Paper Citation Record · LEDGER

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework

As of 19 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2501.09631.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.09631 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:57:25.597026Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:33:54.550952Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-07T05:33:56.624006Z

Reference resolution

46 of 46 outbound references displayed

verified exact2
verified fuzzy23
unresolved21
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3dd7ec37-6a54-47fd-93b0-5ad59a55fdd6 · outbound

This paper cites The road to next- generation multiple access: A 50-year tutorial review,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework The road to next- generation multiple access: A 50-year tutorial review,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:26.223697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8f505ae2-527f-48e4-b331-34fd48678de6 · outbound

This paper cites Age-of-information min- imization in federated learning based networks with non-IID dataset,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Age-of-information min- imization in federated learning based networks with non-IID dataset,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:26.212553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d648f019-1c42-4328-a047-195bce688005 · outbound

This paper cites Age of incorrect information-aware data dissemination for distributed multi-agent sys- tems,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Age of incorrect information-aware data dissemination for distributed multi-agent sys- tems,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:26.200822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 871c3162-e942-4f35-b137-1f93d71ce6ef · outbound

This paper cites Integrating pre-trained lan- guage model with physical layer communications,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Integrating pre-trained lan- guage model with physical layer communications,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:26.190268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4a8e2f6d-ad65-4bc2-9627-39999a4d16bc · outbound

This paper cites Interactive AI with retrieval-augmented generation for next generation networking,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Interactive AI with retrieval-augmented generation for next generation networking,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:26.178893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0b4046ac-2dde-4fb3-ad68-1e4562a1c279 · outbound

This paper cites Large Wireless Model (LWM): A Foundation Model for Wireless Channels.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Large Wireless Model (LWM): A Foundation Model for Wireless Channels

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T19:57:25.438476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bebfa6f6-7352-4cdf-a25e-3fece29bb200 · outbound

This paper cites an unresolved cited work.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-10T19:57:26.167958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 76eea7c0-63b5-4c4a-ba48-9d8aa36ccb03 · outbound

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

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T19:57:25.447126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5a7b23d-07c8-4a1c-9078-17f9e019c7e9 · outbound

This paper cites SPEC5G: A dataset for 5G cellular network protocol analysis,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework SPEC5G: A dataset for 5G cellular network protocol analysis,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:26.156359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 51946b23-95fa-41fe-abac-329cbbdaca38 · outbound

This paper cites LLM4CP: Adapting large language models for channel prediction,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework LLM4CP: Adapting large language models for channel prediction,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:26.144267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aca8795d-f764-4cb4-bf51-5a37a38b7ead · outbound

This paper cites Curriculum learning,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Curriculum learning,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T19:57:25.457951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e799358-54ee-4b5d-912c-5bed253dd3c6 · outbound

This paper cites Understanding dataset difficulty with V-usable information,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Understanding dataset difficulty with V-usable information,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:26.132374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f063aa41-8f33-4129-9c1b-0443731ea2d1 · outbound

This paper cites WDMoE: Wireless Distributed Large Language Models with Mixture of Experts.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework WDMoE: Wireless Distributed Large Language Models with Mixture of Experts

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T19:57:25.465024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a99465f2-0ddf-45c3-b810-7384ab3d2af8 · outbound

This paper cites Generative AI agents with large language model for satellite networks via a mixture of experts transmission,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Generative AI agents with large language model for satellite networks via a mixture of experts transmission,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:26.120512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1a39d06a-1fed-400c-949e-1962531deb49 · outbound

This paper cites FedsLLM: Federated Split Learning for Large Language Models over Communication Networks.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework FedsLLM: Federated Split Learning for Large Language Models over Communication Networks

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-10T19:57:25.790896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dbe248c7-2c22-4f51-846c-102073c7288b · outbound

This paper cites Personalized Wireless Federated Learning for Large Language Models.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Personalized Wireless Federated Learning for Large Language Models

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-10T19:57:25.773855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c2bd37b6-e3e5-4a88-ae83-889582184191 · outbound

This paper cites Large language model-based wireless network design,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Large language model-based wireless network design,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:26.108424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b6d351a8-6316-4ca9-8d7c-fc64d10e786a · outbound

This paper cites Unlocking telecom domain knowledge using llms,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Unlocking telecom domain knowledge using llms,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:26.096042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation eee7a4b8-02fb-484b-9dbd-6688ac3a50f8 · outbound

This paper cites Instruction Tuning with Human Curriculum.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Instruction Tuning with Human Curriculum

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T19:57:25.488528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2196475e-5631-443d-8674-253e28f079ca · outbound

This paper cites Question difficulty estimation in community question answering services,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Question difficulty estimation in community question answering services,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:26.083898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 78313fc6-2439-4adc-8548-0615c9cdb845 · outbound

This paper cites SQuAD: 100,000+ questions for machine comprehension of text,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework SQuAD: 100,000+ questions for machine comprehension of text,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:26.071447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fdf840ae-8a57-47d8-aeb5-2cd733357bca · outbound

This paper cites Item response theory in AI: Analysing machine learning classifiers at the instance level,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Item response theory in AI: Analysing machine learning classifiers at the instance level,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:26.059673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8a5c97b3-4009-416f-801f-af55c9035159 · outbound

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

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T19:57:25.504664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02791e21-8ffb-4293-a36e-eb59f12a0225 · outbound

This paper cites User associa- tion and power allocation for multi-cell non-orthogonal multiple access networks,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework User associa- tion and power allocation for multi-cell non-orthogonal multiple access networks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:26.045841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c7e73afe-15f8-4885-976c-b31f7d0773e6 · outbound

This paper cites [Online].

