Millimeter-Wave Propagation and Modeling

Latest papers

• Radiomap Blind Prediction under Incomplete Observation: Error Characterization and Correctable Propagation-Prior Learning

  Xiaojie Li, Yu Han, Han Fang, Shangqing Liu, Guangxu Zhu, Shi Jin, Chao-Kai Wen · 29 September 2026

  Radiomap blind prediction aims to infer radiomaps from observable representations of the propagation environment and base station configuration without field measurements. In practice, the observable representations are inherently incomplete. Thus, the target radiomap is not fully determined by the …

• TRACE: Learning to Self-Calibrate Wireless Digital Twins from ISAC Measurements

  Saad Masrur, Saeed R. Khosravirad, Ismail Guvenc · 29 September 2026

  Wireless digital twins (DTs) rely on 3D environment models to predict radio propagation and support wireless-network decisions, yet these models are often initialized from imperfect 3D maps. Errors in building position, height, footprint, and orientation can therefore cause a high-fidelity propagati…

• WiNeRF: Measurement Constrained Radiance Fields for Actionable Wireless Channel Modeling

  Saif Ur Rahman, Rafid Umayer Murshed, Anton Dmitriev, Cagri Tanriover, Rahul C. Shah, Elah\'e Soltanaghai · 22 September 2026

  Wireless embedded systems increasingly rely on wireless channel information for decision making, yet practical platforms operate under severe constraints, including few antennas, narrow bandwidth, and sparse, noisy measurements. While neural field based approaches inspired by Neural Radiance Fields …

• Rethinking Radiomap Blind Prediction with Limited Environment and Configuration Representations

  Xiaojie Li, Yu Han, Han Fang, Shangqing Liu, Shi Jin, Chao-Kai Wen · 11 September 2026

  Radiomap blind prediction infers radiomaps from observable representations of the propagation environment and base station (BS) configuration without field measurements. These representations are inherently incomplete and cannot uniquely determine the target radiomap. Under squared loss, we identify…

• Robust Beam Prediction for V2X Networks with Multi-Modal Sensing

  Chen Shang, Dinh Thai Hoang, Diep N. Nguyen, Jiadong Yu · 10 September 2026

  Integrated sensing and communication (ISAC) provides a promising foundation for beam prediction in future vehicle-to-everything (V2X) networks. However, existing sensing-assisted beamforming methods still rely heavily on radio-frequency sensing, which may become unreliable in complex vehicular envir…

• BeamRMX: Radiation-Pattern-Driven Learning for Generalizable Beam Radio Map Prediction and Beam Management

  Yue Zhang, Xiucheng Wang, Wenshuo Chen, Nan Cheng · 2 September 2026

  The evolution toward sixth-generation (6G) wireless networks is driving larger antenna arrays and highly directional multi-beam transmission, making accurate knowledge of beam-dependent spatial coverage important for beam management and environment-aware network operation. Radio maps (RMs) provide s…

• GCNO: Gramian Chebyshev Neural Operator for Physics-Based Compression of Wireless Channels

  Rafid Umayer Murshed, Shahab Hamidi-Rad, Elahe Soltanaghai, Akshay Malhotra · 20 August 2026

  Large antenna arrays allow wireless systems to serve more users and achieve higher data rates, but they also make channel feedback expensive: the receiving device must repeatedly report a large complex-valued channel matrix to the base station. Most neural compressors treat this matrix like an image…

• Physics-Unrolled Neural Operator for Wireless Field Modeling

  Rafid Umayer Murshed, Saif Ur Rahman, Mingyue Tang, Elahe Soltanaghai · 20 August 2026

  Radio maps are essential for wireless decision-making tasks such as access-point placement, coverage planning, and localization, but their fine spatial details are governed by complex propagation effects and are costly to simulate accurately. Machine learning offers a path to high-fidelity radio-map…

• Intelligent Base Station Deployment in Urban Wireless Networks: A Geographic Data-Informed Digital Twin Approach

