Map as a Prompt: Learning Multi-Modal Spatial-Signal Foundation Models for Cross-scenario Wireless Localization
Authors: Yong Chu
Xun Zhou
Zenglin Xu
Hui Wang
Yue Yu
Venue: ICLR 2026 Poster (2026)
Keywords: Wireless Localization
Foundation Models
Self-Supervised Learning
Fine-Tuning
6G Networks
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: We propose SigMap, a foundation model that uses self-supervised learning with cycle-adaptive masking and map-conditioned prompting to achieve accurate and generalizable wireless localization across diverse scenarios.

Abstract: Accurate and robust wireless localization is a critical enabler for emerging 5G/6G applications, including autonomous driving, extended reality, and smart manufacturing. Despite its importance, achieving precise localization across diverse environments remains challenging due to the complex nature of wireless signals and their sensitivity to environmental changes. Existing data-driven approaches often suffer from limited generalization capability, requiring extensive labeled data and struggling to adapt to new scenarios. To address these limitations, we propose SigMap, a multimodal foundation model that introduces two key innovations: (1) A cycle-adaptive masking strategy that dynamically adjusts masking patterns based on channel periodicity characteristics to learn robust wireless representations; (2) A novel "map-as-prompt" framework that integrates 3D geographic information through lightweight soft prompts for effective cross-scenario adaptation. Extensive experiments demonstrate that our model achieves state-of-the-art performance across multiple localization tasks while exhibiting strong zero-shot generalization in unseen environments, significantly outperforming both supervised and self-supervised baselines by considerable margins.

Published as a conference paper at ICLR 2026
MAP AS A PROMPT: LEARNING MULTI-MODAL SPATIAL-SIGNAL FOUNDATION MODELS FOR CROSS-SCENARI WIRELESS LOCALIZATION
Yong Chu Xun Zhou Zenglin Xu Hui Wang", Yue Yu²
Harbin Institute of Technology, Shenzhen
Pengchhnng Laboratory
s "Shanghai Academy of AI for Science
Artificial Intelligence Innovation and Incubation Institute, Fudan University chuyong@stu. hit. edu. cn, zhouxun2023@hit.cem
ABSTRACT
Accurate and robust wireless localization is critical enabler for emerging 5G/6G applications, including autonomous driving, extended reality, and smart manu- facturing. Despite its importance, achieving precise localization across diverse environments remains challenging due to the complex nature of wireless signals and their sensitivity to environmental changes. Existing data-driven approaches often suffer from limited generalization capability, requiring extensive labeled data and struggling adapt to new scenarios. To address these limitations, we propose SigMap, multimodal foundation model that introduces two key inno- vations: (1) A yyclevadaptive masking strategy that dynamically adjusts masking patterns based channel periodicity characteristics to learn robust wireless repre- sentations; (2) A novel "map-as-promp"" framework that integrates 3D geographic information through lightweight soft prompts for effective cross- scenario adapta- tion. Extensive experiments demonstrate that our model achieves state-of-the-a performance across multiple localization tasks while exhibiting strong zero-shot generalization in unseen environments, significantly outperforming both super- vised and self-supervised baselines by considerable margins.
INTRODUCTION
Wireless localization has evolved from classical model-based methods to data-driven deep learning approaches, and more recently, to paradigms built upon foundation models and large language mod- els (LLMs). Despite these advances, existing techniques continue to face significant challenges in complex environments particullrryy under non-line-of-sight (NLoS) conditions and in rich multi- path scenarios due to limitations in representation learning and environmental reasoning.
Traditional localization systems rely on geometric or signal-strengee measurements such as time- of-arrival (ToA), ceiee-diffeeneeof-arre
(TDoA), angle-of-arrival (AoA), and received signal strength (RSS) (Chen et al., 2022a). Classical algorithms including MUSIC and OMP are widely adopted for parameter estimation (Keskin et al. (222)) However, such model-based methods assume idealized propagation conditions and perform poorly urban settings with substantial multipath and NLoS effects, often incurring errors over 100 meters (Chen et al. 2024). Although some works at- tempt to mitigate NLoS via filtering or hardware enhancements (Huang et al., 2023 Zhou et al,) 2019). they typically overlook richer environmental semantics from maps channel characteristics.
To address these issues, data-driven methods have been extensively explored. Supervised models such MLPs (Gao et al.) 2023), CNNs (Wu al. 2021), and LSTMs (Chen et al.] 2023) learn direct mappings from channel state information (CSI) to user positions. While effective in specific settings, they require large labeled datasets and exhibit limited cros--environment generalization (Pan et al. 2025). Subsequent semi-supervised and unsupervised approaches-use autoencoders,
"Corresponding author

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Published as a conference paper at ICLR 2026
GANs, and domain adaptation (Ruan et al. 2023 Chen al. 2022b Junoh & Pyun 2024 Li al. 2021] aim to reduce labeling costs, yet often fail to learn robust and transferable representations that capture high-level semantic features of the environment.
Recent efforts have turned toward self-supervised learning (SSL) and foundation models inspired by successes in NLP and vision. Methods such LWM (Alikhani et al. 2024) and WirelessGPT (Yang et al. 2025) employ masked channel modeling to learn general-purpont channel representa- tions, while contrastive learning frameworks (Salihu et al. 2024) extract invariant channel features. However, these models are not designed specifically for localization and often lack task-aware se- mantic understanding. Several SSL-based frameworks target localization more directly, including CrowdBERT (Han et al.) 2024) and signal-guided masked autoencoders (Wang et al. (025), which adopt masking strategies for reconstructing RSS or channel impulse responses (CIR). Despite their potential, such approaches are often confined to specific configurations and rely single SSL ob- jectives, limiting the diversity and generalizability of the learned features.
