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.
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.
For the last few years, the amount of data has significantly increased in the companies. It is the reason why data analysis methods have to evolve to meet new demands. In this article, we introduce a practical analysis of a large database from a telecommunication operator. The problem is to segment a territory and char…
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.
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.
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.
Most existing fingerprints-based indoor localization approaches are based on some single fingerprints, such as received signal strength (RSS), channel impulse response (CIR), and signal subspace. However, the localization accuracy obtained by the single fingerprint approach is rather susceptible to the changing environ…
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.
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.
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.
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.
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.
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.
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.
New adaptive filters reduce energy consumption in electronic devices.
problem Reducing energy consumption in adaptive filtering algorithms.
method Data-selective adaptive filters, set-membership (SM) filters, trinion and quaternion systems, partial-updating, LMS and recursive LMS algorithms.
result Improved adaptive filtering algorithms with reduced computational complexity and enhanced stability.
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.
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.
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…
Radio emitter recognition in dense multi-user environments is an important tool for optimizing spectrum utilization, identifying and minimizing interference, and enforcing spectrum policy. Radio data is readily available and easy to obtain from an antenna, but labeled and curated data is often scarce making supervised …
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.
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.
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.
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…
Massive MIMO is a variant of multiuser MIMO where the number of base-station antennas M is very large (typically 100), and generally much larger than the number of spatially multiplexed data streams (typically 10). Unfortunately, the front-end A/D conversion necessary to drive hundreds of antennas, with a signal band…
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.
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.
New learning-based methods improve spectral efficiency in mmWave full-duplex systems.
problem Residual self-interference and high pathloss in mmWave full-duplex systems.
method Proposed two learning schemes (ELM-HBF and CNN-HBF) using ADMM and MM algorithms for SI cancellation and joint HBF optimization.
result Learning-based schemes achieve at least 22.1% higher spectral efficiency and faster online prediction and training times.
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.
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.
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.
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.
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.
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.
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.
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.
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…
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.
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.
Method discovers user habits from mobile data.
problem Understanding human mobility patterns and habits.
method Density-based clustering for spatio-temporal data and Gaussian Mixture Model (GMM).
result Many unique habits were identified from the datasets.
A learning algorithm optimizes beamforming for holographic transceivers in far-field communication.
problem Optimal phase-shifts for beamforming in holographic transceivers are challenging due to unknown receiver locations and large phase-shifts.
method Developed a learning algorithm using a fixed-budget multi-armed bandit framework to learn optimal phase-shifts.
result The algorithm, HoloBeam, outperforms state-of-the-art methods in beamforming optimization.