  Zhenyu Tao, Yuxuan Li, Wei Xu, Yongming Huang, Xiaohu You · 18 August 2026

  The placement of base station (BS) is a fundamental determinant of coverage and capacity of urban wireless networks. Yet large-scale BS deployment optimization remains challenging due to its dependency on site-specific radio propagation and user spatial distributions, both of which are unfortunately…

• Physics-informed VAE-EVT for Tail Aware Radio Map Prediction

  Amanda Sheron Gamage, Niloofar Mehrnia, James Gross · 18 August 2026

  Ultra-reliable low-latency communication (URLLC) requires precise identification of spatial regions where the signal-to-noise ratio (SNR) falls below an outage threshold. In this context, an outage refers to instances in which SNR falls below a specified threshold, which, for URLLC, can be as string…

• CFM-Bench: A Unified Multi-Domain, Multi-Task Benchmark for Channel Foundation Models

  Yuan Gao, Wenjun Yu, Jun Jiang, Yunfan Li, Xinyu Guo, Shugong Xu · 17 August 2026

  Channel foundation models (CFMs) are commonly evaluated in model-specific pipelines that differ in data, radio configurations, partitions, adaptation procedures, task definitions, and metrics, preventing reproducible comparison across CFMs and against task-specific networks. We release CFM-Bench, a …

• GSBF: Gaussian Splatting for Environment-Aware Beamforming

  Yijie Bian, Wei Guo, Zixin Wang, Shenghui Song, Jun Zhang, Khaled B. Letaief · 7 August 2026

  Beamforming plays a key role in multiple-input-multiple-output (MIMO) communication systems. However, conventional beamforming design normally requires accurate instantaneous channel state information (CSI) and iterative optimization, which incur substantial pilot overhead and computational complexi…

• MultiPathFormer: Towards a Foundation Model for Multipath Wireless Propagation

  Blessed Guda, Kayley Sze, Carlee Joe-Wong · 6 August 2026

  Recent advances in machine learning have enabled training of wireless foundation models, which aim to support tasks such as channel estimation, beam prediction, and localization based on wireless signals. Existing wireless foundation models typically pretrain on channel tensors using masked reconstr…

• Generative Models for Modeling and Synthesizing MIMO Channels in Adverse Weather Conditions

  Vignesh Nandakumar, Faraz Barati, Brian L. Evans · 4 August 2026

  The push for broader coverage in future cellular networks depends on reliable service, yet this is increasingly harder to do as we encounter more instances of extreme weather conditions. In extreme weather conditions, we have difficulty evaluating coverage due to limited access to channel measuremen…

• SymNet: A Multi-Task Network for Joint Radio Map Reconstruction and Transmitter Localization

  Lyuzhou Ye, Thanh Dat Le, Yan Huang · 4 August 2026

  Accurately predicting directional radio maps is essential for wireless applications, yet prior approaches primarily focus on omnidirectional signals and typically treat transmitter localization and signal map reconstruction as separate tasks. In omnidirectional settings, predicting the maximum signa…

• Point2Radio: A Foundation Model for Cross-Scene Radio Fields from Material-Aware Point Clouds

  Chaozheng Wen, Chenghong Bian, Hongze Chen, Jun Zhang · 3 August 2026

  High-fidelity radio fields are typically simulated for every scene--transmitter configuration or fitted separately to each scene, failing to exploit propagation structures shared across environments. We present Point2Radio, a foundation model that learns a transferable propagation prior from multipl…

• mmRadarTwin: A Measurement-Calibrated Signal-Level Digital Twin Platform for Indoor mmWave Radar

  Jianyi Zhou, Chenghao Zhang, Yanli Li, Dong Yuan · 31 July 2026

  Indoor mmWave radar perception is difficult to reproduce because measured range-angle responses depend on scene geometry, material response, multipath, hardware conventions, and signal processing. Existing ray-tracing and digital-twin tools often expose rendering, channel, or path-level quantities, …

