DeepRT: A Hybrid Framework Combining Large Model Architectures and Ray Tracing Principles for 6G Digital Twin Channels

Abstract

With the growing demand for wireless communication, the sixth-generation (6G) wireless network will be more complex. The digital twin channel (DTC) is envisioned as a promising enabler for 6G, as it can create an online replica of the physical channel characteristics in the digital world, thereby supporting precise and adaptive communication decisions for 6G. In this article, we systematically review and summarize the existing efforts in realizing the DTC, providing a comprehensive analysis of ray tracing (RT), artificial intelligence (AI), and large model approaches. Based on this analysis, we further explore the potential of integrating large models with RT methods. By leveraging the strong generalization, multi-task processing capabilities, and multi-modal fusion capabilities of large models while incorporating physical priors from RT as expert knowledge to guide their training, there is a strong possibility of fulfilling the fast online inference and precise mapping requirements of the DTC. Therefore, we propose a novel DeepRT-enabled DTC (DRT-DTC) framework, which combines physical laws with large models like DeepSeek, offering a new vision for realizing the DTC. Two case studies are presented to demonstrate the possibility of this approach, which validate the effectiveness of physical law-based AI methods and large models in generating the DTC.

1. Introduction

The sixth-generation (6G) communication system is expected to integrate sensing and artificial intelligence (AI) capabilities to enable intelligent, hyper-reliable, and ubiquitous connectivity, thereby enabling the Internet of Everything (IoE) [1]. The channel, serving as the medium between the transmitter and the receiver, determines the performance limits of wireless communication systems [2]. To fulfill the objectives of 6G, realistic, high-fidelity, and efficient channel characterization is essential, leading to the emergence of the digital twin channel (DTC) [3,4]. The DTC is a novel technology that provides digital replicas of the entire process of channel fading states and variations in the physical world. This capability allows for proactive adaptation to the evolving technologies and requirements of wireless communication systems.

Ray tracing (RT), as a powerful physics-driven method for modeling wireless channel characteristics [5,6], has been widely applied in channel modeling. For example, in [7], a hybrid modeling approach that combines RT methods with channel measurement is presented for low-terahertz indoor communication, enhancing the overall accuracy of the channel model. An RT-based approach for deterministic radio channel modeling is proposed in [8], focusing on simulating the channel characteristics of reconfigurable intelligent surfaces (RISs) in urban environments. The study in [9] also highlights the key role of RT in constructing dynamic wireless environment models. Although RT has the potential to offer detailed environment mapping and accurate simulation results, its high computational complexity remains a significant challenge for implementing the DTC in the dynamic, large-scale, and ever-changing environments of 6G systems.

With the rapid advancement of technology, AI has gained great attention in the communications field due to its powerful analytical and generalization capabilities, enabling reliable channel prediction and real-time communication decisions. Several works have contributed to advancing the DTC through AI [10,11,12,13,14]. For example, the study in [11] proposes an artificial neural network model that incorporates environmental parameters for high-precision path loss prediction. An end-to-end neural network is designed based on uplink pilots to predict downlink channel state information (CSI), avoiding additional errors from uplink channel estimation [13]. All these works have shown the great potential of AI to enable the DTC. However, many existing AI models are relatively small scale, often designed for single tasks or specific scenarios. Moreover, most are purely data-driven methods, relying on large data while lacking physical constraints, which limits their ability to meet the diverse, multi-task demands of the DTC. Recently, large models, such as DeepSeek and OpenAI’s GPT series models, have risen to prominence due to their multi-task processing abilities, efficient inference, and excellent generalization. Many researchers are now exploring the application of these large models in the field of communications [15,16,17,18,19]. For example, the study in [15] builds a neural network for channel prediction based on the pre-trained GPT-2, achieving great prediction performance on generalization tests with low training and inference costs. A framework called WirelessLLM is proposed, which leverages large models to address various tasks in wireless networks, such as channel prediction and resource management, thus improving the intelligence and efficiency of wireless systems [16]. The study in [17] introduces WirelessGPT, a pioneering foundation model designed for multi-task learning in wireless communication and sensing. These large models exhibit strong modeling capabilities, exceptional generalization, and efficient processing in diverse, multi-task scenarios, offering a promising pathway for realizing the DTC.

