Adversaries with multiple antennas can fool deep learning modulators more effectively.
problem Improving evasion attacks on deep learning-based modulation classifiers.
method Utilizing multiple antennas to enhance adversarial attacks on deep learning classifiers.
result Adversarial attacks with multiple antennas significantly improve classifier accuracy.
Improved indoor localization using multiple fingerprints from multiple antennas.
problem Susceptibility of single fingerprint approaches to changing environments and multi-path propagation.
method Fused Group of Fingerprints (FAGOT) via multiple antennas, parallel GOOF multiple classifiers, MUCUS fusion algorithm.
result Significantly improved prediction accuracy compared to single fingerprint approaches.
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.
Cognitive radar selects optimal antenna subarrays using deep learning.
problem Optimize radar antenna selection for cost and performance.
method Convolutional Neural Network (CNN) for multi-class classification.
result CNN provides 22% better classification performance and 72% more accurate DoA estimates.
Study improves efficiency of MIMO systems' sum rate estimation.
problem Maximizing sum rate in MIMO systems with PAPC constraints.
method Proposes two new low-complexity approaches: alternating optimization and machine learning.
result Demonstrates superior performance compared to existing methods.
Unified DNN-based precoder for MIMO networks with multiple objectives.
problem Optimizing data transmission, energy harvesting, and security in MIMO networks.
method Rotation-based precoding and DNN for multi-objective optimization.
result DNN-based precoder reduces computational complexity and achieves near-optimal performance.
Fused Group of Fingerprints improves indoor localization accuracy and reduces fingerprint building time.
problem Susceptibility to changing environment, multipath, and NLOS propagation in SIOF; time-consuming fingerprint building.
method Building a GOOF from multiple antenna transformations, training GOOF-RF classifiers, SWIM fusion algorithm.
result Significantly improved localization accuracy and reduced fingerprint building time.
Study segments French territories based on mobile call patterns.
problem Characterize inhabitant behavior in mobile telephony.
method Two-stage analysis: spatial clustering and temporal discretization.
result Identifies distinct areas with similar call patterns over time.
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.
Paper tackles BA in dual-band systems using ML.
problem Choosing the best frequency band for communication in dual-band systems.
method Formulated as binary classification problem, proposed supervised ML solutions.
result Analytical and Viterbi Algorithm-based solutions for directional BA.
Paper proposes a time-frequency analysis method for blind modulation classification in MIMO systems.
problem Blind modulation classification in MIMO systems with overlapping signals and unknown channel parameters.
method Time-frequency analysis using windowed short-time Fourier transform, conversion to RGB spectrogram images, convolutional neural network for classification, decision fusion.
result Proposed scheme achieves high classification accuracy at different SNRs, outperforming existing methods.
Paper proposes neural network for efficient MIMO channel estimation and pilot reduction.
problem High overhead from pilot transmission in wideband MIMO systems.
method Neural network architecture for frequency-aware pilot design and channel estimation, with pruning technique.
result Neural network outperforms linear minimum mean square error (LMMSE) estimation.
New MIMO constellation design for noncoherent communications reduces hardware complexity.
problem Designing efficient MIMO constellations for noncoherent communications over fading channels.
method Geodesic curves of the Grassmann manifold for structured constellation design.
result Achieves comparable error performance to unstructured designs with reduced hardware complexity.
Optimizes antenna settings in heterogeneous cellular networks using RL.
problem Complex interactions between cells cause optimization challenges.
method Two-step approach: offline multi-agent mean field RL, online single-agent deep RL.
result Approaches multi-agent RL performance with fewer trials.
New method recovers radar and communication signals from overlaid data.
problem Recover radar and communication signals from overlaid data with unknown parameters.
method Propose minimizing the sum of multivariate atomic norms (SoMAN) for multi-antenna receiver.
result Minimum number of samples and antennas required for perfect recovery is logarithmically dependent on the maximum of radar targets and communications paths.