• CORF-GS: Real-Time Wireless Radiance Field Reconstruction via Coupled Optical-RF Gaussian Splatting

  Jinya Zhang, Jiajia Guo, Chao-Kai Wen, Shi Jin · 29 July 2026

  Recent advances in 3D Gaussian Splatting (3DGS)-based wireless radiance field (WRF) reconstruction provide an efficient solution for wireless channel modeling. However, existing WRF reconstruction methods rely on pre-collected observations and offline optimization, and thus struggle to provide real-…

• RadioDiff-v2: Generative Angular Radio Maps for Multi-Beam Selection and Localization

  Xiucheng Wang, Junxi Huang, Nan Cheng · 10 July 2026

  Angular radio maps describe the received-power distribution over the angle of arrival and underpin beam selection and receiver localization in sixth-generation (6G) networks. Predicting the angular power spectrum (APS) from geometry is difficult, because the mapping is ill-posed in non-line-of-sight…

• Learning Coverage- and Power-Optimal Transmitter Placement from City Maps: A Comparative Study of Direct and Indirect Neural Approaches

  \c{C}a\u{g}kan Yapar · 7 July 2026

  Optimal wireless transmitter placement is a central task in radio-network planning, and exhaustive search becomes prohibitively expensive at scale. This paper studies the single-transmitter setting under a learned propagation model, enabling exhaustive per-pixel assessment at scale in a regime where…

• Scene-Conditioned PINN-GNN for Multipath RF Maps: Cross-Scene Generation and In-Scene Completion

  Lizhou Liu, Xiaohui Chen, Zihan Tang, Mengyao Ma, Wenyi Zhang · 3 July 2026

  Radio frequency (RF) maps provide a compact representation of multipath propagation characteristics and are fundamental to channel modeling, coverage analysis, and environment-aware wireless optimization. This paper proposes a unified RF map construction framework based on a physics-informed neural …

• Meta-Transfer Learning for mmWave Beam Alignment

  Ahmet Nuri Cevik, Sinem Coleri · 2 July 2026

  Millimeter-wave (mmWave) beam alignment plays a critical role in next-generation wireless systems, yet its efficient implementation remains challenging. Meta-learning and transfer learning have been explored to enable deep learning-based beam prediction models to rapidly adapt to unseen environments…

• GeNeRT: A Physics-Informed Approach to Intelligent Wireless Channel Modeling via Generalizable Neural Ray Tracing

  Kejia Bian, Meixia Tao, Shu Sun, Tongjia Zhang, Jun Yu · 30 June 2026

  Neural ray tracing (RT) has emerged as a promising paradigm for channel modeling by integrating physical propagation principles with neural networks. However, existing neural RT methods remain limited by strong spatial dependence and weak adherence to electromagnetic laws. We propose GeNeRT, a gener…

• Multi-Modal Conditioned High-Resolution Transformer for Urban Electromagnetic Field Map Prediction Download PDF

  Do-Eon Kim, Dongryul Park, Seungyoung Ahn, Namwoo Kang, Seong-heum Kim, Seongsin Kim · 29 June 2026

  Predicting electromagnetic field (EMF) strength in urban environments is essential for cellular network planning but computationally expensive with physics-based simulators. We propose a multi-conditioned dense prediction framework that generates 500 500 EMF maps from building layout images and ante…

• On the Limitations of Ray-Tracing for Learning-Based RF Tasks in Urban Environments

  Armen Manukyan, Hrant Khachatrian, Edvard Ghukasyan, Theofanis P. Raptis · 19 June 2026

  We study the realism of Sionna v1.0.2 ray-tracing for outdoor cellular links in central Rome. We use a real measurement set of 1,664 user-equipments (UEs) and six nominal base-station (BS) sites. Using these fixed positions we systematically vary the main simulation parameters, including path depth,…