In this article, we first provide a systematic review of existing efforts and challenges across different levels of channel twins, analyzing the roles and characteristics of RT, AI, and large models in enabling the DTC, particularly in terms of fast online inference and accurate channel prediction. Motivated by these insights, we explore the potential of combining large models with RT methods. By leveraging the strong generalization, multi-task processing capabilities, and multi-modal fusion capabilities of large models while incorporating RT-based physical priors as expert knowledge to guide their training, a promising trade-off between accuracy and efficiency can be achieved, further paving the way toward the vision of the online and precise mapping requirements of the DTC. Based on this, we propose a novel DeepRT-enabled DTC (DRT-DTC) framework, which combines the physics laws of RT with large models such as Deepseek, offering a promising approach to realize the DTC and addressing the identified challenges.

This article is organized as follows: Section 2 introduces the DTC and its framework; Section 3 summarizes the existing work on different levels of channel twins and introduces the promising framework of DRT-DTC; Section 4 offers two examples of how to help realize the DTC; Section 5 discusses some open issues and future opportunities; and Section 6 gives the conclusion.

2. Framework of DTC

In this section, we introduce the DTC and then provide a detailed description of its framework.

2.1. What Is the DTC?

The DTC utilizes digital twin technology to model and reconstruct the wireless channel by creating a virtual representation of the physical environment. It is capable of accurately reflecting the entire process of channel fading states and dynamic variations in the physical world, thereby enabling it to provide intelligent decision-making for wireless communication tasks. Meanwhile, the DTC interacts continuously with real-world feedback, dynamically updating its digital model and engaging in online learning to improve accuracy and better approximate actual channel conditions. As a result, it can provide more reliable and adaptive communications decisions. To support autonomous 6G networks, the DTC is expected to meet several key requirements, including precise mapping, with real-time, self-updating, and task-oriented characteristics [3].

2.2. Framework

Building on the capabilities of the DTC and the predictive 6G network [4], a novel DTC implementation framework is proposed, as shown in Figure 1. This framework follows the paradigm of “sensing–prediction–decision and interaction”, with each functional module designed to support intelligent and adaptive communication processes. The detailed operations of each module are elaborated below.

• Sensing: As noted in [3,4], two main types of information are required: environmental information and channel information. In this module, multi-modal data are collected from the physical world using various sensing devices such as cameras, radar, and GPS. These devices capture environmental information, including the location, size, and material of scatterers, as well as environmental maps. At the same time, channel information, another type of critical information for the DTC, is obtained from wireless communication devices. Then, the collected multi-modal and heterogeneous data undergo denoising, feature extraction, and dimensionality reduction, enabling an accurate reconstruction of the physical entities in the digital world. A summary of related work is presented in Table 1.

Table 1. Different methods for environmental sensing and reconstruction.

Ref.	Source	Method	Ability
[20]	Environment sensing devices	A sensing-enhanced radio environment prediction platform	Swiftly reconstruct the physical environment
[21]	Communication signaling with camera image	Multi-user selection and Multi-modal fusion	Increases the accuracy and robustness of environment reconstruction
[22]	LiDAR point cloud data	Pre-processing and post-processing techniques	Real-time mapping for digital twin development
[23]	Radio frequency data	Deep learning	Paving the way towards lightweight and scalable reconstructions
[24]	Channel knowledge map	Combines physical environment maps with deep learning techniques	Constructing detailed spatiotemporal maps of channel characteristics

Table 1. Different methods for environmental sensing and reconstruction.

Ref.	Source	Method	Ability
[20]	Environment sensing devices	A sensing-enhanced radio environment prediction platform	Swiftly reconstruct the physical environment
[21]	Communication signaling with camera image	Multi-user selection and Multi-modal fusion	Increases the accuracy and robustness of environment reconstruction
[22]	LiDAR point cloud data	Pre-processing and post-processing techniques	Real-time mapping for digital twin development
[23]	Radio frequency data	Deep learning	Paving the way towards lightweight and scalable reconstructions
[24]	Channel knowledge map	Combines physical environment maps with deep learning techniques	Constructing detailed spatiotemporal maps of channel characteristics