Concurrently, LLMs have been introduced to the wireless domain. For example, LirelessLLM Shao et al., 2024) incorporates domain knowledge via prompt engineering and retrieval-augmentee generation. Although effective for high-level protocol reasoning. LLMs struggle with low-level sig- nal processing and often produce hallucinations when applied to channel-bart inference, restricting their applicability to precise localization tasks.
1.1 RESEARCH GAPS
Current wireless localization methods face two fundamental limitations:
Inadequate Handling of Signal Periodicity: Existing self-supervised approaches employ generic masking strategies that ignore the inherent cyclic patterns in Channel State Infor- mation (CSI). This allows models to exploit local periodic shortcuts rather than learning meaningful global representations of signal propagation.
Superficial Geographic Integration: While some methods incorporate basic map data, they fail to capture the rich spatial-topological relationships in 3D environments. The fu- sion between geometric constraints and channel representations remains shallow and lacks interpretability.
1.2 CONTRIBUTIONS
This work addresses these gaps through three key contributions:
Cycle-Adaptive Masked Modeling: We introduce novel masking strategy that dynam- ically adapts to CSI periodicity by computing row-wise cross-correlation and generating shift-aware patterns. This disrupts periodic shortcuts and forces learning of globally mean- ingful signal representations.
We develop a geographic prompt mechanism that en- codes 3D map information via graph neural networks. These prompts enable interpretable fusion of environmental constraints during fine-tuning, enhancing accuracy in complex multipath scenarios.
Saap-Conditioned Prompt Tuning:
Cerameter-Efficien Generalization: Our foundation model achieves state-of-the-atr per- formance with limited labeled data and demonstrates strong zero-shot generalization to unseen environments and base station configurations.
PRELIMINARIES
This section introduces the concepts of wireless channel modeling and formally defines the lo- calization problem. The physical principles explained here are directly leveraged by our geographic prompt tuning method.
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2.1 WIRELESS CHANNEL MODELING FOR LOCALIZATION
The fundamental premise of our work is that Channel State Information (CSI) contains geometric relationships between transmitters and receivers. As shown in Figure | wireless signals propagate through Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) paths, creating unique spatial finger- prints in the CSI data.
150
Kity
Figure Wireless propagation paths in urban environments. LoS represents direct propagation, while NLoS paths result from reflections and diffractions. Bottom-right inset: corresponding 3D map visualization showing the same LoS/NLoS topology used for prompt generation.
For a MIMO-OFDM system with N₂ transmit antennas and N, receive antennas, the CSI matrix H[k] N.NN. at subcarrier can be expressed as the superposition of both LoS and NLoS components:
INuas
HK = CLSS faamaafe a, (oLoo) (Dios)*
are -axn a (0))a,,)))))
LoS component
NLe8 components
where LNLaS denotes the number of NLoS multipath components, ag and 7 represent the complex gain and delay of the 1-th path, f& is the frequency of the k-th subcarrier, and a, (67)) (6)) are the array steering vectors at receiver and transmitter, respectively.
The key insight for localization is that each path in equation equation carries geometrie infor- mation. Single-base station localization is possible because CSI contains time delay (related to distance) and angle information that can define a spatial vector. Multipath effects provide multiple such constraints, enabling rough positioning even without precise time measurements. Multi-base station setups provide richer information by offering diverse spatial perspectives.
2.2 RAY-TRACING AND MAP ALIGNMENT
We use ray-tracing to generate realistic training data that captures the mapping between physical geometry and wireless channels. The process can be abstracted as:
(PBS: PUE. Ray-tracing
HESI
(2)
where Pas is the base station position, PUE the user position, and M is the 3D environment map.
The map M serves two crucial purposes: 1) generating physically realistic training data, and 2) providing geometric constraints during inference to resolve multipath ambiguity. This alignment process helps decompose raw CSI into its constituent LoS and NLoS components, which is learned implicitly by our model through geographic prompt tuning.
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2.3 PROBLEM FORMULATION: WIRELESS LOCALIZATION
We define the wireless localization problem as estimating user equipment position from channel measurements and environmental context.
Inputs:
Channel State Information E CNN.NNXXN. from one or multiple base stations
3D environment map M containing building geometries
Base station positions PBS [PAs (1) ...........
.... PBS (2))
Output: Estimated user position Pus R.3.
Objective: Learn mapping function fe that minimizes:
f
PBS) Puell
(3)
The inclusion of map information M differentiates approach from conventional CSI-only meth- ods, enabling more accurate and physically consistent localization.
3
METHODOLOGY
3.1 OVERALL FRAMEWORK
Our proposed wireless localization foundation model addresses the fundamental challenge of achiev- ing accurate positioning across diverse environments with minimal labeled data requirements. The framework follows two-stage learning paradigm consisting of self-supervised pre-training un- labeled CSI data followed by prompt-based fine-tuning for specific localization tasks. This approach enables the model learn general- purppss representations of wireless signal propagation that can be efficiently adapted to new environments.
illustrated in Figure 2 the framework integrates three core components: (1) transformer-based backbone network that captures long-range dependencies in CSI data, (2) novel cycle-adaptive masked modeling strategy that prevents shortcut learning in periodic signals, and (3) geographic prompt tuning mechanism that incorporates environmental constraints during fine-tuning, The key innovation lies our cycle-aware masking approach that dynamically adapts to signal periodicity, combined with map-conditioned prompts that enable efficient adaptation with minimal parameter updates.