New ML-based detection improves PMH signal detection in load-modulated MIMO systems.
problem Detecting PMH signals without prior CSI is challenging and computationally expensive.
method Proposes HEM-ML and HEM-KD schemes using EM and KD-tree for efficient detection.
result Achieves comparable detection results to optimal ML detector with reduced complexity.
Deep neural networks improve MIMO detection performance.
problem Improving detection accuracy in massive MIMO systems.
method Introduced a neural network architecture based on BP algorithms, optimized with deep learning techniques.
result DNN MIMO detectors achieve lower bit error rates compared to other detectors.
Optimizes antenna tilt for better QoS in cellular networks.
problem Hard to learn optimal antenna tilt policies in real networks due to risk and simulation gap.
method Uses off-policy Contextual Multi-Armed-Bandit (CMAB) techniques to learn from existing data.
result Trained policies show consistent improvements over existing logging policies.
The paper addresses frequency-dependent distortions in massive MIMO systems and proposes a method to recover covariance matrices.
problem Frequency-dependent distortions in the covariance matrix of massive MIMO systems.
method Proposes a novel UL-DL covariance interpolation technique under a mild reciprocity condition.
result The proposed method can recover the covariance matrix in the DL from an estimate in the UL, especially in FDD massive MIMO systems.
CC charts radio geometry for user localization.
problem Locating users in radio environments.
method Unsupervised learning from passive CSI.
result Extracts channel features for spatial comparison.
Dynamic cell-free networks reduce complexity in serving many devices with distributed APs and DRL.
problem Designing efficient cell-free networks with many devices and APs.
method Dynamic architecture, SIC, DAS, DRL for optimization.
result DRL significantly improves performance in dynamic cell-free networks.
Unified geometric formulation of Maxwell-Vlasov system using presymplectic and symmetry reduction.
problem Unified geometric formulation of Maxwell-Vlasov system.
method Skinner-Rusk formalism, presymplectic geometry, reduction by diffeomorphism group, affine Hamiltonian controls.
result Unified geometric structure unifying Lagrangian, Hamiltonian, gauge, reduction, and control-theoretic aspects.
Deep neural networks improve angle of arrival estimation with lower complexity.
problem Estimating the number of sources and their angles of arrival from a single antenna array observation.
method Apply a deep neural network (DNN) approach to the problem.
result Deep neural networks can attain maximum likelihood performance with feasible complexity and outperform other methods.
Deep learning reduces training overhead in massive MIMO systems.
problem Reducing training overhead in massive MIMO systems.
method Use of deep learning (NNs) to improve CSI acquisition and feedback processes.
result Significant improvements in performance and reduced complexity.
A neural network improves DOA estimation from a single snapshot.
problem Estimating DOAs from a single snapshot with limited aperture.
method Deep learning architecture trained to generate high-resolution spatial spectrum.
result Our (SP)2-Net outperforms classical methods. Random orthogonalization improves FL in massive MIMO systems without CSI.
problem Efficient model aggregation in FL with minimal channel estimation overhead.
method Combining FL with massive MIMO's channel hardening and favorable propagation, random orthogonalization reduces channel estimation overhead.
result Achieves model aggregation without CSI, significantly reducing channel estimation overhead.
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.
DeepCMC compresses CSI for massive MIMO systems, reducing overhead and improving performance.
problem High CSI overhead in massive MIMO systems limits spectral efficiency.
method Deep learning-based fully convolutional neural network with residual layers and entropy coding.
result DeepCMC outperforms state-of-the-art schemes in CSI reconstruction quality for the same compression rate.
We study a parabolic equation for finding solutions to the optimal transport problem on compact Riemannian manifolds with general cost functions. We show that if the cost satisfies the strong MTW condition and the stay-away singularity property, then the solution to the parabolic flow with any appropriate initial condi…
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 learning reduces complexity for MIMO DF relay channel detection.
problem Efficient signal detection in MIMO DF relay channels with varying channels.
method Deep learning-based detection networks (NMLDNs) for signal detection in changing channels.
result Deep learning reduces detection complexity without sacrificing performance.
