Wireless Signal Modulation Classification

Latest papers

• Graph neural networks for sampling-invariant embeddings of organized signal sets

  Martin Bauw (CMM), Santiago Velasco-Forero (CMM), Jesus Angulo (CMA) · 30 September 2026

  Sensor networks and radars can deliver signals as organized sets, e.g. ordered signals, signals describing range cells within a grid or signals perceived as graph nodes. Within such sets, individual signals may be characterized by distinct sampling parameters. This paper investigates organized signa…

• FRESHLATENT: Channel-Aware Latent Adaptation for Resource-Constrained Embodied VLM Perception

  Rajat Bhattacharjya, Minwoo Kim, Arnab Sarkar, Tamoghno Das, Sing-Yao Wu, Eli Bozorgzadeh, Marco Levorato, Nikil Dutt · 28 September 2026

  Mission-critical UAVs increasingly rely on split vision-language model (VLM) perception under tight onboard-resource and wireless-communication constraints. However, corruption of transmitted intermediate features creates a deployment mismatch for clean-trained split interfaces, while stronger chann…

• Adaptive Pilot Selection for Unified Semantic Communication and Semantic Sensing in ISAC

  Muhammad Abubakar Rashid, Muhammad Hannan Akram, Haejoon Jung, Syed Ali Hassan · 28 September 2026

  Semantic communication (SemCom) and integrated sensing and communication (ISAC) are promising technologies for future 6G wireless networks. Existing studies have applied semantic technology to either the communication module or the sensing module of ISAC. In this work, we propose SemISAC, which perf…

• Diffusion-aided Task-oriented Semantic Communications with Model Inversion Attack

  Xuesong Wang, Mo Li, Xingyan Shi, Zhaoqian Liu, Shenghao Yang · 25 September 2026

  Semantic communication enhances transmission efficiency by conveying semantic information rather than raw input symbol sequences. Task-oriented semantic communication further aims to retain only task-specific information, thereby achieving greater bandwidth savings. However, these neural-network-bas…

• Fast Frame Rate Estimation in Electromagnetic Side-Channel Attacks on Public Systems

  Alyson Isaluski, Leonardo Teodoro, Kleber V. Cardoso, Antonio Oliveira-Jr, Saulo Queiroz · 25 September 2026

  Frame refresh rate estimation is a fundamental step in identifying compromising harmonic frequencies in electromagnetic side-channel attacks. Methods based on discrete linear autocorrelation (DLA) are robust across different scenarios and require a computational complexity of $O(N\log N)$ for a sign…

• Unlocking Cross-Scenario Physical Layer Security: A Mixture-of-Experts Framework with Generative Diffusion Models

  Xiao Tang, Tong Hui, Chao Shen, Yichen Wang, Qinghe Du, Li Sun, Zhu Han · 23 September 2026

  The future 6G networks are expected to incorporate a proliferation of wireless services in diverse environments, which presents a significant challenge for information security. Conventionally optimization always requires recalculation and learning strategy often suffers poor generalization, which a…

• HFEMCNet: A Compact Hybrid Frequency Enriched Multi Channel Network for Automatic Modulation Classification

  Qamar Ijaz, Nayyer Aafaq · 22 September 2026

  Automatic modulation classification (AMC) of received radio signals is prudent for further signal processing tasks such as communication monitoring, cognitive radio operation, and interference mitigation in the electromagnetic spectrum. Traditional methods often rely on handcrafted features and stru…

• Experimental Evaluation of a Low-Power Ultra-Wideband Receiver for Spectrum Sensing

  Panagiotis Vlachos, Ioannis A. Bartsiokas, Cedric Dehos, Francois Rivet, George Karachalios · 22 September 2026

  Walsh-sequence-based receiver architectures offer an alternative approach for the reception and reconstruction of multiple simultaneous RF signals over wide bandwidths. While previous works have focused on the architecture and theoretical operation of Walsh-domain processing, limited experimental re…

• Calibrated RF-Fingerprinting Under Interference With Heterogeneous Transmission Protocols