• Prediction: The collected multi-modal data are further processed to explore and establish the relationship between environmental information and channel data. AI techniques are employed to enable accurate channel prediction. For example, the wireless environment knowledge pool (WEKP) is proposed to explore the relationship between the channel and the environment, thereby providing prior knowledge for channel prediction [4]. An environment feature-based model is presented for predicting path loss using random forest methods [25]. Similarly, the study in [26] proposes a framework for millimeter-wave communication systems based on environmental semantics, using AI methods to extract semantic information in image form for beam and blockage prediction.
• Decision and interaction: Communication decisions are made in the digital world for the served users based on predicted channel data and then transmitted to the physical world. Subsequently, the DTC engages in continuous interaction with the physical environment via a feedback mechanism, iteratively updating its models and data. This closed-loop process enhances the accuracy of digital–physical mapping and ensures more reliable and adaptive decision-making.

3. DeepRT Enabled DTC

The evolution of the channel twin can be defined as five different levels, as mentioned in [3]. In this section, we first provide a systematic overview of recent research efforts and key challenges in realizing the DTC for 6G, with a particular focus on the latter three stages of channel twin evolution: intermediate, advanced, and autonomous twins. Furthermore, the promising DRT-DTC framework is introduced, which integrates RT-based physics laws with AI methods to achieve a level-5 ($L_5$) autonomous twin.

3.1. Traditional RT-Enabled Level-3 Intermediate Twin

RT [27], as a deterministic modeling method, simulates radio wave propagation based on the provided environmental information and transmitter–receiver configurations, thereby generating parameters such as path loss (PL), channel impulse response (CIR), and CSI. As shown in Figure 2, the RT process primarily consists of environmental construction, path-finding, electromagnetic computation, and channel prediction. The environmental model is typically constructed manually or imported from sources like Google Maps or OpenStreetMap. Additionally, the material properties of scatterers within the scene need to be defined to facilitate the subsequent electromagnetic computations.

Path-finding utilizes geometric optics (GO) and the uniform theory of diffraction (UTD) [28] to simulate electromagnetic wave propagation. It includes two types of algorithms: a direct algorithm and an inverse algorithm. The direct algorithm, known as shooting and bouncing rays (SBR), generates rays from the transmitter and traces them in all directions until they reach the receiver or exit the scene. When the rays encounter objects, their new directions are calculated based on reflection or diffraction. The inverse algorithm, based on Fermat’s principle, calculates the path between the transmitter and receiver using image-based methods.

Once the propagation paths of the electromagnetic waves are determined, parameters such as field strength at the receiver can be computed, including reflection and diffraction. The reflection electromagnetic calculation is based on the Fresnel equations:

$$R_{\Vert }={\displaystyle \frac{\frac{{\widehat{\epsilon }}_2}{{\widehat{\epsilon }}_1}cos{\theta }_i-\sqrt{\frac{{\widehat{\epsilon }}_2}{{\widehat{\epsilon }}_1}-{sin}^2{\theta }_i}}{\frac{{\widehat{\epsilon }}_2}{{\widehat{\epsilon }}_1}cos{\theta }_i+\sqrt{\frac{{\widehat{\epsilon }}_2}{{\widehat{\epsilon }}_1}-{sin}^2{\theta }_i}}}$$

(1)

$$R_{\perp }={\displaystyle \frac{cos{\theta }_i-\sqrt{\frac{{\widehat{\epsilon }}_2}{{\widehat{\epsilon }}_1}-{sin}^2{\theta }_i}}{cos{\theta }_i+\sqrt{\frac{{\widehat{\epsilon }}_2}{{\widehat{\epsilon }}_1}-{sin}^2{\theta }_i}}}$$

(2)

where $R_{\Vert ,\perp }$ is the reflection coefficient, ${\theta }_i$ is the angle of incidence, and ${\widehat{\epsilon }}_1$ and ${\widehat{\epsilon }}_2$ are the complex dielectric constants of the materials on either side of the reflective surface. Diffraction electromagnetic computation is based on UTD theory, and the diffraction coefficient $D_{s,h}$ is given as