Wireless Large Model with Map-Signal Joint Modeling
Data
Pretrain and Finetune
Tasks
Pretrain
Origin Channel
Masking
Decodert: Strip-Mask
Backbone
Decoder2: Grid-Mask
Head
Finetune
3D Map with BSLoc
Decoderl: Single-Loc
GNN
Backbone
Decoder2: Multi- ti-Loc
Prompt Tokens
Head
Figure 2: Overall architecture of our wireless localization foundation model, showing the two-stage learning process with self-supervised pre-training and prompt-based fine-tuning.

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3.2 SIGNAL REPRESENTATION AND PREPROCESSING
Wireless Channel State Information (CSI) provides rich characterization of the propagation envi- ronment by capturing multipath effects, fading characteristics, and spatial diversity. multi-antenna OFDM systems, we represent the channel frequency response as a complex-valued tensor:
H. E NNNNN
(4)
where N,, N₁. and N, denote the number of receive antennas, transmit antennas, and subcarriers respectively. Each element [ represents the complex channel gain between specific antenna pairs at different subcarriers.
To facilitate deep learning processing while preserving critical phase information, transform the complex CSI data into real-valued representation through channel-wise separation:
X (RHH,, ((H) RRXN,
This representation maintains the spatial and frequency diversity essential for accurate localization while being compatible with standard neural network operations.
3.3 CYCLE-ADAPTIVE MASKED MODELING
Traditional masked autoencoding approaches often struggle with wireless signals due to their in- herent periodic patterns, which can exploited as learning shortcuts. Our cycle-adaptive mask- ing strategy addresses this limitation by dynamically generating mask patterns that disrupt periodic structures while preserving semantically meaningful information.
The core insight is detect dominant periodicities in the CSI data and generate masks that prevent simple interpolation- -aaedd reconstruction. For each input sample, we compute shift patterns using cross-correlation analysis and generate adaptive mask patterns:
Moysleli,1 0
j (Jo + ii drina)l S w
(6)
otherwise
where danal represents the detected periodicity shift, jo is the starting offset, and w controls the mask width. This approach ensures that the model must learn meaningful signal representations rather than relying on pattern repetition, as illustrated in Figure 3
Aduptive mask
So Moip-Mokeel CFR
Figure Illustration of our cycle-adaptive masking strategy. The mask pattern (right) is dynamically generated based on the detected periodicity in the CSI amplitude data (left), preventing the model from exploiting simplistic periodic shortcuts.
The reconstruction objective trains the model to recover the original signal from masked inputs:
LMAE Ex [||X Jon. masked) [|]]
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3.4 GEOGRAPHIC PROMPT TUNING
Following pre-training. we employ a sarameter-efficient fine-tuning strategy that leverages geo- graphic information from 3D environment models. The core innovation is the transformation of spatial relationships between buildings and base stations into set of learnable prompt tokens that guide the pre-trained model without updating its core parameters.
Algorithm Geographic Prompt Generation
1: procedure GENERATEGEOPROMPTC PBS)
2: h MLP ven W
Encode vertex positions
3: hgs MLPps PuS Pas: Wbs)
> Encode BS positions
4: Vinit (h: (0) } (h
5:
for = to do
2 Graph convolution layers
6:
for i do
7:
h
h (1-1) + Ejer(a) bwng--))
8:
end for end for
9:
10:
g Glooal (h (2) M
(i=1
Aggregate graph information b Project to prompt dimension
11: Sprompt -MLPpre projl (g; W
proj
return gprompt
13: end procedure
Delaunay triangulation
Global Mean Pool
Projection
MLP
GCN
GCN
Prompt tokens
coordinates
Building verlex
Base station
Graph edge
Figure 4: The pipeline of geographic prompt generation.
The process begins with the construction of heterogeneous graph (V,E) that encodes the spa- tial configuration of a given scene. The scene is defined by a 3D building mesh M, represented by set of vertices {v,) where each V; R and the positions of T base stations, denoted as PaS {p*)T- where each p¹ R3 The node of the graph the union of these building vertices and base station positions: V {v1...., vv U {p' ...., p" To capture the inherent proximity rela- tionships in 3D space, the edge set constructed using Delaunay triangulation over the node set V, formally defined as & (ii,j) nodes i and are connected in the Delaunay triangulation of V).
The GCN update rule for each layer formally defined as:
(++1) = AD- w()
where A + is the adjacency matrix with self-connections, D is the degree matrix of A, and W() are the trainable weights of layer 1.
The generated geographic prompt Spomps E RDD is integrated into the pre-trained Transformer's input sequence. The complete input sequence Tinput 1()+++))x is constructed by prepending the prompt to the existing sequence. It consists of the frozen classification token tak. the trainable geographic prompt token T geo
gpromp (for single prompt), and the frozen sequence of CSI

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measurement tokens Tesi- This combined sequence is then added to the frozen positional encoding Epos:
T imput [taki geo Tesil
+ E Eps
The self-attention mechanism then operates on this extended sequence. The Query (Q), Key (K), and Value (V) matrices are derived by projecting the input sequence with the frozen pre-trained weight matrices W W and WV:
Q T inpor W K Tinpur W
Tinpur W
OK?
The attention output is computed as Attention K, V) softmax
V.
vdl
The parameter efficiency of this approach a key advantage. The only parameters updated during fine-tuning are those of the GNN ggg,,), the projection MLP (ppro)) and the task-specific head (0ms).
The optimization process is formulated as:
San P...MMPs..)) Dunk (M,
min
where the complete forward pass is defined as M, PBS) = fana (So.. (T goo Tcsi))).
