Future 6G wireless networks are expected to evolve toward AI-native systems that support intelligent, environment-aware services, including positioning and mapping. Current research has already integrated AI into several aspects of communication, particularly wireless channel estimation. However, most learning-based channel estimators focus on communication-oriented objectives rather than explicitly estimating multipath parameters such as delay, angle of arrival (AoA), and angle of departure (AoD). These parameters provide essential geometric information for downstream tasks including positioning and radio simultaneous localization and mapping (SLAM). In this work, we propose a Spectral-Convolutional Factorized Transformer (SCFT) for joint multipath component (MPC) estimation from noisy complex channel estimates and physical beam-pair descriptors. SCFT combines local spectral convolution, permutation-equivariant beam-set attention, and a query-based Transformer decoder to detect and classify line-of-sight (LoS) and non-line-of-sight (NLoS) paths and estimate their observed delays, AoAs, and AoDs. Experiments on realistic ray-tracing data show that SCFT reduces the generalized optimal sub-pattern assignment (GOSPA) error by 13.4% compared with ResNet, the strongest competing baseline. We further demonstrate this advantage in independent snapshot positioning and radio SLAM, where SCFT reduces position RMSE by 18.5% and 65.0%, respectively, relative to ResNet. These findings highlight the importance of explicitly recovering multipath geometry, as improvements in MPC estimation can translate into substantial gains in downstream tasks.
Figures & tables
Fig. 1: Overview of the proposed Spectral-Convolutional Factorized Transformer (SCFT) framework for joint multipath component estimation.
Item
Value
Carrier; subcarrier spacing
28 GHz; 120 kHz
PRS subcarriers; selected pairs
Nsc=768 ; S=32
BS/RX arrays; codebooks
8×8 / 4×4 ; Kronecker DFT
Train/val./cal./test samples
35000/5000/5000/5000
SNR; MPC queries
0 – 30 dB; M=5
SCFT encoder/decoder
Nb=4 , R=2 , d=128 , 8 heads
TABLE I: Experimental configuration.
Method
Params (M)
GFLOPs
GOSPA μ±σ
F1
Delay RMSE (ns)
AoA MAE ( ∘ )
AoD MAE ( ∘ )
SCFT
1.490
3.621
2.331±2.730
0.903
21.37
5.54
2.31
Flat Transformer
1.491
4.081
3.276±3.073
0.853
24.91
7.93
3.13
ResNet
1.520
3.614
2.692±2.838
0.887
20.41
6.29
3.02
Vanilla CNN
1.520
3.612
6.770±2.552
0.646
38.73
20.71
4.88
Spectral U-Net
1.563
3.583
6.343±2.632
0.688
37.04
19.57
4.41
SAGE
–
–
7.827±1.746
0.302
62.03
7.98
7.76
TABLE II: Test-set MPC estimation performance. Geometric errors are evaluated over matched paths; computational complexity is reported per sample.
Fig. 2: MPC estimation performance versus SNR: (a) delay RMSE, (b) AoA MAE, (c) AoD MAE, and (d) GOSPA. Solid and dashed curves denote LoS and NLoS paths, respectively, in (a)–(c).
Variant
GFLOPs
GOSPA
F1
SCFT (spectral convolution)
3.621
2.331
0.903
Full spectral attention
3.777
2.784
0.885
No cross-pair attention
3.681
2.790
0.884
Neutral beam descriptors
3.777
7.966
0.467
TABLE III: Controlled architectural ablations.
Metric
SCFT
ResNet
SAGE
Oracle
Snapshot RMSE
2.55±0.16
3.13±0.14
35.30±1.11
0.78±0.41
SLAM RMSE
1.78±0.37
5.09±7.93
25.79±85.03
0.83±0.32
RPE
0.47±0.05
0.94±0.87
21.86±119.15
0.18±0.03
Map GOSPA
4.94±1.12
3.997±1.08
11.90±2.91
1.26±1.33
Association F1
0.879±0.024
0.813±0.093
0.081±0.038
0.977±0.013
TABLE IV: Downstream results over 50 paired noisy channel realizations ( μ±σ ). RMSE, RPE, and map GOSPA are in metres.
Fig. 3: Positioning and radio SLAM results: (a) snapshot positioning error, (b) radio SLAM position error, and (c) representative trajectories and virtual-anchor maps. Panels (a) and (b) show mean empirical CDFs with pointwise standard-deviation bands across 50 noisy realizations. Panel (c) shows a common realization selected based on proximity to the method-wise median SLAM RMSE and map GOSPA.