  Tariq Abdul-Quddoos, Xiangfang Li, Lijun Qian · 18 September 2026

  Radio Frequency(RF)-Fingerprinting is a spectrum monitoring technique that identifies specific transmitters based on hardware impairments imprinted within the emitted signal. Although widely researched, studies almost exclusively consider scenarios where only one transmitter is emitting at a time, l…

• Radio-Frequency Convolutional Neural Networks

  Zhihui Gao, Shi-Yuan Ma, Yiran Chen, Dirk Englund, Tingjun Chen · 18 September 2026

  Running artificial intelligence (AI) models directly on edge devices such as smartphones, wearables, and drones offers low latency, pervasive scalability, and data privacy, but these devices rarely carry the computing capability that modern neural networks demand. Edge accelerators have been develop…

• Task-Oriented Semantic Feature Transmission for Multi-Task Satellite Remote Sensing over Low-SNR Channels

  Shuoyuan Sun, Hongyu Wang, Mugen Peng, Wenjia Xu · 18 September 2026

  Conventional satellite remote sensing transmission follows a reconstruct-then-infer paradigm that optimizes pixel-level fidelity, creating an objective mismatch with downstream tasks such as classification and detection, especially at low SNR. This paper investigates a task-oriented framework that b…

• Semantic CSI Feedback for Beam Selection: When Task-Aware Embeddings from Sparse Pilots Outperform Full-Bandwidth Reconstruction

  Cristian J. Vaca-Rubio, Konstantinos Vandikas, Aneta Vulgarakis Feljan · 17 September 2026

  Classical CSI feedback in FDD massive MIMO transmits a compressed reconstruction of the channel, optimizing fidelity to the original signal regardless of the downstream task. We propose a semantic communication perspective: instead of reconstructing the channel, the UE transmits a learned \emph{sema…

• Channel-Informed Neural Network for Physical Layer Key Generation

  Jose Angel Sanchez Viloria, George Sklivanitis, Dimitris Pados, Elizabeth Serena Bentley · 16 September 2026

  Physical-layer key generation (PKG) enables wireless devices to establish shared keys from reciprocal channel observations without directly exchanging the key. This capability is attractive for edge networks, where distributed and resource-constrained devices may require lightweight key establishmen…

• CRFCAN: A Complex-Valued Cross-Domain Residual Network for Joint Channel and Phase Noise Estimation in Sub-THz OFDM Systems

  Ruilin Wang, Xiaodai Dong · 14 September 2026

  In sub-terahertz (sub-THz) communications, the coupling of ultra-wide bandwidth and severe phase noise (PN) impairments renders conventional joint channel and PN estimation highly complex and computationally prohibitive. To address this, we propose CRFCAN, a complex-valued residual FFT convolutional…

• Scalable Discrete-to-Continuous Channel Simulation for Compression and Privacy

  Joseph Rowan, Buu Phan, Ashish J. Khisti · 14 September 2026

  Channel simulation has recently emerged as a useful component in machine learning systems where samples from a prescribed probability distribution are to be compressed. Yet, general channel simulation algorithms often suffer from high computational costs, random stopping times or, in the worst case,…

• Adaptive Distributed Physical-Layer Authentication and Attack Detection in 6G Non-Terrestrial Networks via Causal Meta-Learning

  Parsa Rajabi, Mohammad Reza Abedi, Nader Mokari, Paeiz Azmi, Halim Yanikomeroglu · 10 September 2026

  Physical-layer authentication (PLA) in non-terrestrial networks (NTNs) is challenged by severe Doppler shifts, long delays, and fast channel variations, which cause distribution shifts and degrade conventional learning methods. Existing PLA schemes often rely on single features or generalize poorly …

• Foundation Models for Generalizable Semantic and Goal-Oriented Communication

  Boliang Liu, Wint Yi Poe, Riccardo Trivisonno, Giuseppe Caire · 9 September 2026

  Semantic and goal-oriented communication is increasingly studied for 6G, but generalization beyond seen data remains a key weakness under tight rate budgets. Many existing systems overfit their training data and degrade sharply at very low bit rates because they attempt to compress the entire signal…