$$\begin{array}{cc}\hfill D_{s,h}=& {\displaystyle \frac{-e^{-j\frac{\pi }{4}}}{2n\sqrt{2\pi k}sin{\beta }_0}}\hfill \\ \hfill & \times \left[cot\left({\displaystyle \frac{\pi +\left(\varphi -{\varphi }^{\prime }\right)}{2n}}\right)F\left[kL{a}^{+}\left(\varphi -{\varphi }^{\prime }\right)\right]\right.\hfill \\ \hfill & +cot\left({\displaystyle \frac{\pi -\left(\varphi -{\varphi }^{\prime }\right)}{2n}}\right)F\left[kL{a}^{-}\left(\varphi -{\varphi }^{\prime }\right)\right]\hfill \\ \hfill & \mp \left\{cot\left({\displaystyle \frac{\pi +\left(\varphi +{\varphi }^{\prime }\right)}{2n}}\right)F\left[kL{a}^{+}\left(\varphi +{\varphi }^{\prime }\right)\right]\right.\hfill \\ \hfill & \left.+cot\left({\displaystyle \frac{\pi -\left(\varphi +{\varphi }^{\prime }\right)}{2n}}\right)F\left[kL{a}^{-}\left(\varphi +{\varphi }^{\prime }\right)\right]\right\}\hfill \end{array}$$

(3)

where $\varphi$ and ${\varphi }^{\prime }$ are the angles of the diffracted and incident rays, respectively, and ${\beta }_0$ is the angle between the incident ray and the tangent to the edge. The k is the propagation constant of light, and $n=2-{\displaystyle \frac{\alpha }{\pi }}$, where $\alpha$ is the angle of the interior wedge. L is a distance parameter, given by the following expression, and the function $F\left(X\right)$ involves a Fresnel integral and is given by $F\left(X\right)=2j\sqrt{X}{e}^{jX}{\int }_{\sqrt{X}}^{\infty }{e}^{-j{\tau }^2}d\tau$:

$$L=\left\{\begin{array}{cc}s{sin}^2{\beta }_0\hfill & \mathrm{for}\text{plane-wave}\mathrm{incidence}\hfill \\ {\displaystyle \frac{r{r}^{\prime }}{r+r^{\prime }}}\hfill & \mathrm{for}\text{cylindrical-wave}\mathrm{incidence}\hfill \\ {\displaystyle \frac{s{s}^{\prime }}{s+s^{\prime }}}{sin}^2{\beta }_0\hfill & \mathrm{for}\text{conical-}\mathrm{and}\text{spherical-wave}\mathrm{incidences}\hfill \end{array}\right.$$

(4)

where s and $s^{\prime }$ are the distances along the diffracted and incident ray paths, respectively. For a cylindrical wave with radius $r^{\prime }$ incident on the edge, r is the perpendicular distance of the field point from the edge.

Based on the derived propagation paths and electromagnetic computations, the channel characteristics within the environment can be generated, including delay, PL, and CIR. The accuracy of these results mainly depends on the fidelity of environment modeling, the electromagnetic parameters of the materials, and the selected mechanisms for simulating wave propagation.

Traditional RT methods achieve accurate channel modeling by analyzing all possible electromagnetic wave paths in complex environments. They offer high precision, strong adaptability across diverse scenarios, and robustness to frequency variations. Moreover, RT methods are well suited to support new features emerging in 6G channels, such as integrated sensing and communication (ISAC) [29], extra-large-scale multiple-input multiple-output (XL-MIMO) [30], and RIS [31], making it a promising research direction for future channel modeling. However, in complex scenarios, the large number of face intersection tests significantly increases computation costs. Additionally, manually constructed or imported environmental models often lack sufficient geometric and material details [32], and the assigned electromagnetic parameters may not accurately reflect the properties of real materials, leading to decreased accuracy. Therefore, traditional RT methods are suitable for tasks with less stringent online requirements, such as network planning, management, and optimization, and they are typically used to support level-3 ($L_3$) twins in [3].

3.2. RT and AI Methods Enabled Level-4 Advanced Twin

In response to the high computational cost of traditional RT, various acceleration strategies have been proposed, as shown in

Table 2. Related work combining RT and AI.

	Ref.	Method	Application	Efficiency	Limitation
RT optimization	[33]	GPU and kD-tree	Path-finding	Acceleration	The bottleneck of the physical model, non-task-oriented, non-real time
RT dominance	[35]	Neural rendering	Path-finding	Reducing path-finding time
	[36]	Model-based NN	Material calibration	Automatic calibration
	[37]	Semantic segmentation	3D mapping	High precision
AI dominance	[38]	SR algorithm	Channel data generation	Generating HR propagation data	Requiring data, weak interpretability, small scale
	[39]	PMNet	PL prediction	Predicting PL fast and accurately
	[40]	Transformer-based model	PL prediction	Adaptation to various maps
	[25]	Environment feature-based model	PL prediction	Environment information acquisition and accurate prediction
	[41]	UNet	Simulating radio maps	Reducing run time and improving accuracy

NN: neural network; SR: super-resolution; HR: high resolution; PMNet: path loss map prediction-oriented NN; PL: path loss; UNet: U-shaped convolutional network.

