Organizations: College of Computing and Data Science Nanyang Technological University Singapore · Beihang Sino-French Engineer School Beihang University Beijing, China · School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu, China · Electronic Information Engineering Beihang University Beijing, China
Near-field to far-field transformation is central to large-aperture antenna testing, yet two coupled challenges remain: costly phase acquisition at millimeter-wave bands and violations of the centering assumption under offset mounting. Existing methods address these issues separately, requiring either dense full-field data or offset vectors. We tackle both jointly by exploiting a key observation: amplitude fields under different offsets are coordinate-transformed views of the same near field. The challenge is to recover the center-aligned field from offset amplitudes without a phase or offset vector. We propose AntennaFlow, a three-stage framework: a contrastively learned encoder that maps offset views to an offset-invariant embedding, a deterministic flow-matching transport that maps offset amplitudes to center-aligned ones, and the Simplified Extrapolation Technique, whose Green-function Taylor expansion is valid only for centered fields. Experiments show that AntennaFlow enables fast, phaseless, offset-vector-free NF--FF reconstruction from sparse amplitude-only measurements, consistently outperforming existing baselines while preserving physical consistency.
Figures & tables
Fig. 1: Comparison of far-field reconstruction under offset mounting: prior methods suffer from offset sensitivity or reliance on offset vectors, while AntennaFlow achieves superior reconstruction quality.
Capabilities
Method
ET
SET
SWE
TSWE
SRM
Ours
Sparse Sampling
✗
✓
✗
✗
✗
✓
Fast Inference
✗
✓
✗
✗
✗
✓
Phaseless
✓
✓
✗
✗
✓
✓
Offset Correction
✗
✗
✗
✓
✓
✓
Offset-Vector Free
–
–
–
✗
✗
✓
TABLE I: Comparison of the previous methods and the proposed method
Fig. 2: The AntennaFlow pipeline for NF-FF transformation. (1) Data Collection: Near-field data are sampled from different antenna configurations. (2) A feature encoder is trained using a supervised contrastive learning framework to learn discriminative representations. (3) Flow Matching Transport: A U-Net-based architecture is used to learn the flow-matching velocity field for center near-field generation. (4) Far-Field Reconstruction via SET. Note that SET requires measurements from multiple distinct surfaces; for simplicity, only one surface is illustrated in the figure.
Fig. 3: Comparison of antenna far-field reconstruction using different phaseless reconstruction methods. The red solid curves denote the reconstructed far-field patterns, while the black dashed curves represent the theoretical far-field reference. The cyan curves indicate the corresponding ESS.
Fig. 4: Visual comparison of reconstructed far-field amplitude patterns under offset conditions. From left to right: SET, TSWE, SRM, AntennaFlow, and the ground truth. AntennaFlow demonstrates the highest fidelity in restoring detailed pattern textures compared to the ground truth.
Fig. 5: Reconstruction from offset near-field measurements. Different offset inputs (top) are mapped to consistent center-aligned fields (bottom).
Dataset
Metric
SET
TSWE
SRM
Ours
MATLAB
RMS ESS ( ↓ )
-18.97
-28.37
-34.44
-36.90
Peak ESS ( ↓ )
-6.91
-18.48
-24.32
-27.06
FEKO
RMS ESS ( ↓ )
-18.28
-25.25
-29.70
-33.81
Peak ESS ( ↓ )
-8.42
-15.34
-18.33
-25.58
-
Inference Time
4s
10 min
30 min
9s
TABLE II: Quantitative Comparison of Far-Field Reconstruction Accuracy (RMS ESS and Peak ESS in dB)
CL Use
NF RMS
NF Peak
FF RMS
FF Peak
w/o CL
-25.74
-19.18
-24.43
-16.46
w/ CL
-34.39
-26.40
-33.81
-25.58
TABLE III: Ablation Study on the Effect of Contrastive Pre-training
Fig. 6: t-SNE projection of the encoder embeddings of 768 amplitude samples drawn from 24 distinct antenna configurations under random offsets. Colors denote antenna identity. Samples of the same antenna fall into tight clusters, while clusters of distinct antennas are clearly separated.
Perturbation
NF RMS
NF Peak
FF RMS
FF Peak
Default
−35.39
−26.40
−33.81
−25.58
Horn Probe
−33.94
−25.89
−32.67
−24.50
Dipole Probe
−34.28
−24.48
−32.91
−24.81
Additive noise
−33.49
−25.18
−31.07
−23.67
TABLE IV: Sim-to-Real Robustness Evaluation.(RMS ESS AND PEAK ESS IN DB)
Deep unrolling methods for inverse problems augment traditional reconstruction algorithms with learned priors to improve their performance. These methods have been developed under the implicit assumption that the geometry of the measurement operator does not change between training and deployment; when this is no longer the case, the quality of obtained reconstructions can degrade significantly. In this paper, we argue that this is not an inherent limitation of the deep unrolling paradigm and can be mitigated by a careful choice of the underlying iterative algorithm. We propose a deep unrolling framework for the phase retrieval problem that augments the alternating projections method with a learned denoising step. Our method obtains high-quality reconstructions without the need for retraining in settings where conventional model-based alternatives struggle, such as when the dynamic range of the measurement operator changes significantly at test time.
Electrical & Computer Engineering, University of Washington · NSF-Simons National Institute for Theory and Mathematics in Biology · Department of Computer Science, University of Chicago +3
Full-wave electromagnetic (EM) simulation enables accurate patch-antenna analysis but is computationally expensive for large-scale forward prediction and inverse design. We present a mesh-native, physics-augmented graph-learning framework that treats radiation-pattern prediction as signal reconstruction on an irregular surface mesh. For the forward problem, a GPS graph transformer is trained with Physics-Augmented Intermediate Supervision (PAIS), an auxiliary node-level objective that predicts complex surface currents, the physical intermediate linking geometry to radiation. PAIS improves multiple GNN backbones at no inference-time cost, while shuffled-current and non-physical controls show the gain comes from physical correspondence. Direction-conditioned decoding and a differentiable radiation-integral consistency loss further exploit this structure. On an 80,000-sample CST benchmark, GPS+PAIS reaches MSE 0.17 / PSNR 19.67, generalizes to a PCA split, and transfers zero-shot to canonical patches. For inverse design, surrogate-filtered diffusion beats nearest-neighbor retrieval by 32% relative MSE.
This paper investigates over-the-air federated learning (AirFL) in wireless systems where the access point is equipped with a multi-waveguide pinching antenna system (PASS). We adopt the widely studied learning-oriented AirFL formulation, which seeks to maximize the number of selected devices while keeping the aggregation distortion below a prescribed threshold. The resulting joint optimization of device selection, receive beamforming, and pinching-antenna placement is highly nonconvex due to the intricate coupling among these system variables. To address this challenge, we develop AirPASS, an alternating optimization framework with two main components: a homotopy-Riemannian margin-consolidation method for device selection and receive beamforming under a fixed PASS configuration, and a homotopy-assisted geometry optimization method for updating the pinching-antenna positions under fixed selected devices and beamformer.
Seyed Mohammad Azimi-Abarghouyi, Christopher G. Brinton
Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden · School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN USA