• Frequency Estimation Based on SNR-adaptive Frequency Estimator Under Wide SNR Range

  Hee-Yang Jung, Dong-Hee Paek, Woo-Jin Jung, Seung-Hyun Kong · 9 September 2026

  Frequency estimation is the problem of estimating individual tone frequencies from noisy multi-tone sinusoidal signals. Existing frequency estimation methods have difficulty accurately estimating both the number of tone frequencies and the individual tone frequencies in low signal-to-noise ratio (SN…

• IIns-VAE+: A Robust Transfer Learning Framework for Environmental Identification in Wireless Sensing

  Yuxiao Li, Keke Hu, Bobai Zhao, Santiago Mazuelas, Yuan Shen · 9 September 2026

  Environmental identification in wireless sensing is essential for 6G integrated sensing and communication (ISAC) systems to achieve reliable situational awareness. However, deep learning (DL) models for this task often fail to generalize under domain shift across diverse environments. While the Inte…

• Wireless Foundation Models: State-of-the-Art and Open Challenges

  Alonso M. Pacheco Huachaca, Juan J. Rodriguez Rodriguez, Ahmed Aboulfotouh, Nelson L. S. da Fonseca, Carlos A. Astudillo, Hatem Abou-Zeid · 7 September 2026

  Wireless foundation models (WFMs) have emerged as a promising approach for learning reusable representations from large-scale wireless data and adapting them to downstream tasks. However, the rapidly growing literature remains fragmented across modalities, pretraining objectives, architectures, adap…

• Cooperative Multi-Task Semantic Communication for Joint Classification and Regression Tasks

  Ahmad Halimi Razlighi, Mohammad Siddiqur Rahman, Maximilian H. V. Tillmann, Edgar Beck, Armin Dekorsy · 4 September 2026

  Multi-Task semantic communication (SemCom) prioritizes simultaneous execution of multiple tasks over bit-accurate reconstruction in future intelligent networks. In our prior work [1], we introduced the cooperative multi-task SemCom (CMT-SemCom) framework, in which the semantic encoder is divided int…

• Ada-TokenCom: Rate-Adaptive Token Communications via Large-Model-Driven Token Compression and Generation

  Zijun Zhang, Li Qiao, Mahdi Boloursaz Mashhadi, Zhen Gao, Mehdi Bennis, Kaibin Huang · 31 August 2026

  Token Communications (TokenCom) has recently emerged as a new paradigm in which tokens serve as unified units for communication and computation, enabling efficient multimodal semantic and goal-oriented transmission. In this paper, we develop Ada-TokenCom, a rate-adaptive TokenCom framework based on …

• GAN-Based Semantic Communication for Image Transmission in IoV

  Ruixing Ren, Shan Chen, Junhui Zhao, Xiaoke Sun · 31 August 2026

  For cooperative perception in the internet of vehicles, this paper proposes a generative adversarial network-based semantic communication framework to address the efficiency and fidelity bottlenecks of traditional communication systems in visual data transmission under limited bandwidth and dynamic …

• Clearing the Underbrush: AI-Enhanced RF Interference Suppression

  Rahul Jain, Pierre Trepagnier, Rick Gentile, Joey Botero, Alexia Schulz · 27 August 2026

  AI-based structured interference rejection has grown more popular because deep learning approaches can outperform traditional methods by jointly considering the signal of interest (SOI) and the signal mixture (SOI plus interference). This work builds on a previous AI-enabled approach utilizing autor…

• Token-Oriented Semantic Communication with Pretrained Vision Transformers

  Jiwoong Im, Minwoo Kim, Jaeho Lee, Yo-Seb Jeon, Yongjune Kim · 27 August 2026

  Token communications realize the semantic communication principle at the granularity of transformer tokens, providing a promising direction for client--server collaborative inference in resource-constrained edge systems. However, directly transmitting token embeddings presents two practical challeng…