惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Engineering at Meta
Engineering at Meta
D
Docker
IT之家
IT之家
博客园_首页
罗磊的独立博客
V
V2EX
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
美团技术团队
Y
Y Combinator Blog
博客园 - 聂微东
量子位
阮一峰的网络日志
阮一峰的网络日志
GbyAI
GbyAI
Microsoft Security Blog
Microsoft Security Blog
博客园 - Franky
Martin Fowler
Martin Fowler
Jina AI
Jina AI
大猫的无限游戏
大猫的无限游戏
C
Check Point Blog
月光博客
月光博客
G
Google Developers Blog
B
Blog
T
The Blog of Author Tim Ferriss
爱范儿
爱范儿

eess.SP updates on arXiv.org

ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Federated Learning with Hypergradient-based Online Update of Aggregation Weights Soft Graph Diffusion Transformer for MIMO Detection SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting Sequential Inference for Gaussian Processes: A Signal Processing Perspective Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation Adaptive Transform Coding for Semantic Compression EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures Sparse Graph Learning from Sparse Data via Fiedler Number Maximization A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals EVT-Based Generative AI for Tail-Aware Channel Estimation Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring Speech Enhancement Based on Drifting Models Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids Explainable AI in Speaker Recognition -- Making Latent Representations Understandable Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
Affine Frequency Division Multiplexing (AFDM) for 6G: Pro...
Hyeon Seok Rou, Kuranage Roche Rayan Ranasinghe, Vincent Savaux, · 2025-07-29 · via eess.SP updates on arXiv.org

Affine frequency division multiplexing (AFDM) is an emerging waveform candidate for future sixth generation (6G) systems offering a range of promising features, such as enhanced robustness in heterogeneous and high-mobility environments, as well as inherent suitability for integrated sensing and communications (ISAC) applications. In addition, unlike other candidates such as orthogonal time-frequency space (OTFS) modulation, AFDM provides several unique advantages that strengthen its relevance to practical deployment and standardization in 6G. Notably, as a natural generalization of orthogonal frequency division multiplexing (OFDM), strong backward compatibility with existing conventional systems is guaranteed, while also offering novel possibilities in waveform design, for example to enable physical-layer security through its inherent chirp parametrization. In all, this article provides an overview of AFDM, emphasizing its suitability as a candidate waveform for 6G standardization. First, we provide a concise introduction to the fundamental properties and unique characteristics of AFDM, followed by highlights of its advantageous features, and finally a discussion of its potential and challenges in 6G standardization efforts and representative requirements.