Semi-supervised learning identifies radio signals from sparse data.
problem Lack of labeled data for radio emitter recognition.
method Combines unsupervised and supervised learning for feature learning and clustering.
result Semi-supervised learning can identify new radio signals efficiently.
The fundamental germ is a generalization of π1, first defined for laminations which arise through group actions in math.DG/0506270. In this paper, the fundamental germ is extended to any lamination having a dense leaf admitting a smooth structure. In addition, an amplification of the fundamental germ called the mo…
Machine learning detects and characterizes whistler radio waves for real-time monitoring of the plasmasphere.
problem Detect and characterise whistler radio waves generated by lightning strokes for real-time monitoring of the plasmasphere.
method Developed a machine learning model using image classification and localisation on spectrogram data to identify and localise whistlers.
result The proposed detectors achieve a misdetection and false alarm rate of less than 15% on Marion's dataset.
This paper tackles mmWave beamforming optimization with active learning.
problem Adaptive and sequential optimization of beamforming vectors during mmWave initial access.
method Hierarchical beamforming codebook, noisy search strategies, and active learning from imperfect labeler.
result Upper bound on search time matches noiseless bisection search, with AoA error probability decaying exponentially.
Paper develops efficient algorithms to estimate channel subspace from low-dimensional projections.
problem Estimating channel subspace information from limited projections in JSDM.
method Develops novel algorithms requiring sampling only O(2√M) elements for p-dim beamformer.
result Estimators return a p-dim beamformer with performance comparable to full knowledge.
This paper continues the study of a class of compact convex hypersurfaces in Euclidean space Rn+1, n≥1, which are boundaries of compact convex bodies obtained by taking the intersection of (solid) confocal paraboloids of revolution. Such hypersurfaces are called reflectors. In R3 reflectors arise naturall…
Generative models preserve semantic features during generation.
problem Generating data with semantic integrity and efficiency.
method Replaces discriminator with a calibrated classifier in GANs, focusing on semantic space.
result Models generate objects with strong guarantees on properties across various domains.
Paper presents a neural network for estimating wavefronts in direction of arrival scenarios.
problem Estimating the number of wavefronts in direction of arrival scenarios.
method Cross-entropy trained multilayer neural network for online adaptation of antenna array imperfections.
result The method outperforms classical model order selection schemes in accuracy, especially at low signal-to-noise-ratios.
This paper presents a ML-based receiver for SDR that outperforms conventional methods.
problem Complexity and performance issues in multiuser detection.
method Supervised learning for direct symbol detection without parameter estimation.
result The ML-based receiver achieves similar or better performance than SIC and MMSE receivers.
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.
This paper describes caustics of wave fronts reflected by a surface.
problem Understanding the geometry of caustics formed by wave fronts reflected by a surface.
method Purely geometric description of caustics, clarifying their dependence on surface characteristics.
result Clarifies the geometry and topology of caustics formed by wave fronts after reflection from a mirror surface.
Paper improves channel charting using autoencoders with spatial constraints.
problem Improving logical positioning of UEs using channel-state information.
method Representation-constrained autoencoders to enhance channel charts.
result Improved quality of learned channel charts for UE positioning.
New NMF method identifies unknown number of delayed signals sources.
problem Identifying unknown number of sources emitting delayed signals.
method Nonnegative Matrix Factorization (NMF) for linear mixtures with delays.
result Identifies unknown number of sources, delays, and signal strengths.
Paper improves DOA estimation in sparse arrays using Siamese neural networks.
problem Challenges in DOA estimation with limited snapshots in sparse linear arrays.
method Introduces a Siamese neural network with a sparse augmentation layer for enhanced signal feature embedding.
result Demonstrates improved DOA estimation accuracy in sparse arrays.
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.
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.
Global geometric expressions derived for manifold embeddings.
problem Expressing geometric quantities globally on manifolds.
method Global formulas using operator-valued expressions and affine projection.
result Explicit cross-curvature results for specific metrics.