. For example, the study in [33] leverages a kD-tree structure for the efficient processing of environmental geometry and utilizes GPU-based parallel computing to reduce the path-finding time. Moreover, methods such as simulated annealing (SA) [34] are employed to calibrate the electromagnetic parameters of materials, thus enhancing the accuracy of electromagnetic computations.

In recent years, with the rapid development of AI, machine learning (ML) has been increasingly applied to channel prediction, leading to performance improvements. These methods can be broadly categorized based on their level of AI integration: one enhances traditional RT by incorporating AI to address its limitations, while the other adopts an end-to-end paradigm, replacing physical modeling with neural networks. Table 2 lists some related works. For example, the study in [35] uses neural networks to simulate the interaction between rays and surfaces, improving the path-finding speed of the SBR model. AI can also be applied to the automatic calibration of electromagnetic parameters. In [36], gradient-based methods are used to calibrate the material properties, resulting in models that combine neural networks (NNs) with physics-informed characteristics. In environmental reconstruction, deep neural networks can generate 3D scenes with electrical information from point cloud data [37]. Furthermore, a trained super-resolution (SR) model is proposed to predict clusters and CIR [38]. Furthermore, NN models trained on large datasets can predict the path loss based on input environmental information and transceiver positions [25,39,40], without relying on RT algorithms. The study in [41] employs an end-to-end convolutional neural network (CNN) to rapidly and accurately generate radio maps, given the known environmental structure and transmitter location.

With the assistance of AI, the efficiency and environmental adaptability of RT have been improved, enhancing the accuracy of channel modeling and supporting level-4 ($L_4$) twins. However, the two approaches both have limitations. The former is based on physical models, using fixed physical formulas that attempt to simulate the real-world radio propagation phenomenon, but discrepancies between simulation and reality still lead to accuracy bottlenecks. Although various acceleration strategies have been employed, online performance in complex environments remains unsatisfactory. Moreover, the non-differentiable nature of traditional RT makes it unable to self-adapt or optimize according to environmental or task changes, limiting its flexibility. The latter approach, being purely data-driven, often neglects physical laws, requiring large volumes of high-quality data for training. It also suffers from weak interpretability and operates as a “black box”. Furthermore, many current AI models are small scale, task-specific, and lack generalization capabilities.

3.3. DeepRT Enabled Level-5 DTC

The core advantages of the DTC lie in its fast online inference and self-updating abilities, which define it as an $L_5$ twin, known as an autonomous twin [3]. As mentioned earlier, RT methods, as physical law-based models, have high computational complexity, making it challenging for them to meet real-time processing requirements. In contrast, AI-driven approaches offer improved efficiency through data-driven learning, but they often struggle with generalization in diverse environments and still face accuracy limitations.

Thus, in $L_5$, to enable the DTC to support self-sustaining and proactive online learning wireless systems and unlock novel capabilities and applications, it is essential to establish a data-model dual-driven AI-based network. In this context, large models offer greater potential than conventional small-scale AI models, owing to their strong generalization abilities, multi-task processing capabilities, and aptitude for multi-modal data fusion. By integrating RT-derived physical priors as expert knowledge into the training process of large models, it becomes possible to strike an effective balance between accuracy and efficiency, thereby unlocking new capabilities and applications for the DTC in diverse 6G scenarios.

To achieve this, we propose a novel framework called DRT-DTC, which is inspired by large-scale models such as DeepSeek [42,43,44] and is combined with physics-informed methods, providing a promising solution for the autonomous twin.

3.3.1. Framework

The system framework is shown in Figure 3, including data acquisition, the DeepRT network, and action decision.

