Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model | Paper Citation Record

Observation 82052167-d8a9-4f15-b82e-f72864716aaa · outbound

This paper cites AI for 5G : research directions and paradigms.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model AI for 5G : research directions and paradigms

Reference 1

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Observation b0e4e01b-53c3-4e4f-8f21-a0e031635708 · outbound

This paper cites Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.604052Z

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Observation fb15e8d5-b1d5-4bf0-b5b8-aa9c5209db24 · outbound

This paper cites Energy efficient semantic communication over wireless networks with rate splitting.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Energy efficient semantic communication over wireless networks with rate splitting

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.589290Z

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Observation 8d53c9a1-4b46-4805-9cc8-6c74a4ac39a0 · outbound

This paper cites The roadmap to 6G: AI empowered wireless networks.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model The roadmap to 6G: AI empowered wireless networks

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.575333Z

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Observation 08e09f71-be9a-4df9-8060-a25943241325 · outbound

This paper cites When AI meets sustainable 6G.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model When AI meets sustainable 6G

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.560408Z

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Observation 273be4c7-51e6-4020-ba27-50277f708875 · outbound

This paper cites Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.548001Z

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Observation 63d86c70-f686-4b0b-90cd-698d9484dad0 · outbound

This paper cites Viewing channel as sequence rather than image: A 2-D Seq2Seq approach for efficient MIMO-OFDM CSI feedback.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Viewing channel as sequence rather than image: A 2-D Seq2Seq approach for efficient MIMO-OFDM CSI feedback

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.534726Z

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Observation 6c95dea5-09e8-4eb8-96da-5e9af98ea206 · outbound

This paper cites C-GRBFnet: A physics-inspired generative deep neural network for channel representation and prediction.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model C-GRBFnet: A physics-inspired generative deep neural network for channel representation and prediction

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.520573Z

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Observation 56f04964-cec9-4f3a-8b68-b0ba13f4291f · outbound

This paper cites From data-driven learning to physics-inspired inferring: A novel mobile MIMO channel prediction scheme based on neural ODE.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model From data-driven learning to physics-inspired inferring: A novel mobile MIMO channel prediction scheme based on neural ODE

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.507096Z

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Observation c9c8360b-4464-4716-a4d6-1899d69a54f6 · outbound

This paper cites Deep learning-based multi-user positioning in wireless FDMA cellular networks.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Deep learning-based multi-user positioning in wireless FDMA cellular networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.494059Z

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Observation 20b214c0-1d39-466e-b207-a0479ef29eb1 · outbound

This paper cites Channel mapping based on interleaved learning with complex-domain MLP-Mixer.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Channel mapping based on interleaved learning with complex-domain MLP-Mixer

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.478564Z

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Observation 5035f94c-c753-4bd0-b487-0e4667e5f307 · outbound

This paper cites Deep CSI compression for dual-polarized massive MIMO channels with disentangled representation learning.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Deep CSI compression for dual-polarized massive MIMO channels with disentangled representation learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.463157Z

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Observation e76f3803-c705-4c8f-be58-7a474b934fef · outbound

This paper cites Channel Deduction: A New Learning Framework to Acquire Channel from Outdated Samples and Coarse Estimate.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Channel Deduction: A New Learning Framework to Acquire Channel from Outdated Samples and Coarse Estimate

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:25:32.130214Z

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Observation 00084c37-67a2-4968-ba46-31218882c788 · outbound

This paper cites Deep learning-based CSI feedback for beamforming in single- and multi-cell massive MIMO systems.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Deep learning-based CSI feedback for beamforming in single- and multi-cell massive MIMO systems

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.449095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5b6926e7-7d2d-4d62-b6ad-317318df79d5 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model On the Opportunities and Risks of Foundation Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:31.411232Z

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Observation a72d15b6-fd72-471e-a51d-cf82ec857e32 · outbound

This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 16

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Observation 85a6f3b5-85df-4b15-bc32-5338dd303f98 · outbound

This paper cites DeepSeek-V3 Technical Report.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model DeepSeek-V3 Technical Report

Reference 17

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Observation d25dd362-bfdf-4a00-9608-e3d0df95a709 · outbound

This paper cites Qwen2.5 Technical Report.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Qwen2.5 Technical Report

Reference 18

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Observation fdf51eaf-ad7c-4112-9a25-f07ea98e997f · outbound

This paper cites Big AI models for 6G wireless networks: Opportunities, challenges, and research directions.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Big AI models for 6G wireless networks: Opportunities, challenges, and research directions

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.434361Z

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Observation 8f265b41-c0ec-4c1f-b50c-f6d3d9aa2c53 · outbound

This paper cites Observations on LLMs for telecom domain: Capabilities and limitations.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Observations on LLMs for telecom domain: Capabilities and limitations

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.420459Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6e7f2f3b-cb12-45d6-9714-c690c11df66f · outbound