3.5 TASK-SPECIFIC ADAPTATION
We design specialized output heads to handle different localization scenarios. For single-base station localization, the user equipment position is directly predicted from the final [CLS] token using a simple MLP head:
PUE MLP single (tess W single)
For multi-base station scenarios, we employ an attention-based fusion mechanism that dynamically integrates information from all available base stations. The process begins by extracting the [CLS] tokens from all T base stations and stacking them into a tensor B E RTAD, We then compute attention weights og, for each base station using a learned attention function:
cxp(vT tanh W
0t
(9)
j=1 cxp(vT tanh( (W Wann (6
Each base station's [CLS] token is processed independently through dedicated MLP heads gen- erate preliminary position estimates PUE. The final position estimate is obtained through weighted fusion:
MLP(O) (1), w(O multi multi)
PUE
au
(10)
1=1
This attention mechanism allows the model to dynamically prioritize contributions from different base stations based on their signal quality and geometric configuration, with stations having stronger signals or more favorable geometric relationships receiving higher weights. The comprehensive framework demonstrates how sule-super pre-training combined with geographic-aware prompt tuning can achieve robust wireless localization across diverse environments while maintaining pa- rameter efficiency and practical deployability.
4 EXPERIMENTS
We conduct comprehensive experiments evaluate wireless localization foundation model across diverse scenarios. The experiments address four key questions: (1) How does our method compare to state-of-the-art approaches? (2) What is the impact of geographic information? (3) How effective is our cycle-adaptive masking? (4) How well does our method generalize to new environments?

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4.1 DATASETS AND EVALUATION METRICS
We evaluate our method on the DeepMIMO dataset (Alkhateeb) 2019), using the 01.3p5 urban scenario for both pre-training and fine-tuning. The dataset provides realistic CSI data generated through ray-tracing simulations. Detailed configuration parameters are provided in Appendix [B3
Evaluation metries include Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Cu- mulative Distribution Function at meter (CDF@ Im). All results are averaged over 5 independent runs.
4.2 MAIN RESULTS
We compare against OMP (compressed sensing), CNN-based, SWIT (Slihu et al. 2024), and LWLM (Pan al. 2025).
Single-BS localization under NLoS represents one of the most challenging scenarios. As shown in Table T SIGMAP with geographic information achieves an MAE of 1.564 m, RMSE 5.675 m, and CDF@ Im of 60.5%, outperforming the best baseline (LWLM) by 34.4% in MAE and more than doubling the CDF@ 1m.
The key advantage stems from our NLoS-aware attention mechanism that explicitly models multi- path propagation:
exp(o(os" WNLOS)
02
(11)
Ej exp(o(o!") Wxi.s))
which allows the model differentiate between direct and reflected paths, significantly reducing positioning ambiguity.
Table 1: Metrics of Single-BS localization.
Method
MAE (m)
RMSE (m)
CDF @ 1m (%)
SIGMAP (w/ map)
1.564
5.675
60.5
SIGMAP (w/o map)
2.275
8.532
31.0
LWLM
2.382
5.822
25.3
SWTT
2.586
8.967
24.3
CNN
2.943
9,423
21.7
OMP
3.287
9,851
15.4
Multi-BS collaboration leverages spatial diversity to overcome NLoS limitations. Table [iolee[stn shows
that SIGMAP with map achieves 0.673 MAE, 1.099 RMSE and 84.5% CDF@ 1m, improv- ing the second-best result (SIGMAP w/o map) by 14.7% MAE and 7.0 percentage-points in
CDF@ Im.As further visualized in Figure 5 SIGMAP dominates accuracy, robustness and preci- sion simultancously. The CDF curves are shown in B.5
Table 2: Metrics of Multi BS (4-BS) collaborative localization.
Method
MAE (m)
RMSE (m)
CDF@ Im (%)
SIGMAP (w/ map)
0.673
1.099
84.5
SIGMAP (w/o map)
0.789
1.285
77.5
LWLM
0.828
1.178
75.6
SWIT
1.102
1.368
68.1
CNN
1.398
1.731
59.3
OMP
1.685
2.089
50.6
4.3 EFFECTIVENESS OF CYCLE-ADAPTIVE MASKING
Table
compares masking strategies. Cycle-adaptive masking (last row) yields the best trade-off:
0.673 m MAE and 84.5% CDF@ Im, outperforming fixed grid or strip masking. Dynamic dis- ruption of periodic CSI patterns forces the model to learn generalizable features instead of shortcut interpolation.
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Muses
SIGMAP (ml map)
SIGMAP (wio
Overa
SinglBS
-ONN
DDP
scinaroo
TaA
NLO5
AGA
Figure 5: Comprehensive performance comparison across metrics. Our method shows consistent superiority accuracy and robustness.
Table 3: Effect of cycle-adaptive masking strategy.
Method
MAE (m)
RMSE (m)
CDF@ @ Im (%)
Grid-masking only
0.770
1.176
80.3
Strip-masking only
0.753
0.972
75.3
Adaptive masking
0.673
1.099
84.5
4.4 ABLATION STUDY ON MAP PROMPTS
To quantify the influence map quality on localization accuracy, we conducted an ablative compar- ison using (i) complete 3-D mesh, (ii) 2-D bird's-eye polygon, and (iii) no-map (CSI-only). Except for height, the 2-D variant follows the same pipeline as the 3-D version; the gap arises solely from missing height and facade normals.
Two-dimensional and three-dimensional map ablations are illustrated side-by-side in Figure The near-overlapping
bars indicate that most of the topological benefit is retained without vertical detail. This outcome suggests an immediate upgrade path: replacing the 2-D polygon with street-level photograph (visual prompt) could re-introduce facade and texture cues, offering a low- cost yet effective extension for future work.