• Data acquisition: Multi-modal data are collected from the physical world, including both environmental information and channel information. These data are sourced from base stations, environmental sensors, and user terminals. For example, 2D cameras capture RGB images, depth cameras provide depth maps, and LiDAR or 3D cameras extract point clouds. Additionally, real-time transceiver configurations (e.g., the location, frequency, bandwidth, and antenna) and multi-dimensional channel feedback (e.g., CIR, angle, delay, and power) are collected to support dynamic prediction and DRT-DTC adaptation. These multi-modal data sources provide a rich and accurate informational foundation for environment reconstruction and subsequent channel prediction. Through a sensor network and edge computing platforms, real-time data streams are transmitted to the DeepRT network for further processing and analysis.
• DeepRT network: The collected multi-modal data are processed and analyzed in this module, and communication decisions are made. The process consists of the following key steps:
  • Data processing: Data collected from various sources undergo cleaning, normalization, and temporal processing to ensure quality and consistency. This step is essential to make the data usable for subsequent analysis. Using multi-modal fusion techniques, the processed data are integrated into a unified representation. This ensures that information from different modalities can be effectively handled in the same space.
  • Multi-modal feature extraction: Specialized neural networks, such as deep neural networks, convolutional neural networks, or long short-term memory networks, are used to extract features from each data modality. The model automatically learns high-dimensional features from the data, while self-attention mechanisms are applied to dynamically adjust the weights of each modality feature, enhancing the network’s capacity to capture complex relationships across heterogeneous data sources.
  • Wireless environment knowledge pool: Inspired by RT methods, wireless environment knowledge (WEK) can be constructed based on fundamental electromagnetic propagation principles to explore the mapping relationship between the environment and the channel. The WEKP represents the integration and extension of the WEK. The architecture of the WEKP is introduced in [4]. Based on physical laws, the WEKP serves as a physical prior and provides expert knowledge to the network.
  • Knowledge embedding: The WEKP is designed to serve as a source of structured domain knowledge and physical constraints within the deep learning framework. Rather than treating knowledge as auxiliary features, this conceptual module aims to embed physically meaningful relationships, such as propagation principles and environmental semantics, into the model’s latent space to guide learning in a physics-consistent manner. Potential implementation approaches include relation-aware attention [45,46], symbolic embeddings [47], or multi-level alignment mechanisms [48] that map knowledge representations to the internal layers of the model. By incorporating the WEKP in this manner, the model is better constrained during training, mitigating issues common in purely data-driven approaches, such as limited generalization and poor adaptability in unfamiliar environments.
  • Mixture of experts (MoE) network: The processed multi-modal data are fed into the MoE network, which can dynamically select the most appropriate expert network based on the characteristics of the input data. This ensures that the network chooses the optimal processing path for different types of data. By facilitating the division of labor among multiple sub-networks, the MoE network effectively handles heterogeneous data and adapts to varying environmental conditions.
  • Hybrid-driven prediction module: This module first performs wireless channel prediction by combining data-driven learning with physics-informed knowledge. The predicted channel characteristics, such as path loss, delay spread, and spatial parameters, serve as inputs for subsequent decision-making tasks, including spectrum allocation, beamforming, and power control. By integrating both data-driven and physical insights, the module provides more accurate and reliable communication decisions.
• Action decision: The generated communication decisions are transmitted to physical devices and interact with the real-world wireless network. Simultaneously, feedback from the physical world is sent back to the DTC system through a feedback mechanism. Based on this, the model performs online learning, enabling self-updating and optimization. This closed-loop learning cycle ensures that the communication system can consistently make optimal decisions under dynamic environmental and network conditions.

Overall, this framework establishes a closed-loop intelligent communication system that integrates multi-modal perception, physics-informed learning, and adaptive decision-making. By fusing data-driven insights with electromagnetic priors, the system enables accurate and low-latency channel prediction, as well as robust decision-making in diverse environments. Through the action decision and feedback mechanism, the model continuously learns from real-world interactions and self-updates in real time. This ensures long-term adaptability and optimal performance under dynamic network and environmental conditions, paving the way for fully autonomous 6G wireless systems.