This paper cites Large generative AI models for telecom: The next big thing? IEEE Commun Mag, 2024, 62: 84-90.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Large generative AI models for telecom: The next big thing? IEEE Commun Mag, 2024, 62: 84-90

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.407200Z

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Observation f861da82-f331-4506-95b8-b2a980ab0150 · outbound

This paper cites Understanding telecom language through large language models.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Understanding telecom language through large language models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.393377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d94bbc59-cde1-4d86-a8e3-f8b0e47c587d · outbound

This paper cites Linguistic Intelligence in Large Language Models for Telecommunications.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Linguistic Intelligence in Large Language Models for Telecommunications

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:31.447234Z

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Observation a551911b-7d58-4af0-9178-2ecaf9f9e7e4 · outbound

This paper cites TKG : Telecom knowledge governance framework for LLM application, 2023.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model TKG : Telecom knowledge governance framework for LLM application, 2023

Reference 24

Resolution: verified exact; doi, observed 2026-08-11T17:25:31.677682Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8fb9111f-7ae9-43b3-81bc-9eb472a0b27c · outbound

This paper cites A Primer on Generative AI for Telecom: From Theory to Practice.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model A Primer on Generative AI for Telecom: From Theory to Practice

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:31.456260Z

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Observation 2689b5d4-d350-45c7-b4f9-b52f490a2f47 · outbound

This paper cites Large language models for telecom: Forthcoming impact on the industry.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Large language models for telecom: Forthcoming impact on the industry

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.375119Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d9fb2ed6-6d0c-4cd7-a35a-7547994108d3 · outbound

This paper cites Using large language models to understand telecom standards.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Using large language models to understand telecom standards

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.357713Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b55f9d2c-3dd7-496b-b521-e9eeb8f1a4ff · outbound

This paper cites Large Language Model (LLM)-enabled In-context Learning for Wireless Network Optimization: A Case Study of Power Control.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Large Language Model (LLM)-enabled In-context Learning for Wireless Network Optimization: A Case Study of Power Control

Reference 28

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Observation 87fdd4ac-0dc1-4644-8f3b-3ab6e57ea355 · outbound

This paper cites Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:31.477904Z

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Observation 23200109-66cd-4615-a959-8b8754b60899 · outbound

This paper cites LLM-Empowered Resource Allocation in Wireless Communications Systems.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model LLM-Empowered Resource Allocation in Wireless Communications Systems

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:31.483935Z

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Observation 4bfbcfde-d284-46e6-ae96-e2e685333831 · outbound

This paper cites Leveraging Large Language Models for Wireless Symbol Detection via In-Context Learning.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Leveraging Large Language Models for Wireless Symbol Detection via In-Context Learning

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:31.488588Z

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Observation 92580d4f-cda8-47b3-acb0-6fc79dd96cde · outbound

This paper cites WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:31.493352Z

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Observation 3b956802-fd97-4b40-8566-b89382554818 · outbound

This paper cites Mobile-LLaMA: Instruction fine-tuning open-source LLM for network analysis in 5G networks.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Mobile-LLaMA: Instruction fine-tuning open-source LLM for network analysis in 5G networks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.342140Z

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Observation f37cdc4c-1b37-48dd-879e-36b12fdd2178 · outbound

This paper cites TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:31.503416Z

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Observation ecccb954-ce65-4816-9f16-d935413760f6 · outbound

This paper cites Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:31.509567Z

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Observation 6b786af5-3021-4ee8-9d81-eef15dac0af2 · outbound

This paper cites Large Language Models (LLMs) Assisted Wireless Network Deployment in Urban Settings.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Large Language Models (LLMs) Assisted Wireless Network Deployment in Urban Settings

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:31.514473Z

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Observation 6a3d2652-f96b-43c5-8b41-016ec53c415c · outbound

This paper cites Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunications.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunications

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:31.518514Z

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Observation 880d92b7-b813-48d2-b204-156f73af02d8 · outbound

This paper cites TelecomRAG: Taming Telecom Standards with Retrieval Augmented Generation and LLMs.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model TelecomRAG: Taming Telecom Standards with Retrieval Augmented Generation and LLMs

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:31.523265Z

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Observation dacf4d77-9eae-4d5a-81da-b76ef3b012e7 · outbound

This paper cites Telecom Language Models: Must They Be Large?.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Telecom Language Models: Must They Be Large?