Results are summarised Table the 2-D bird's-eye view degrades MAE by 8 % relative the full 3-D mesh confirming that most gain comes from topological/LoS cues and that the prompt mechanism is robust to moderate geometric simplification.
Table 4: Single-Br localization with different map modalities.
Method
MAE (m)
RMSE (m)
CDF@ 1m (%)
SIGMAP (3-D map)
1.564
5.675
60.5
SIGMAP (2-D birdview)
1.692
6.128
55.7
SIGMAP (w/o map)
2.275
8.532
31.0
4.5 GENERALIZATION TO NEW ENVIRONMENTS
We evaluate generalization on two completely unseen ray-tracing suites: (i) DeepMIMO.0O sce- nario and (ii) WAIR-D (Huangfu al.) (022) Scenario-2 (100 real-world city scenes extracted from OppnStreetMap). Typical complex scenes of WAIR-D are shown in Figure [ illustrating dense urban canyons and irregular footprints that challenge cross-domain transfer.
In experiments, only the downstream task heads are fine-tuned using limited target samples (ap- proximately 100 instances per scenario), while the self-supervised backbone remains frozen. This few-shot learning setup demonstrates the method's ability to rapidly adapt to new environments. Results are listed in Table 4.5

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Figure 6: Two typical complex scenes of WAIR-D.
Generalization performance unseen scenarios with minimal fine-tuning.
Method
MAE (m)
RMSE (m)
CDF@ 1m (%)
DeepMIMO 02 outdoor
SIGMAP (w/ map)
1.026
1.551
66.4
SIGMAP (w/o map)
1.282
5.824
63.9
LWLM
2.213
11.837
63.2
WAIR-D D Scenario-2 (100 cities)
SIGMAP (w/ map)
1.880
3.717
58.0
SIGMAP (w/o map)
2.578
4.650
51.5
LWLM
3.375
6.921
50.3
Equipped with geographic prompts, SIGMAP reaches 1.026 MAE on DeepMIMO O2 and 1.580 m on WAIR-D Scenario-2, outperforming LWLM by 53.2 % and 44.3 %, respectively, while updat- ing only 0.4 % of parameters. The results confirm that, generating environment-speecffee prompts, SIGMAP delivers good transfer performance across entirely different wireless environments.
4.6 PARAMETER EFFICIENCY
Table 5: Training comparison under our experimental setup.
Stage
Trainable Params
Time/Epoch
Total Time
Pre-train
11.730 M 0.085 M
10.8 min
36 30 min
Fine-tune
1.8 0.83 ms/sample
Inference
Under the experimental setup detailed in Appendix the model is first pre-trained for 200 epochs and then fine-tuned for 1000 epochs; because only 0.7% of the total parameters are activated during fine-tuning, the entire 1000-epoch fine-tuning stage takes merely 30 min, while still preserving the rich representations learned during pre-training, demonstrating significant parameter efficiency.
5 CONCLUSION
This paper presents wireless localization foundation model that achieves state-of-the perfor- mance through cycle-adaptive masking and geographic prompt tuning. Our approach delivers strong and consistent accuracy in both single-BS and multi-BS tasks, and generalizes robustly across pre- viously unseen geographic scenarios.
Future work will explore two key directions: extending beyond localization to develop general- purpose wireless foundation models for channel estimation, beamforming and signal processing tasks; and integrating visual modalities such images and point clouds with wireless signals to create richer environmental representations when 3D maps are incomplete or unavailable.
These advances will lead to more versatile and practical wireless perception systems for emerging applications in smart infrastructure and mobile computing.
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ACKNOWLEDGMENTS
This work was partially supported by the National Natural Science Foundation of China (62472125), Guangdong Basic and Applied Basic Research Foundation (2025A151501 1258), Key Technologies R&D Program of Guangdong Province (2026B0909060001) and Shenzhen Science and Technology Programs (GXWD20231 1000022112210220 1810022200,, ZDCY20250901 110000901111000000 I 17050077 ZDSSS2002002200 203008). This work supported by the Major Key Project of PCL.
(Grant
No. PCI.2024A08).
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Yanlin Ruan, Liang Chen, Xin Zhou, Zhaoliang Liu, Xiaoyan Liu, Guangyi Guo, and Ruizhi Chen.
iPos-3G: Indoor positioning via commercial 5G NR CSI. IEEE Internet Things J., 10(10)88718- 8733, 2023.
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A STATEMENTS
A. USE OF LLMS STATEMENT
We employed wwwallaggg-modeeee tools primarily for language polishing and phrasing sugges- tions. All technical content, experimental designs, and scientific interpretations conceived, reviewed, and approved by the authors.
A.2 REPRODUCIBILITY STATEMENT
To facilitate reproducibility, we have consolidated the complete pipeline- dataset generation, model pre-training, fine-tuning, and evaluation scripts-in in anonymous GitHub repository (htpps:
//anonynous. 4open. (cpeensscience///iiigee.. htr.oo/yaaapaaonnoooouuuuuu
anonymous- 838D))
B DETAILED SETUPS OF OUR EXPERIMENTS
B.1 COMPUTE RESOURCES
Our experiments were conducted on computing server equipped with the following specifications:
GPUs: 6 NVIDIA A800 80GB PCIe
GPU Memory: 80 GB per GPU (480 GB total)
Driver Version: 550.144.03. 144.03
CUDA Version: 12.4
The A800 GPUs provided the necessary computational power for training large-scale transformer models and processing high-dimensional CSI data with complex shift pattern augmentations.