3.3.2. Advantages

• Strong modeling capability: The framework builds a data-model dual-driven network by embedding physical knowledge as expert priors. This design enables the model to learn complex features from data while maintaining consistency with real-world electromagnetic behaviors, thereby enhancing both prediction accuracy and physical consistency.
• Multi-modal fusion: By leveraging large models’ capabilities in handling multi-modal data, the framework integrates heterogeneous sources such as base stations, sensors, and user terminals. Through unified representation and self-attention mechanisms, it enables efficient cross-modal collaboration and the fusion of information.
• Multi-scenario adaptability: The MoE network dynamically selects the most appropriate expert model based on input data characteristics. This dynamic selection empowers the system to adapt to various network environments and data types, ensuring accurate communication decisions across diverse scenarios.
• Online self-learning and optimization: Through feedback mechanisms and online learning, the system performs self-optimization using real-world interaction feedback, ensuring that communication decisions remain optimal under dynamic environments and network conditions.

4. Validation

In this section, to validate the vision of DRT-DTC, which combines physical priors with AI, we introduce two simulation results.

4.1. WEK-Based AI Methods for Channel Prediction

In this subsection, we present a WEK-based AI method for channel prediction. The study in [49] builds a mapping relationship between environmental information and electromagnetic wave propagation based on stochastic geometry and electromagnetic propagation theory. This method quantifies the contributions of reflection, diffraction, and blockage to the propagation process by leveraging the coordinate information of the scatterers, transmitters, and receivers within the environment. The WEK is then constructed and can be used for channel prediction. In this work, all datasets are generated based on RT simulations from the Beijing University of Posts and Telecommunications and China Mobile Communications Group DataAI-6G Dataset (BUPTCMCC-DataAI-6G Dataset) [50]. The dataset covers an area of 646 m × 290 m. Within this area, channel data are simulated for a reception area of 59.5 m × 30.0 m, containing 7320 receivers arranged in 61 columns of 120 devices. Taking one column of receivers as an example, the computed wireless environmental knowledge is visualized in the form of a WEK spectrum, as shown in Figure 4a.

As an example, the study in [49] uses path loss prediction to compare the performance of different methods, including the unprocessed location data-based method, the environment feature-based method, and the proposed WEK-based method, using the RT-simulated datasets as true values. In this setup, a convolutional neural network is employed for path loss prediction. There are a total of 7320 data samples, each associated with a propagation knowledge matrix that includes three types of propagation: reflection, diffraction, and blockage. To ensure a robust evaluation, 75% of the data are allocated for training and 25% for testing, with the training and testing samples selected from non-overlapping RX locations. The effectiveness of the WEK-based method is validated by comparing the cumulative distribution function (CDF) plots of the predicted and true values. These CDF plots are shown in Figure 4b. These results demonstrate that the proposed WEK-based method exhibits a much more consistent trend with the true values than the contrast methods. This indicates that the WEK spectrum, as a physical prior provided to the AI model, is more capable of capturing the entire path loss process and leads to a more accurate representation of channel characteristics.

In addition,

Table 3. Performance comparison of path loss prediction obtained by the proposed WEK, the location data-based, and the environment feature-based methods [49].

Methods	NRMSE	RMSE (dB)	Training Time (s)	Testing Time (s)
Location data-based	0.565	4.212	8.21	0.227
Environment feature-based	0.456	3.399	7.67	0.170
Proposed WEK-based	0.271	2.020	4.07	0.004

compares the three methods in terms of the normalized root mean square error (NRMSE), root mean square error (RMSE), training time, and testing time. The proposed WEK-based method achieves a significantly higher prediction accuracy, with NRMSE reductions of 52.04% and 18.5% compared to the location data-based and environment feature-based methods, respectively. In particular, the RMSE of the WEK-based method is reduced to 2.020 dB, representing improvements of 2.192 dB and 1.379 dB over the respective baselines. While RT-based parameter generation takes about 20 minutes, the WEK enables predictions within milliseconds, reducing the computational complexity by four orders of magnitude and making real-time communication prediction feasible. These results demonstrate the effectiveness of integrating physical priors with AI techniques, as proposed in this work.

4.2. Large Models to Enable DTC

In this article, we propose a new vision for realizing the DTC by integrating large models. ChannelGPT [51] is a large model-driven DTC generator, which helps to validate the effectiveness of incorporating large models into the DTC framework. In this work, an outdoor urban scenario is constructed for dataset generation, covering an area of 200 m × 200 m with four building groups and four roads. RT simulations are conducted with receivers placed along the streets, resulting in rich multi-modal datasets that combine geometric, visual, and wireless information. ChannelGPT performs channel prediction based on multi-modal information and compares the normalized mean square error (NMSE) results with those of two other methods: random pilot sampling without wireless environment information (RS-WOWEI) and random pilot sampling with wireless environment information (RS-WWEI).

