Wireless Physical-Layer Multi-Task Foundation Models Toward 6G Embodied Intelligence: Advances, Opportunities, and Challenges

TIAN Yunxiao; SHI Qingyu; QIAO Li; YING Keke; WAN Ziwei; MAO Yijie; GAO Shijian; ZHAO Zhongliang; CHEN Xiaoming; GAO Zhen

doi:10.11999/JEIT260754

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TIAN Yunxiao, SHI Qingyu, QIAO Li, YING Keke, WAN Ziwei, MAO Yijie, GAO Shijian, ZHAO Zhongliang, CHEN Xiaoming, GAO Zhen. Wireless Physical-Layer Multi-Task Foundation Models Toward 6G Embodied Intelligence: Advances, Opportunities, and Challenges[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260754

Citation:

TIAN Yunxiao, SHI Qingyu, QIAO Li, YING Keke, WAN Ziwei, MAO Yijie, GAO Shijian, ZHAO Zhongliang, CHEN Xiaoming, GAO Zhen. Wireless Physical-Layer Multi-Task Foundation Models Toward 6G Embodied Intelligence: Advances, Opportunities, and Challenges[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260754

Citation:

TIAN Yunxiao, SHI Qingyu, QIAO Li, YING Keke, WAN Ziwei, MAO Yijie, GAO Shijian, ZHAO Zhongliang, CHEN Xiaoming, GAO Zhen. Wireless Physical-Layer Multi-Task Foundation Models Toward 6G Embodied Intelligence: Advances, Opportunities, and Challenges[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260754

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Wireless Physical-Layer Multi-Task Foundation Models Toward 6G Embodied Intelligence: Advances, Opportunities, and Challenges

doi: 10.11999/JEIT260754 cstr: 32379.14.JEIT260754

TIAN Yunxiao^1
,,
SHI Qingyu^1
,,
QIAO Li^2
,,
YING Keke^1
,,
WAN Ziwei^3
,,
MAO Yijie^4
,,
GAO Shijian^5
,,
ZHAO Zhongliang^6
,,
CHEN Xiaoming^7
,,
GAO Zhen^{1, 3, 8
,
,}

1.
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
2.
Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong 999077, China
3.
Yangtze Delta Region Academy of Beijing Institute of Technology (Jiaxing), Jiaxing 314000, China
4.
School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
5.
Internet of Things Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511400, China
6.
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
7.
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
8.
Advanced Technology Research Institute, Beijing Institute of Technology, Jinan 250300, China

Funds: Supported by the National Natural Science Foundation of China (No.62471036, No.U2233216), Supported by the Natural Science Foundation of Shandong Province, China (No.ZR2025QA30), Supported by the Beijing Natural Science Foundation (No.L242011, No.QY24167, No.QY25256, No.QY25257)