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B.2 GENERAL CONFIGURATIONS
The input to the model is the complex CFR matrix H. To facilitate neural network processing, we decompose it into its magnitude and phase components, denoted as H and rewrite it as
H ||H,|, 2H,] R2x Natte
(12)
The input H, a 3D tensor of shape (2, Namt Nune), with the first dimension corresponding to the amplitude and phase, respectively. Although H. represents a specific format of the channel input, for notational consistency throughout the paper, we will still use H, to refer to the general representation of the input channel data in all subsequent discussions.
We employed transformer-based encoder-decoder framework specifically designed for wireless channel modeling and localization tasks. The key architectural components include:
Input Dimensions: B x C F where:
B: Batch size (32)
C: Channel dimensions (2 for real/imaginary components)
T: Time/Antenna dimension (128)
F: Frequency/Subcarrie dimension (32)
Encoder: Multi-head self-attention layers with positional encoding
Decoder: Cross-attention mechanisms for coordinate prediction
Feature Dimension: 512-dimensional latent representations
Table 6: Training Hyperparameters
Parameter
Value
Batch Size
32
Optimizer
Adam
Learning Rate
x 10 44
Weight Decay
10
Training Epochs
300
Gradient Clipping
1.0
Learning Rate Schedule
Cosine Annealing 10
Warm-up Epochs
B.3 DATASET PARAMETERS
Table 7: Detailed DeepMIMO dataset configuration parameters
Parameter
Pre-training
Fine-tuning
Scenario
O1 01_3p5
01 01_3p5
Number of BSs
4
BS IDs
[3, 10]
[3, 4, 9, 10]
Frequency bands (MHz)
[10, 20, 50]
10
Bandwidth (GHz)
[0.01, 0.02, 0.05]
0.01
Subcarriers
128
128
Antenna elements
32 Uniform
32
User distribution
Random
User subsampling
100%
2%
Number of paths
5
CSI samples
480,000
12,000
Train/Val/Testt split
10.000/1,000/10.00
The pre- pre-training data was generated using the following key parameters:
Scenario: 01 3p5 (urban outdoor environment)
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Active base stations: 3, 4, 9, 10
Frequency bands: 10MHz, 20MHz, 50MHz
Antenna configuration: 32-element uniform linear array
Subcarrier configuration: 128 subcarriers, all selected
User coverage: Complete row coverage (5200 users)
Each sample contains complex channel data (real and imaginary components), user and base station parameters. The fine-tuning dataset shares the same environmental scenario but with different sampling strategy:
locations, line-of-sight status, distance information, and
Single frequency band: 10MHz
User subsampling: 2% of available users
Data split: 10,000 training, 1,000 validation, 1,000 test samples
Quality filtering: Only samples with valid path information are included
The dataset ensures comprehensive coverage of the environment while maintaining realistic user distribution patterns for effective model evaluation.
B.4 DETAILS DATA AUGMENTATION EXPERIMENTS
CSI amplitude data often exhibits periodicity due to hardware properties of RF chains, such as antenna spacing and carrier frequency. For instance, wireless systems with uniform linear arrays, channel responses between antennas may repeat periodically after fixed number of antennas.
In the process of reconstructing masked channel data using Vision Transformer (ViT)-based Masked Autoencoder (MAE), masking only individual rows columns could allow the model to casily learn superficial periodic patterns, thereby failing to capture global features from redundant information.
To address this, we adopted a classical time-series method: computing the cross-correlation coeffi- cient between each row of the Channel Frequency Response (CFR) matrix and the next row. Given that each row has the same length, significant boundary effects emerge. To mitigate this, we re- stricted comparisons only valid regions, avoiding boundary artifacts. In the example provided, row shifts vary (e.g., 8, d -3, d 0, see Fig. 7), which can be positive, negative, or zero. This motivated our adaptive masking strategy.
When the bandwidth equals the row-wise shift amount, adjacent masked bands connect end-to-end, forming visually continuous diagonal strips without gaps. This represents the minimum critical width required to achieve solid and continuous masking. The shift pattern augmentation technique mathematically formulated as follows:
Main GenerateShiftMass((.... No
N,
(13)
where:
d: Slope parameter controlling shift direction and magnitude
Na: Number of antenna-based masks (8)
Ng: Number of subcarrier-based masks (32)
Time dimension size (128)
F: Frequency dimension size (32)
The shift pattern generation algorithm proceeds through these steps:
1. Parameter Initialization:
Bandwidth bw [d]
Half-bandwidth hw = bw//2
Padding P hoo
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2. Matrix Padding: Expand frequency dimension accommodate shifts:
Ppudded F + 2P
(14)
3. Antenna-based Mask Generation: For = 1 to Na:
Start column co () Column positions <(I) = - 1. d
(15)
(16)
Mask band (()) [c(t) hww c(t) + hw] n [0, padded)
(17)
4. Subcarrier-based Mask Generation: For j N.:
Start row Tо ((, Column positions <(f) = (t-ro) (-d) +
(18)
(19)
Mask band B(t) [c(t) hw, c(t) + hw) n [0, F Faadeed]
(20)
5. Mask Application: The final augmented input is computed as:
X augmented X M (1 M) @ Tmak
(21)
where mask represents learnable mask tokens.