As shown in Figure 5, with the increase in training epochs, ChannelGPT demonstrates a faster initial convergence speed than the other methods. Furthermore, it outperforms other methods in terms of the NMSE, achieving the lowest NMSE throughout the training process. Large models like ChannelGPT often exhibit greater variability during training due to factors such as batch-to-batch variations, complex gradient updates, and the interplay among diverse data modalities. These fluctuations, although more pronounced, eventually stabilize as the model converges to a more optimal solution, resulting in superior overall performance. Additionally, to assess the generalization capability of the proposed ChannelGPT, all compared methods are trained on the original scenario and tested on a newly constructed scenario with different building and vehicle distributions. After being trained on the original scenario, the optimal models are re-evaluated in the new scenario using the NMSE, as presented in

Table 4. Comparison of the NMSE between the original and new scenarios for ChannelGPT and other methods [50].

Methods	NMSE (Original Scenario)	NMSE (New Scenario)
RS-WOWEI	0.0893	0.1030
RS-WWEI	0.0320	0.0414
ChannelGPT	0.0128	0.0129

. While the performance of RS-WWEI and RS-WOWEI degrades significantly under the domain shift, ChannelGPT maintains stable performance across both scenarios, demonstrating a promising generalization ability in diverse wireless environments. This highlights the significant potential of large models in optimizing channel prediction performance. Moreover, this suggests that integrating large models into the DTC generation process has strong prospects for future wireless communication systems.

5. Challenges and Future Opportunities

Although the DRT-DTC framework presents promising potential in enabling the DTC for 6G networks, it is still in its early stages, and several key challenges must be addressed for its further research.

Large-scale multi-modal data collection: The DRT-DTC framework, which uses large models, relies on high-quality, large-scale, and rich multi-modal datasets, incorporating both environmental sensing data and channel data. Collecting and storing such comprehensive data efficiently and cost-effectively remain a significant challenge, particularly given the need to capture the characteristics of complex and dynamic wireless environments. Additionally, synchronizing distributed devices for coordinated data collection poses further difficulties, as ensuring consistency and timeliness across multiple data sources is not trivial.

Collaboration between WEKP and large models: The integration of the WEKP with large models is still at an early stage. Although it has been demonstrated that incorporating the WEKP into AI models can effectively enhance communication tasks, embedding communication-specific knowledge into large models for online decision-making remains a significant challenge. Current research has provided promising results, yet more research is needed to explore the seamless collaboration between the WEKP and large models in dynamic environments.

Hardware and energy consumption optimization: The computational demands of training and running large models present significant challenges, especially in terms of hardware capabilities and energy efficiency. Large models powering DRT-DTC require substantial computational resources, which may not be readily available in current wireless network infrastructures. Moreover, the energy consumption associated with these processes is a critical concern, particularly in edge and mobile environments, where power constraints are prevalent.

Despite the current challenges, the advanced technological foundations of 6G, such as ISAC and distributed AI architectures, offer promising avenues to address these issues and ultimately realize the potential of the DRT-DTC framework.

6. Conclusions

In this article, we review and analyze the current efforts and key challenges in realizing the DTC for 6G networks, focusing on the evolution from $L_3$ to $L_5$ channel twins. We provide an analysis of the characteristics of RT, AI, and large models in enabling the DTC. While RT offers detailed physical modeling and accurate simulation results, its high computational complexity limits its applicability in dynamic environments. AI offers strong learning and generalization capabilities for real-time prediction, yet it struggles with multi-tasking and adaptation in diverse scenarios. Large models show great potential in enabling the DTC due to their strong generalization, multi-task processing, and multi-modal fusion capabilities. Inspired by the strengths of these three methods, we propose the DRT-DTC framework, which integrates physical priors from RT with the learning capabilities of large models. This hybrid approach offers a promising solution for realizing the DTC and effectively addresses the challenges identified. In addition, two case studies are presented to demonstrate the possibility of this approach, which validate the effectiveness of physical law-based AI methods and large models in generating the DTC. Finally, some open issues and future opportunities related to the implementation of DRT-DTC are discussed.