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework [Online]

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:26.033543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bcdbb71c-6d7f-4aef-ae23-3a45664d9998 · outbound

This paper cites On the resemblance and containment of documents,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework On the resemblance and containment of documents,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T19:57:25.516042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29c9cb50-3692-4024-a85f-6c47c958113a · outbound

This paper cites Cofca: A Step-Wise Counterfactual Multi-hop QA benchmark.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Cofca: A Step-Wise Counterfactual Multi-hop QA benchmark

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T19:57:25.519996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5bdcb2f-26b4-4c0c-b09a-cabb89522ad8 · outbound

This paper cites Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T19:57:25.524091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa63c156-eabe-4af2-afb3-a222989dc9bd · outbound

This paper cites Decoupled weight decay regularization,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Decoupled weight decay regularization,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T19:57:25.528040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da1a5bc5-caec-4589-bf5b-26d611fe5f39 · outbound

This paper cites LoRA: Low-rank adaptation of large language models,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework LoRA: Low-rank adaptation of large language models,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T19:57:25.535633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87bce69a-05a1-4457-a8c4-ee601ed5a6f0 · outbound

This paper cites The Llama 3 Herd of Models.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework The Llama 3 Herd of Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T19:57:25.543710Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca318b44-fcdc-4f1c-a53b-f77758d21135 · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T19:57:25.547436Z

Source-reported events for the cited work

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Observation 7a409e31-a657-4b71-952c-3674a9eadcad · outbound

This paper cites Selecting Large Language Model to Fine-tune via Rectified Scaling Law.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Selecting Large Language Model to Fine-tune via Rectified Scaling Law

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T19:57:25.551926Z

Source-reported events for the cited work

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Observation dc5ba220-aa92-4144-adf8-a9012cb3dcd6 · outbound

This paper cites Scaling laws for downstream task performance of large language models,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Scaling laws for downstream task performance of large language models,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:25.999728Z

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Observation 9095e4d7-89e1-488b-b91b-bf578cefb383 · outbound

This paper cites Model index for researchers,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Model index for researchers,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:25.986666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 18a0811b-ed57-4205-8331-4e4b5bdb2405 · outbound

This paper cites TinyLlama: An Open-Source Small Language Model.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework TinyLlama: An Open-Source Small Language Model

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T19:57:25.562717Z

Source-reported events for the cited work

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Observation c0fca424-2b8f-4c71-a1ad-e0512288cdb4 · outbound

This paper cites Language models scale reliably with over-training and on downstream tasks.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Language models scale reliably with over-training and on downstream tasks

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T19:57:25.566946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20d14d60-5a00-4654-b790-056cee3a4ca3 · outbound

This paper cites BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-10T19:57:25.571043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82706de2-3370-4579-8411-a6e04a0c857e · outbound

This paper cites Fine-tuning large neural language models for biomedical natural language processing,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Fine-tuning large neural language models for biomedical natural language processing,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:25.974742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 577f8e89-c9a5-43d0-9f8b-f037e3ab07bb · outbound

This paper cites Unsupervised machine learning-based user clustering in thz-noma systems,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Unsupervised machine learning-based user clustering in thz-noma systems,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:25.962656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f216787e-ba1b-45b1-9fff-60f4fe3f4445 · outbound

This paper cites Unsupervised machine learning-based user clustering in millimeter-wave-noma systems,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Unsupervised machine learning-based user clustering in millimeter-wave-noma systems,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:25.949386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f68c95b5-efc8-4749-af7e-4052fad82e98 · outbound

This paper cites Large language model based multi- agents: A survey of progress and challenges,.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Large language model based multi- agents: A survey of progress and challenges,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:25.937397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9548fdec-f386-42a9-805b-b1570fdffa61 · outbound

This paper cites LLM Multi-Agent Systems: Challenges and Open Problems.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework LLM Multi-Agent Systems: Challenges and Open Problems

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-10T19:57:25.591820Z

Source-reported events for the cited work

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Observation bd8a6580-bba5-4ecf-a16c-7c4859c3c8ae · outbound

This paper cites Decoupled Weight Decay Regularization.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Decoupled Weight Decay Regularization

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-10T19:57:25.531721Z

Source-reported events for the cited work

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Observation 7b665135-ef17-436e-b859-ee7b180aee3f · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework LoRA: Low-Rank Adaptation of Large Language Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-10T19:57:25.539729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2257402-353e-4be4-a48b-a3d05b3d7923 · outbound

This paper cites Available: https://api.semanticscholar.org/CorpusID: 267499950.

Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework Available: https://api.semanticscholar.org/CorpusID: 267499950

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:57:25.925021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 7d9547f8-e754-4804-95cc-2c17d1028f11 · inbound

Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration cites this paper.

Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:33:56.627435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.