CSI Amplitude Healmaps for Different Shift Patterns d
== -8
13
d
(c)
Artenna Indeox
Arienns index
Antenna Indix
Sub-carier
Sub-carrier
Figure 7: CSI Amplitude Heatmaps for Different Shift Patterns
B.5 LOCALIZATION ERROR CDF CURVES
The Cumulative Distribution Function (CDF) of localization eтrоr measures the probability that the positioning еттог is less than or equal given distance. the key metric used in Section 4.2 (Main Results) to compare accuracy and robustness across methods. Figures & 2 these CDFs for single-BS and 4-BS collaborative scenarios, respectively. steeper curve and higher value 1 m indicate better performance; SIGMAP (w/ map) reaches 60.5% and 84.5% CDF@ 1m in the two settings, clearly outperforming all baselines.
C CHARACTERISTICS OF CHANNEL DATA
Wireless Channel State Information (CSI) data exhibits unique characteristies that distinguish it from conventional vision or language modalities and even from generic time series and data. These traits motivated the antenna-subcarier joint masking used in SigMap.
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Single-Base Statton Localization Performance Comparison
Localiction Emor (ml
Figure 8: Single- Base Station Localization Performance Comparison
Mujtt Base Station Localization Performance Comparison
Curnenteve Deoriutto Rendonn
Localisation Cror (n1)
Figure 9: Multi-Base Station Localization Performance Comparison
C.1 UNIQUE DIMENSIONALITY: SPATTAL-TEMPORAL-SSEETT STRUCTURE
A CSI tensor H captured by multi-antenna OFDM system is inherently multi-dimennional, span- ning three critical domains:
Spectral Domain (Subcarriers): Represents the frequency-selective fading of the channel.
The correlation across subcarriers is function of the delay spread Tiax of the multipath environment, often modeled by the channel's frequency correlation function.
Spatial Domain (Antennas): Captures the geometric aspects of the propagation. The correlation across antenna elements n is a function of the angle spread Apment and array geometry, described by the spatial correlation matrix Ryputial H
Temporal Domain (Snapshots): Represents the time-varying nature of the channel due to mobility or environmental changes, characterized by the Doppler spread saa
This structure can be formalized as a 3D tensor E Na No. Nane, making it Spectral data cube. This is distinct from:
Time Series: Which are typically ID (Nume) and lack explicit spatial and spectral structure.
Tempooall Data (e.g., traffic grids, videos): Which often 2D+Time (Height x Width x Time) with spatial homogeneity. The spatial dimensions in CSI are non-grid (antenna array geometry) and coupled with the spectral domain.
-oii-ii--lkke like
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C.2 IMPLICATIONS FOR FOUNDATION MODEL DESIGN
The aforementioned characteristics necessitate specialized adaptations in foundation model archi- tecture and pre-training strategies, moving beyond direct applications of models designed for other modalities.
Beyond Standard VIT Patches: While Vision Transformers (ViTs) process images by splitting them into regular 2D patches, this is suboptimal for CSI. Our cycle-adaptive mask- ing strategy (Sec. 3.2) is direct response to this, designed to respect the inherent period- icity and structure within the spatial-spectral planes of the CSI tensor, rather than treating it as generic image.
Beyond Standard MAE for Images: Masked Autoencoding (MAE) for images relies on the intuition that adjacent pixels are highly correlated. CSI, the correlation structure is more complex and governed by wireless physics. A random masking strategy fails to exploit the known structure along the antenna and subcarrier dimensions. Our method explicitly leverages this domain knowledge to create more challenging and meaningful pre-training task.
Beyond NLP and Time Series Models: While models for natural language (e.g., GPT) or time series may handle ID sequences, they are not equipped to natively handle the intertwined 3D correlations present in CSI. The success of SigMap hinges on ability to simultaneously learn representations across these three domains through its tailored pre- training objectives.
In conclusion, the design of SigMap is a principled approach to building a foundation model that respects the unique inductive biases of wireless signal data, rather than forcing the data to conform to architectures designed for fundamentally different modalities.
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Reviews:
[{"review_id": "otZhJeUNq0", "reviewer": "Reviewer_ct8k", "summary": "This paper presents SigMap, a prompt-based architecture for cross-scenario wireless localization that integrates masked autoencoding with geographic and topological maps serving as soft prompts. The model introduces a cycle-adaptive masking mechanism designed to align with the cyclic nature of Channel State Information (CSI) signals, thereby improving feature learning during pretraining. Evaluated within simulated DeepMIMO environments, SigMap demonstrates strong generalization capability and achieves parameter-efficient few-shot adaptation. The approach aims to bridge the gap between environment-specific training and scalable localization across diverse wireless scenarios.", "questions": "Can the authors report scalability experiments by evaluating SigMap on larger or denser map graphs, or by simulating more complex urban propagation conditions, to objectively assess how the method performs in real-world large-scale deployments and justify its practical robustness?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "(1) The idea of using maps as prompts is both innovative and practical. By embedding spatial priors directly into the learning framework, the model can better understand geographic context without requiring explicit supervision or heavy parameterization. This approach provides a lightweight yet effective way to integrate domain knowledge into data-driven models.\n\n(2) The proposed cycle-adaptive masking strategy effectively leverages the inherent periodic and structural characteristics of CSI signals. This allows the pretraining process to focus on more informative segments of the data, improving robustness and representation quality, especially when dealing with noisy or incomplete measurements.\n\n(3) The demonstration of few-shot adaptation using a frozen backbone is impressive, as it highlights the model’s ability to generalize with minimal retraining. This efficiency in adapting to new environments or conditions suggests that SigMap could serve as a versatile foundation for scalable wireless localization systems, reducing computational and data requirements during deployment.", "weaknesses": "(1) The absence of real-world evaluation limits the impact of the results. Without validation on empirical datasets or publicly available benchmarks such as CSI-Bench, it is difficult to assess how well the approach generalizes beyond simulation. This gap weakens the practical relevance of the presented findings.