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:31.528994Z

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Observation 0acabb25-c527-4efc-a328-d4cb2ec5cd71 · outbound

This paper cites Unlocking telecom domain knowledge using LLMs.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Unlocking telecom domain knowledge using LLMs

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.324194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 89f8f73f-fbeb-4da5-942b-f9c750319afa · outbound

This paper cites Design of a large language model for improving customer service in telecom operators.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Design of a large language model for improving customer service in telecom operators

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.310002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a0aeaa78-d948-47e3-8674-08406f942d59 · outbound

This paper cites SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:31.544085Z

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Observation 6cb22a39-b901-4827-a060-bfc40183142a · outbound

This paper cites TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:31.548573Z

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Observation 005c9a75-f7a5-4873-8d62-08cbeee5e6c1 · outbound

This paper cites TSpec-LLM: An Open-source Dataset for LLM Understanding of 3GPP Specifications.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model TSpec-LLM: An Open-source Dataset for LLM Understanding of 3GPP Specifications

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:31.552850Z

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Observation d557390c-27e1-489b-987e-58e07a01ad99 · outbound

This paper cites Tele-LLMs: A Series of Specialized Large Language Models for Telecommunications.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Tele-LLMs: A Series of Specialized Large Language Models for Telecommunications

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:31.557309Z

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Observation 4dbe698f-76b6-4570-b60b-67ad92f5aebb · outbound

This paper cites WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:31.561728Z

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Observation c0949748-80e0-4dbc-9f14-873e07d0040e · outbound

This paper cites LLM Agents as 6G Orchestrator: A Paradigm for Task-Oriented Physical-Layer Automation.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model LLM Agents as 6G Orchestrator: A Paradigm for Task-Oriented Physical-Layer Automation

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:31.566622Z

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Observation 7288df92-4d3a-4635-ad31-4ab1e2bcda0a · outbound

This paper cites LLMind: Orchestrating AI and IoT with LLM for complex task execution.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model LLMind: Orchestrating AI and IoT with LLM for complex task execution

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.297464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8c4ffbca-8b66-4216-8bd3-33b5067fa949 · outbound

This paper cites When large language model agents meet 6G networks: Perception, grounding, and alignment.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model When large language model agents meet 6G networks: Perception, grounding, and alignment

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.284410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cf869e3d-0c61-4221-b577-eed86e8be307 · outbound

This paper cites Large language model enhanced multi-agent systems for 6G communications.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Large language model enhanced multi-agent systems for 6G communications

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.268764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6efc4cd5-e2e0-4cca-8250-d40c142fb983 · outbound

This paper cites Deep learning for massive MIMO CSI feedback.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Deep learning for massive MIMO CSI feedback

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.254186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f22c09d2-c5be-4461-9899-6aa7ee530b48 · outbound

This paper cites Fingerprint-based localization for massive MIMO-OFDM system With deep convolutional neural networks.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Fingerprint-based localization for massive MIMO-OFDM system With deep convolutional neural networks

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.239646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 83ac20ce-615c-48ab-8ee8-612a626cca9b · outbound

This paper cites Federated learning with unsourced random access.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Federated learning with unsourced random access

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.224865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b1a18f88-734f-4761-83fd-281c5d874a07 · outbound

This paper cites CSI-GPT: Integrating Generative Pre-Trained Transformer with Federated-Tuning to Acquire Downlink Massive MIMO Channels.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model CSI-GPT: Integrating Generative Pre-Trained Transformer with Federated-Tuning to Acquire Downlink Massive MIMO Channels

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:31.600144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be4aa709-aed7-48bd-b21c-ff372201e73e · outbound

This paper cites Csi-LLM: A Novel Downlink Channel Prediction Method Aligned with LLM Pre-Training.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Csi-LLM: A Novel Downlink Channel Prediction Method Aligned with LLM Pre-Training

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:31.604904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5af658a-c190-486b-9eaf-a99e802df7a8 · outbound

This paper cites LLM4CP: Adapting Large Language Models for Channel Prediction.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model LLM4CP: Adapting Large Language Models for Channel Prediction

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T17:25:31.609621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32d22cb0-7a5c-4a73-820e-02cf1c991c98 · outbound

This paper cites Assessing air-interface dataset similarity and diversity for AI-enabled wireless communications.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Assessing air-interface dataset similarity and diversity for AI-enabled wireless communications

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.210226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 53e3ecff-44b9-40c6-86a5-8a8ed3dbef60 · outbound

This paper cites VBIM-Net: Variational Born Iterative Network for Inverse Scattering Problems.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model VBIM-Net: Variational Born Iterative Network for Inverse Scattering Problems

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.195928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 00f11924-4730-4af2-976f-1bfd662d1a5e · outbound

This paper cites Meta-Material Sensor-Based Internet of Things for Environmental Monitoring by Deep Learning: Design, Deployment, and Implementation.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Meta-Material Sensor-Based Internet of Things for Environmental Monitoring by Deep Learning: Design, Deployment, and Implementation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.181652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e40c9e3d-7d7d-40d7-9714-80980aca38d1 · outbound

This paper cites Stepsize-Adaptive SAMP Algorithm for Fast mmWave Radar Imaging.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Stepsize-Adaptive SAMP Algorithm for Fast mmWave Radar Imaging

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.164034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 138beba4-4554-4d77-a913-d7811509add9 · outbound

This paper cites Wireless Federated Learning Over Resource-Constrained Networks: Digital Versus Analog Transmissions.

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model Wireless Federated Learning Over Resource-Constrained Networks: Digital Versus Analog Transmissions

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:25:32.147421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers