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Radio Environment Mapping with World Models for Active Measurement Control: Should Networks Dream of Optimal Control? Jernej Hribar∗ , Ljupcho Milosheski∗† and Ryoichi Shinkuma‡ ∗ Department for Communication Systems, Jožef Stefan Institute, Ljubljana, Slovenia † Jožef Stefan International Postgraduate School, Ljubljana, Slovenia ‡ Faculty of Engineering, Shibaura Institute of Technology, Tokyo, Japan
arXiv:2605.24028v1 [eess.SP] 20 May 2026
Email: {jernej.hribar, ljupcho.milosheski}@ijs.si
Abstract—Radio Environment Maps (REMs) have the potential to serve as an important enabler for intelligent modeling and control in emerging AI-native 6G networks. Despite significant progress, most REM construction methods remain passive, relying on interpolation or static uncertainty models and lacking an explicit mechanism to reason about how future measurements will affect reconstruction quality under a limited measurement budget. In this paper, we formulate REM construction as a sequential decision-making problem and propose a world-model–inspired framework for active Received Signal Strength Indicator (RSSI) map reconstruction. By learning an internal representation of the radio environment and employing a dreaming mechanism to simulate the impact of candidate measurements, the proposed approach actively selects measurement locations under a limited budget. Experimental results on real indoor RSSI data demonstrate that the proposed method significantly outperforms Gaussian Process–based interpolation in the few-shot regime, achieving up to a fivefold reduction in Root Mean Square Error (RMSE) with the same number of measurements. These results highlight the potential of world models as a powerful paradigm for sample-efficient radio environment mapping and intelligent model-based sensing in 6G and beyond networks. Index Terms—Radio Environment Maps, World Models, Machine Learning
I. I NTRODUCTION In emerging AI-native 6G networks, Radio Environment Mappings (REMs) are expected to play an important role in enabling environment-aware modeling and network optimization [1]. Applications such as indoor localization, network planning, spectrum management, and digital twins [2] can improve performance by employing accurate and easy-to-obtain REMs. Furthermore, in indoor environments, such radio maps containing information regarding wireless coverage can help reduce malfunctions or improve decision-making for many automation tasks, e.g., robots navigating in smart buildings. Unfortunately, obtaining these maps with a limited number of measurements, while also accounting for dynamic changes such as moving objects or users in the environment, remains a challenge. In deployment scenarios, measurements are also often sparse or costly to obtain. To overcome this challenge, a wide
range of classical approaches for constructing REMs have been proposed, including interpolation and model based techniques such as Gaussian Processes (GP), Kriging, etc., [3], as well as deep learning-based methods using convolution layers and Generative Adversarial Networks (GANs) [4]. While these methods can yield accurate radio map estimates under static conditions or dense measurements, their performance degrades when the environment changes or when only a limited number of measurements is available. In particular, they lack an explicit mechanism to reason about how new measurements will affect future predictions and therefore cannot actively guide measurement acquisition. To that end, we employ world model framework [5], which learns an internal representation of the environment and enable model-based reasoning about the expected impact of future measurements. World models rely on an internal representation of the environment that enables an agent to reason without directly acting in the real environment. Instead the agent relies on the concept of dreaming. Meaning, that the agent simulates the consequences of candidate actions within its learned model before executing them in the real world, allowing it to anticipate how each action may affect future observations. After an action is executed and new information about the environment is revealed, the internal representation is updated and used to refine the agent’s belief, guiding subsequent decisions in a sequential manner [6]. In our work, we adapt world-model framework to active radio environment mapping. Specifically, we propose a world-model based framework for active Received Signal Strength Indicator (RSSI) map reconstruction that leverages an available empty-environment map as structural prior information. As illustrated in Fig. 1, the framework consists of a vision model that reconstructs the occupied RSSI map from sparse observations and a dynamics model that predicts how the model’s internal belief evolves as new measurements are acquired. By simulating candidate measurement actions through dreaming, the system actively selects informative measurement locations and progressively refines its belief over the occupied environment. The final output is a dense RSSI map obtained after a limited number of measurements, i.e., obtain accurate map with only a few
Input
Output
RSSI map of the Empty Environment
Estimated Environment RSSI map
World Model Sample
Dreams:
Dynamics Model Measurements in Occupied Environment
Sparse Encoding
Dream Based Selector
Vision Model Variational Decoder (VAE) Decode
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Select measuring location:
Fig. 1: High-level overview of the proposed world-model–based framework for active radio environment mapping. The vision model reconstructs the occupied RSSI map from sparse measurements and an empty-environment reference map, while the dynamics model enables dreaming-based simulation of candidate measurement actions to guide measurement selection. shots. We validate the proposed framework using indoor WiFi RSSI data [7]. Although Wi-Fi and 6G cellular are separate technologies, both will remain foundational to future broadband connectivity in within the 6G ecosystem, with Wi-Fi playing a major role in enabling indoor use cases. In this context, indoor radio environment maps derived from Wi-Fi measurements capture propagation- and environment-driven characteristics (e.g., attenuation, shadowing, multipath, etc.) that are largely independent of the specific radio access technology, making the approach broadly applicable and extensible to other systems and potentially to outdoor deployments. In summary, this paper makes the following contributions: • We formulate active RSSI map reconstruction as a sequential decision-making problem and propose a worldmodel inspired framework to solve the problem. • We introduce a dreaming-based active measurement selection strategy that exploits prior information from an empty-environment RSSI map to guide sampling in occupied environments. • Through experimental evaluation and use of real world data1 , we demonstrate that the proposed framework significantly outperforms GP–based interpolation and static baselines in the few-shot regime, achieving up to five times lower Root Mean Square Error (RMSE) with the same number of measurements. The rest of the paper is organized as follows. Section II reviews related work. Section III presents the problem formulation, followed by the proposed solution in Section IV. Experimental evaluation and results are presented in Section V, and Section VI concludes the paper. II. R ELATED W ORK Our proposed solution is related to prior work that focuses on reconstructing radio maps from collected measurements using as few samples as possible [8]–[12]. For example, 1 The code supporting our experiments is publicly available on GitHub: https://github.com/hribarjernej89/WorldModelsREM
the authors in [8] proposed the DeepMap approach, which leverages GP to construct accurate indoor radio environment maps from sparse WiFi Radio Signal Straight (RSS) measurements. Similarly, the work in [9] employs GP-based active learning to select informative measurement locations for radio map construction, aiming to reduce the number of required samples. Bayesian neural networks combined with learningbased planning for actively selecting informative measurement locations are used in [10]. The authors in [11] propose a GAN-based radio map estimation approach that infers spatial RSS distributions from sparse user measurements without requiring transmitter information. Lastly, [12] presents a deep learning–based radio map estimation method using a convolutional autoencoder to learn spatial propagation structures from prior environments and thereby reconstruct radio maps accurately. In contrast to existing approaches that rely on interpolation, regression, or static estimates, our work adopts a world model framework in which the system learns an internal representation of the radio environment and explicitly reasons about the impact of future measurements through dream rollouts. Such a solution enables active measurement selection by predicting how the map reconstruction evolves as new samples are acquired. III. P ROBLEM F ORMULATION We consider the problem of active reconstruction of a REM from sparse received RSSI measurements. The environment is discretized into a two-dimensional grid Ω ⊂ Z2 of size H × W , where each grid cell represents a candidate measurement location. For a given access point, an emptyenvironment RSSI map Ze : Ω → R is assumed to be fully observed and available. Such a reference map can be obtained during network deployment or calibration phases, when the environment is empty or in a known reference state [13]. The corresponding occupied RSSI map Zo : Ω → R is unknown and can only be queried at selected locations, i.e., grid cells. A measurement at location i ∈ Ω returns: zi = Zo [i].
(1)
TABLE I: List of notation used. Notation Ω H, W Ze ∈ RH×W Zo ∈ RH×W St ⊆ Ω zi Vt ∈ RH×W ε(ẐN , Zo ) Mt ∈ {0, 1}H×W xt π Eϕ (·) zt ∈ R d z Dϕ (·) Ẑt at ∈ Ω pψ (zt+1 | zt , at ) (k) Z̃t+1 u(a) P K N
Description Discrete grid of candidate measurement locations Grid height and width Empty-environment RSSI reference map Occupied RSSI map (unknown) Set of measured locations at step t RSSI measurement at location i, zi = Zo [i] Sparse value map of collected measurements RMSE of reconstructed and occupied RSSI map Binary measurement mask Observation provided to the world model Measurement selection policy Encoder mapping observation to latent belief Latent belief state at step t Decoder mapping latent state to RSSI map Reconstructed RSSI map at step t Action: selected measurement location Latent dynamics model Dreamed RSSI map from latent rollout k Dreaming-based uncertainty score for action a Candidate action pool size Number of dream rollouts per candidate Measurement budget
Measurement acquisition is performed sequentially. At decision step t, the measurements have been collected at locations St ⊆ Ω, with |St |= t. The collected measurements are represented by a sparse value map Vt ∈ RH×W and a binary mask Mt ∈ {0, 1}H×W , defined as: ( ( zi , i ∈ St , 1, i ∈ St , Vt [i] = Mt [i] = (2) 0, i ∈ / St , 0, i ∈ / St . Together with the empty reference map, these form the observation available to the reconstruction system: xt = {Ze , Vt , Mt }.
(3)
Given observation xt , a reconstruction model fθ produces an estimate Ẑt = fθ (xt ) of the occupied RSSI map. Measurement acquisition incurs cost, and the system is subject to a fixed measurement budget N . The objective is to select a set of measurement locations SN ⊆ Ω, with |SN |= N , such that the final reconstruction error is minimized. Reconstruction quality is evaluated using the RMSE, denoted by ε, over the entire spatial domain. For a final estimate ẐN obtained after N measurements, the reconstruction error is defined as: s 1 X 2 ε(ẐN , Zo ) = (ẐN [j] − Zo [j]) . (4) |Ω| j∈Ω
Minimizing ε(ẐN , Zo ) promotes accurate reconstruction of the occupied RSSI map by penalizing both large-scale deviations and localized distortions induced by environmental changes. Under a fixed measurement budget N , the objective is to design a measurement selection policy π and a reconstruction model fθ that minimize the expected reconstruction error, h i min E ε ẐN , Zo s.t. |SN |= N, (5) π,fθ
where the expectation is taken with respect to the stochasticity in measurement selection, environmental variability, and model uncertainty. This optimization problem is inherently combinatorial, as the number of possible measurement subsets grows exponentially with grid size. Moreover, the impact of each measurement depends on previously collected measurements, making the problem sequential and history-dependent. As a result, exact optimization is computationally intractable, motivating the use of model-based strategies that reason about the expected effect of future measurements on the reconstructed RSSI map. IV. D REAMING OF O PTIMAL M EASUREMENT L OCATIONS W ITH W ORLD M ODEL F RAMEWORK To design a solution for the problem formulated in Eq. 5, we adopt a world model based framework, which is also illustrated in Fig. 1. The key idea is to reconstruct a REM using only a small number of available RSSI measurements. As shown in Fig. 1, the world model consists of two learned components: (i) a vision model, implemented as a Variational Autoencoder (VAE), i.e., Vt , which maps the current observation to a belief environment representation and decodes it into an estimated RSSI map, and (ii) a dynamics model, i.e., Mt , that predicts how this belief evolves in response to a measurement action. Active measurement selection is enabled through a dreaming mechanism that exploits these learned models. At each decision step, the system considers a set of candidate measurement locations and, for each candidate, simulates multiple imagined futures by propagating the belief through the dynamics model and decoding the resulting predicted maps. These dreamed reconstructions represent how the system expects the reconstructed RSSI map to change if a particular measurement was taken. The variability among the dreamed reconstructions is used as a proxy for predictive uncertainty, where a lower variance indicates a more informative measurement that is expected to reduce uncertainty in the belief about the environment. Consequently, the measurement selection policy prefers actions that minimize the expected predictive variance across the dreamed outcomes. Within the proposed framework, the empty-environment RSSI map serves as a fixed structural prior that conditions the reconstruction process. Rather than being learned, this reference map is provided as part of the observation and enables the world model to exploit the underlying spatial structure of the environment before occupancy-induced perturbations are introduced. By sequentially querying RSSI values at selected grid cells in the occupied environment, the system incrementally refines its belief and predicts the full occupied RSSI map. The combination of the VAE model and the dynamics model allows the system to reason about the expected impact of future measurements and to guide measurement selection under a limited budget. In Alg. 1, we outline the operation of the proposed solution. The algorithm starts by specifying the required inputs, namely the empty-environment RSSI map Ze and the measurement budget N , and initializing the system (line 1). At each decision
step t, the reconstruction system receives the empty reference map Ze together with the current set of sparse occupied measurements St , encoded as a sparse value map Vt and a binary mask Mt . These components form the observation (line 3): xt = {Ze , Vt , Mt }.
(6)
The observation is then encoded by the VAE into a belief representation (line 4), after which a candidate set of measurement actions is sampled (line 5). For each candidate action, the algorithm performs K dreaming rollouts by propagating the belief through the dynamics model and decoding the resulting predicted beliefs into RSSI maps (lines 8–9). The candidate actions are then scored using the predictive disagreement across the dreamed reconstructions, computed as the mean spatial variance (line 11): h i 1 X (k) Vark Z̃t+1 [p] . (7) u(a) = |Ω| p∈Ω
Since lower variance corresponds to a greater expected reduction in uncertainty, the next measurement location is selected as:
until the measurement budget |SN |= N is exhausted. The final reconstruction is obtained by decoding the belief inferred from xN , yielding the estimate ẐN (line 18). Implementation: The VAE model operates on threechannel grid observations comprising the empty-environment RSSI map, sparse occupied measurements, and a binary measurement mask. Its encoder uses four 3×3 convolutional layers with ReLU activations (two with 32 channels and two with 64 channels), interleaved with two max-pooling layers, followed by a fully connected layer with 256 neurons that outputs the mean and log-variance of a 64-dimensional state. The decoder maps this state back to the spatial domain using a fully connected layer, two upsampling stages with convolutional and a linear layer. The dynamics model is implemented as an action-conditioned recurrent model with a single Long Shortterm Memory (LSTM) cell of 128 hidden neurons; actions, represented as normalized spatial coordinates, are embedded via a two-layer multilayer perceptron and concatenated with the state to predict a Gaussian distribution over the next state. During dreaming, multiple candidate actions are evaluated by sampling rollouts, decoding them into RSSI maps, and selecting the action that improve the accuracy of map reconstruction. V. VALIDATION AND R ESULTS
at = arg min u(a) a∈At
(8)
in line 13. The system then queries the occupied environment to obtain the corresponding measurement (line 14), updates the measurement set St+1 (line 15), and refreshes the sparse representations Vt+1 and Mt+1 (line 16). This process repeats Algorithm 1 Dreaming World-model solution for active measurement control for REM reconstruction Require: Empty RSSI map Ze , measurement budget N , encoder Eϕ , decoder Dϕ , latent dynamics pψ (zt+1 | zt , at ), candidate pool size P , number of dream samples K 1: Initialize S0 ← ∅, V0 ← 0, M0 ← 0 2: for t = 0 → N − 1 do 3: Form observation xt = {Ze , Vt , Mt } 4: Encode state zt ← Eϕ (xt ) 5: Sample candidate set At ⊆ Ω \ St with |At |= P 6: for a ∈ At do 7: for k = 1 → K do (k) 8: Sample dreamed map zt+1 ∼ pψ (zt , a) (k) (k) 9: Decode dreamed map Z̃t+1 ← Dϕ (zt+1 ) 10: end for h i P (k) 1 11: Score u(a) ← |Ω| [p] Var Z̃ k t+1 p∈Ω 12: end for 13: Select action at ← arg mina∈At u(a) 14: Query environment: zat ← Zo [at ] 15: Update St+1 ← St ∪ {at } 16: Set Vt+1 [at ] ← zat and Mt+1 [at ] ← 1 17: end for 18: Output final reconstruction ẐN ← fθ (xN ) (VAE-based reconstruction from xN )
In this section, we evaluate the proposed world model–based framework and compare it against two baseline methods, namely a GP–based reconstruction and a copy empty environment map, with performance quantified using RMSE and Mean Absolute Error (MAE). The evaluation is conducted across multiple environment sizes, allowing us to assess robustness. Parameters including the candidate pool size P and dream roll-outs K were selected via grid search on the training scenarios. Table II summarizes the experimental settings, including the number of training episodes Eep and the world-model parameters η and βKL . Dataset: In our validation, we employ an indoor RSSI dataset we collected from a single Wi-Fi Access Point (AP) operating on 2.4 GHz band over a two-dimensional grid [7]. The dataset comprises paired measurements from an openconcept, furnished office, collected under two scenarios: unoccupied and occupied by 7-10 people performing regular 18 × 22
36 × 44
72 × 88
(a) Training loss
(b) RMSE values
0.6 loss
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training epoch 10
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Fig. 2: Training loss and RMSE over number of training epochs for different environment sizes.
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(a) Empty room map.
(b) GP map.
(c) World model map.
(d) Ground truth map.
Fig. 3: Reconstructed RSSI environment maps obtained using different methods for a grid of size 36 × 44 with N = 20 measurements, with x representing selected measuring points. Empty Room Baseline
GP
TABLE II: Values of experimental parameters.
Proposed World Model
Parameter P E η
(b) MAE
(a) RMSE
ε
5 MAE
6
Value 40 200 10−3
Parameter K Eep βKL
Value 12 50 10−3
4 3
4
2 2 1 N 10
20
N
30
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Fig. 4: RMSE and MAE over number of samples N for enviroment size 36 × 44. Empty Room Baseline
ε
GP
Proposed World Model
4.59 4.13
4.13
4.12
4.08
4
3.41 2.94 2.26
2.76
2.44 1.81
2 1.34 0.48
0.64
0.81
0 9 × 11
18 × 22 36 × 44 72 × 88 Environment size H × W
144 × 176
Fig. 5: RMSE over different environment sizes for N = 20. working activities. Four such empty vs. occupied scenario 4 pairs were collected, where three are of them used for training the proposed framework and GP baseline and one is reserved for validation and evaluation. As reflected in the implementation, the empty RSSI map serves as a fully observed prior, while sparse measurements from the occupied map are sequentially queried to reconstruct the full radio environment map. Furthermore, to study multi-resolution behavior, the original grid of size 9 × 11 grid is uniformly upscaled using bilinear interpolation with scale factors {1, 2, 4, 8, 16}, enabling evaluation over higher spatial granularities. Baselines: We employ two baseline solutions against which the performance of the proposed framework is compared. The first baseline simply copies the empty-environment RSSI
map, serving as a reference to illustrate how environmental occupancy impacts the overall reconstruction performance. The second baseline employs a GP, in which a prior model of the radio environment is constructed by learning the spatial covariance structure from the empty-environment RSSI map. Specifically, a stationary kernel composed of a constant term, a Radial Basis Function (RBF), and a white-noise component is fitted to measurements from the empty environment. The learned kernel is then reused as the prior covariance function when reconstructing the occupied-environment RSSI map. Given a set of sparse measurements from the occupied environment, the GP posterior is obtained by conditioning this prior on the observed values, enabling interpolation over the full grid while preserving the spatial characteristics inferred from the empty environment, and thus providing a comparable solution. We first analyze the training behavior of the proposed world model across multiple environment sizes. Fig. 2 reports the training results obtained using three training scenarios for each spatial scale. Fig. 2(a) shows the training loss as a function of the number of epochs, demonstrating consistent convergence across all environment sizes. As training progresses, the loss decreases steadily, indicating that the model improves its internal representation of the radio environment and its ability to incorporate sparse measurements. On the other hand, Fig. 2(b) show the RMSE during training. While the training loss continues to decrease over epochs, the RMSE stabilizes after approximately ten epochs. This behavior suggests that, beyond this point, further optimization mainly refines the internal representation without yielding substantial improvements in reconstruction accuracy of the world map. This indicates that the world model solution reaches a stable reconstruction capability early in training. Fig. 3 presents a qualitative comparison of reconstructed RSSI environment maps obtained using different methods. The figure includes the empty-environment map, the GP-based reconstruction, the proposed world model reconstruction, and the ground-truth occupied map. A clear discrepancy can be
observed between the empty map (Fig. 3(a)) and the ground truth (Fig. 3(d)), highlighting the impact of occupancy on signal propagation in the employed dataset. Compared to the GP-based approach (Fig. 3(b)), the proposed world model (Fig. 3(c)) produces reconstructions that are visually closer to the ground truth, particularly in regions with sparse or no measurements. Fig. 4 illustrates the impact of the number of available measurements N on reconstruction accuracy for a fixed environment size. Both RMSE (as defined in Eq. 4) and MAE are reported. The empty-environment baseline remains constant across all values of N , serving as a reference that does not incorporate any new measurements. The proposed world model framework achieves low reconstruction error even with a very small number of measurements. Notably, with only a single measurement, the proposed framework already outperforms the GP-based approach significantly as well as the empty room baseline, as shown in Fig. 4(a). In contrast, the GP requires a substantially larger number of samples to achieve comparable accuracy. For example, at N = 10, the RMSE of the proposed framework is approximately four times lower than that of the GP-based method. A similar trend is observed for MAE in Fig. 4(b). These results indicate that the learned world model is particularly effective in the few-shot regime, while the GP gradually improves as more measurements become available. The final evaluation, shown in Fig. 5, analyzes reconstruction performance across different environment sizes using bar plots. As expected, reconstruction error increases for both methods as the spatial resolution of the environment increases. However, the proposed world model consistently outperforms the GP-based approach across all scales. The relative performance gap decreases with increasing environment size. For smaller environments (9 × 11), the proposed world model achieves up to a 4.7× reduction in RMSE compared to the GP baseline. For larger environments, e.g., 144 × 176, this advantage reduces to approximately 1.9×. Overall, the results highlight a clear trade-off between measurement efficiency and computational complexity. While GP-based methods can achieve competitive performance with a sufficient number of measurements, their computational cost grows cubically with the number of observations, limiting scalability. In contrast, the proposed world model framework maintains low reconstruction error in the few-shot regime and enables efficient active measurement selection through dreaming. A key advantage of the proposed solution lies in its learned internal representation of the environment, which supports informed decision-making about where to measure next. This makes the approach particularly suitable for applications where only a small number of measurements can be collected, such as rapid radio environment mapping or userassisted sensing scenarios. VI. C ONCLUSION This paper demonstrates the potential of world models for active radio environment mapping under limited measurement budgets. By learning an internal representation of
the radio environment and using dreaming to reason about the expected impact of future measurements, the proposed framework enables informed and uncertainty-aware measurement selection without relying on dense sampling. The results show that learned world models substantially outperform GPbased interpolation in the few-shot regime, while dreamingbased acquisition further improves performance as additional measurements are collected. Beyond improved reconstruction accuracy, the key advantage of the proposed framework lies in its ability to exploit internal beliefs for decision making, which is particularly relevant for AI-native 6G systems. As future work, integrating more advanced model-based reinforcement learning techniques could further enhance long-horizon planning and adaptability in dynamic radio environments. ACKNOWLEDGEMENTS This work was supported in part by the Slovenian Research Agency (ARIS) under grants P2-0016 and MN-0009. and by the Japan Society for the Promotion of Science (JSPS) under grants 23H00464, 25H01124, and 120245002. Additional support was provided by the bilateral project MISA (BI-JP/2426-001), funded jointly by ARIS and the JSPS. R EFERENCES [1] Y. Zeng and X. Xu, “Toward Environment-aware 6G Communications via Channel Knowledge Map,” IEEE Wireless Communications, vol. 28, no. 3, pp. 84–91, 2021. [2] S. Bi, J. Lyu, Z. Ding, and R. Zhang, “Engineering Radio Maps for Wireless Resource Management,” IEEE Wireless Communications, vol. 26, no. 2, pp. 133–141, 2019. [3] M. Pesko, T. Javornik, A. Kosir, M. Stular, and M. Mohorcic, “Radio Environment Maps: The Survey of Construction Methods,” KSII Transactions on Internet and Information Systems (TIIS), vol. 8, no. 11, pp. 3789–3809, 2014. [4] B. Feng, M. Zheng, W. Liang, and L. Zhang, “A Recent Survey on Radio Map Estimation Methods for Wireless Networks,” Electronics, vol. 14, no. 8, p. 1564, 2025. [5] D. Ha and J. Schmidhuber, “Recurrent World Models Facilitate Policy Evolution,” Advances in neural information processing systems, vol. 31, 2018. [6] D. Hafner, J. Pasukonis, J. Ba, and T. Lillicrap, “Mastering Diverse Control Tasks Through World Models,” Nature, pp. 1–7, 2025. [7] L. Milosheski, K. Akiyama, B. Bertalanič, J. Hribar, and R. Shinkuma, “An Indoor Radio Mapping Dataset Combining 3D Point Clouds and RSSI,” arXiv preprint arXiv:2511.00494, 2025. [8] X. Wang, X. Wang, S. Mao, J. Zhang, S. C. Periaswamy, and J. Patton, “Indoor radio map construction and localization with deep Gaussian processes,” IEEE Internet of Things Journal, vol. 7, no. 11, pp. 11 238– 11 249, 2020. [9] K. D. Polyzos, A. Sadeghi, W. Ye, S. Sleder, K. Houssou, J. Calder, Z.L. Zhang, and G. B. Giannakis, “Bayesian Active Learning for Sample Efficient 5G Radio Map Reconstruction,” IEEE Transactions on Wireless Communications, 2024. [10] W. Lu, S. Gao, M. Wen, Y. Liang, L. Yang, C.-B. Chae, and H. V. Poor, “Bayesian-Driven Graph Reasoning for Active Radio Map Construction,” arXiv preprint arXiv:2508.09142, 2025. [11] Z. Zhang, G. Zhu, J. Chen, and S. Cui, “Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN,” in 2024 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2024, pp. 1641–1646. [12] Y. Teganya and D. Romero, “Deep Completion Autoencoders for Radio Map Estimation,” IEEE Transactions on Wireless Communications, vol. 21, no. 3, pp. 1710–1724, 2021. [13] J. Wilson and N. Patwari, “Radio tomographic imaging with wireless networks,” IEEE Transactions on Mobile Computing, vol. 9, no. 5, pp. 621–632, 2010.