\n\n(2) The paper’s claim of developing a “foundation model” for wireless localization appears overstated. While the architecture shows potential for generalization within simulated settings, it lacks evidence of robustness across devices, propagation environments, or hardware variations, all of which are critical for real-world applicability.\n\n(3) Although the system integrates several established components—masked autoencoders, vision transformers, and graph-based prompting—the overall architectural contribution feels incremental. The novelty lies more in the combination and application context rather than in introducing fundamentally new mechanisms or model designs.\n\n(4) The work asserts interpretability through the use of map prompts but does not provide supporting analysis. Visual or quantitative evaluation of how the prompts influence model predictions would strengthen the paper’s interpretability claims and offer deeper insights into model behavior.\n\n(5) The scalability of the proposed approach remains uncertain. The paper does not explore how the framework performs when applied to large-scale or densely connected map graphs, which are common in real-world urban deployments. Understanding such scalability constraints is important for practical use in complex environments.\n\n(6) While the paper relies on ray-tracing–based wireless simulation, this approach—though widely used—offers limited novelty unless extended with advanced modeling such as diffuse scattering, dynamic environments, or hybrid physics–ML calibration. The current setup would benefit from stronger validation or augmentation to better capture real-world propagation complexity.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "aW6Y6rt9A6", "reviewer": "Reviewer_uDeu", "summary": "The paper proposes SigMap, a multimodal foundation-model framework for wireless localization featuring (1) a periodicity-aware adaptive masking pretraining scheme tailored to CSI, and (2) a “map-as-prompt” mechanism that encodes 3D maps as geometric prompts for parameter-efficient finetuning. Experiments on DeepMIMO (O1-3p5) show gains for single/multi-BS localization and some few-shot cross-scenario transfer.", "questions": "All of the paper's training and testing are based on DeepMIMO, a simulation dataset. It lacks validation on data collected in the real world.\nReal-world signals are filled with noise, dynamic interference, and complex propagation effects that simulators cannot fully replicate. It is a significant unknown whether the clean physical laws learned from the simulator can maintain high performance in a dirty real-world environment.\nthe current \"Map-as-prompt\" primarily encodes the environment's geometry by processing 3D coordinates  with a GNN. It does not encode material information. The model doesn't know if it's facing a concrete wall that absorbs\"signals or a glass curtain wall that reflects them.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "I think how the authors use GNN to generate the Prompt  is a great innovation. It cleverly borrows the idea of Prompt-Tuning from LLMs, encoding 3D map information into lightweight soft prompts used to guide a large signal foundation model. This fundamentally solves the problem of model adaptation in new environments.", "weaknesses": "Weakness\n1.\tThe introduction has too many paragraphs, although it compares with many existing works, the logic is not clear. It cannot effectively introduce the work done in this article from existing works. In addition, the shortcomings of many existing studies, such as the inability to capture high-dimensional features for description, are not sufficient to demonstrate the inadequacy of these work.\n2.\tThe experimental setup is relatively single and the validation depth is insufficient. Evidence is mostly from a single ray-tracing world, and there is only one cross scenario experiment. The existing experiments are difficult to fully demonstrate the universal applicability of the proposed model. Apart from error metrics, are there any other experiments that demonstrate the effectiveness of the proposed model?\n3.\tHow to better reflect the mentioned advantages limited labeled samples, efficient parameters, interpretability? For example, the model proposed parameters efficient, but the comparison of training time, memory usage, inference complexity is insufficient.\n4.\tInsufficient ablation experiments.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "MwuDGJ6B86", "reviewer": "Reviewer_GjCa", "summary": "The paper proposes SIGMAP, a transformer backbone pre-trained with cycle-adaptive masked modeling on Channel State Information, then fine-tuned with a learned geographic prompt from a 3D map via a GNN. The paper claims three main contributions: (1) cycle-adaptive masking to break periodic shortcuts in CSI; (2) map-as-prompt conditioning using 3D geometry; (3) parameter-efficient adaptation with strong cross-scenario generalization.\n\nThe experiments demonstrate substantial improvements over other baselines, on both single- and Multi-BS localization, as well as generalization performance.", "questions": "1. Could the authors explain why the model achieves good generalization abilities to new environments? Since the algorithm is not trained using meta-learning or transfer learning, I am curious about how the model learns to generalize.\n\n2. Could the authors give an example of the 3D Map used in the paper? Can the authors discuss how the quality of the 3D map would affect the model’s performance?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The self-adaptive masking and GNN map-as-prompt strategies are novel and meaningful combinations for indoor localization task. The experimental results show significant advantages over other baselines.\n\n2. During fine-tuning, only prompt GNN and projection head are trained, while the backbone is kept frozen. This makes the model efficient and handy for deployment.\n\n3. The algorithm achieves consistent metric gains in different tasks. And the improvements are substantial.", "weaknesses": "1. The paper asserts good generalization abilities, but it’s not intuitively clear why the algorithm achieves this. The model isn’t trained using meta-learning or transfer learning techniques. The paper also lacks of experimental comparisons to modern baselines that target at generalization in indoor localization, e.g., [1].\n\n2. The paper doesn't mention how the quality or degradation of the 3D Map could adversely affect the performance of the model. Illustrations of the 3D Map used are needed. More ablation studies on the qualities of the 3D Map are desirable.\n\n\n[1] Gao, Jun, et al. \"MetaLoc: Learning to learn wireless localization.\" IEEE Journal on Selected Areas in Communications 41.12 (2023): 3831-3847.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)