Received Date: 2026-06-03
Accepted Date: 2026-11-02
Rev Recd Date: 2026-11-02

Available Online: 2026-11-09

Abstract

Abstract

Objective As embodied intelligence expands to robots, unmanned platforms, and collaboration among multiple agents, wireless networks must support information exchange while agents and their surrounding environments continue to change. Agent motion, blockage, and changing access conditions create recurring demands for channel updates, beam management, wireless localization, and coordinated transmission in sixth-generation (6G) systems. The Synesthesia of Machines framework connects communication with intelligent multimodal sensing and provides an application context for understanding these requirements. Existing learning-based physical-layer methods are frequently developed for individual tasks and specific system configurations. Changes in propagation conditions, available observations, or processing objectives may therefore require additional training or model redesign. Wireless physical-layer foundation models have attracted interest as a means of acquiring reusable capabilities through pretraining and connecting them to downstream tasks through adaptation. However, existing studies differ in their backbone architectures, training data, input representations, evaluated tasks, and adaptation requirements. These differences complicate comparisons and make it necessary to distinguish demonstrated capabilities from broader expectations of generalization. This review examines wireless physical-layer foundation models from the perspective of the continuing wireless needs of embodied systems. It aims to clarify their technical pathways, summarize the tasks assessed in existing studies, and explain the mechanisms connecting pretrained representations to downstream processing objectives. Methods The literature is organized along three dimensions: the source of pretrained backbone weights, the tasks evaluated in the original studies, and the mechanisms used for downstream adaptation. Two technical pathways are distinguished. Wireless-native models acquire their backbone capabilities through pretraining on wireless observations, whereas methods based on pretrained large language models transfer existing language-model weights to physical-layer processing. Task coverage is described according to the evidence reported in each study, including channel prediction and estimation, beam selection, localization, signal classification, detection, and precoding. Adaptation mechanisms are examined through task-specific heads, task prompts, parameter updates, and in-context observation–target examples. This framework separates how a model acquires reusable capabilities from how those capabilities are applied to a particular task (Table 2, Fig.1). Representative simulation and measurement datasets are reviewed in terms of their observations, labels, propagation conditions, and supported research objectives. Particular attention is given to the relationships among channel measurements, spatial positions, and environmental information, because these relationships influence the capabilities that can be learned and reused (Table 3). For wireless-native models, the analysis covers input representations, pretraining objectives, adaptation across tasks and configurations, observation quality, and computational requirements. For methods based on pretrained large language models, it examines numerical input mappings, task specification, parameter-efficient adaptation, and output-interface design. Representative models are then compared through task coverage, reported model scale, inference conditions, and publicly available resources (Tables 5 and 6). An evaluation framework is summarized around task performance, transfer capability, adaptation cost, deployment cost, and robustness (Table 7). Complementary experiments compare representative models on beam prediction, channel interpolation, and temporal channel prediction. These experiments use controlled data splits and training sample counts where applicable, and distinguish testing on public data from fixed-weight evaluation in independently generated propagation scenarios. Results and Discussions Existing studies demonstrate several approaches to reusing model capabilities across wireless physical-layer tasks. Wireless-native models construct representations that retain relevant relationships across time, frequency, and antennas. Pretraining objectives such as masked reconstruction and contrastive learning provide different means of learning channel structure and extracting reusable features. Downstream task heads connect shared representations to specific processing objectives, while observation masks and task conditions can organize related estimation or prediction problems within a common framework. In-context approaches instead use observation–target examples to specify the processing behavior required for a current query, allowing task execution without updating model weights during inference. These mechanisms impose different requirements on labeled data, input length, parameter updates, and task interfaces. Consequently, the availability of a shared backbone does not by itself establish that a model can handle arbitrary wireless tasks or configurations. The scope of reuse should be assessed through the tasks, conditions, and adaptation procedures actually evaluated. The review also identifies observation quality and computational cost as practical constraints. Sparse measurements, noise, interference, and configuration changes affect the information available to the model, while input sequence length, backbone design, and compression strategies influence processing overhead. Methods based on pretrained large language models rely on coordinated wireless input mappings, task prompts, parameter updates, and output interfaces. Their performance must therefore be assessed through the complete processing pipeline rather than inferred from the capabilities of the original language backbone. The complementary experiments further show that model comparisons depend on both the target task and the evaluation conditions. With the specified training sample counts, LWM and WiMamba achieve better mean results than RawCSI on beam prediction and channel interpolation. WiMamba obtains higher beam prediction accuracy, whereas LWM achieves a slightly lower interpolation error and more consistent results across repeated training runs. Differences in inference time provide additional information for selecting a model under processing constraints (Table 8, Fig.6). Temporal channel prediction reveals a different aspect of model behavior. On the public test dataset, the task-trained LLM4WM weights achieve the lowest prediction error, while the supervised RawCSI baseline combines a relatively low error with a smaller parameter count and shorter inference time. The directly applied WiFo and WiFo-2 pretrained weights produce higher errors under this particular test condition (Table 9). When all model weights remain fixed and evaluation moves to independently generated urban and rural macrocell scenarios, the relative ranking changes. LLM4WM and RawCSI show substantially increased errors, while the two WiFo models obtain lower mean errors in these scenarios (Fig.7). These results indicate that favorable performance on a public test dataset does not establish suitability for a new propagation environment. They also show why task performance, training history, adaptation data, runtime cost, and robustness should be considered together. Conclusions Wireless physical-layer foundation models provide multiple mechanisms for reusing learned representations and processing capabilities across downstream objectives. Wireless-native pretraining and the adaptation of pretrained large language models rely on different modeling foundations, and their practical value depends on input design, task interfaces, adaptation procedures, and operating conditions. The reviewed evidence supports continued investigation of shared processing capabilities while emphasizing the need for explicit statements of task coverage and transfer conditions. Four research directions are identified. First, dynamic-environment adaptation should examine how models recognize changes in propagation conditions, incorporate recent observations, and maintain reliable processing during updates. Second, reuse across system configurations requires a clearer understanding of which changes in antenna scale, frequency, bandwidth, and observation structure can be accommodated by existing models and which require additional data or parameter adjustment. Third, multi-task sharing should investigate the relationships among task combinations, shared representations, and competing training objectives to determine when joint processing improves or degrades individual tasks. Fourth, deployment on base stations and edge devices requires joint consideration of prediction accuracy, parameter storage, adaptation overhead, and processing latency. These directions are closely related to the continuing channel updates, beam management, localization, and coordinated transmission required by moving and cooperating embodied agents. Further progress requires evaluation under representative propagation changes and hardware constraints, together with transparent reporting of training conditions and computational costs. This review provides a reference for constructing, comparing, and evaluating wireless physical-layer models within this application context.
- Wireless physical layer,
- Foundation models,
- Multi-task learning,
- Large language models,
- Embodied intelligence

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References(46)

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