction vectors improve covert communication.
problem Covert communication in the presence of RISs.
method Designing RIS interaction vectors to balance receiver and eavesdropper detection, adding adversarial perturbations to signals.
result Adversarial perturbations and RIS interaction vectors can be jointly designed to boost covert communications.
ContestTrade uses competitive teams to improve LLM trading performance.
problem High sensitivity to market noise in LLM-based trading systems.
method Internal competitive mechanism, data and research teams, real-time evaluation.
result Significantly outperforms other systems across various metrics.
Unified neural network framework for context-aware Gaussian overbounds in uncertainty propagation.
problem Uncertainty quantification in safety-critical settings requires conservative bounds, but existing methods often fail to compose and are overly conservative.
method Proposes a learning framework that trains neural networks to produce context-aware Gaussian overbounds with provable conservatism.
result The method yields tighter bounds while maintaining conservatism on the enforced grid and in experiments.
New algorithm optimizes beam and rate allocation in mmWave systems for multiple users.
problem Optimizing beam and rate allocation in mmWave systems for multiple users with limited feedback.
method Introducing SAT-CTS, a combinatorial semi-bandit policy with satisficing objective.
result SAT-CTS achieves finite-time regret bounds and reduces satisficing regret in mmWave systems.
Deep neural networks infer downlink CSI from uplink CSI without feedback.
problem Efficient allocation of wireless resources in FDD systems requires accurate DL-CSI.
method Used convolutional neural networks and GANs to infer DL-CSI from UL-CSI.
result Deep learning can accurately predict DL-CSI from UL-CSI for various environments.