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Paper Citation Record · LEDGER

Adversarial Water-Filling: Theory, Algorithms and Foundation Model

As of 21 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2605.26163.

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

pith.paper-citation-record.v1
2605.26163 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T23:34:08.173177Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

  • verified exact7
  • verified fuzzy0
  • unresolved37
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e6fd323e-0a2d-4f54-a96a-c0601727899c · outbound

This paper cites On the road to 6G: Visions, requirements, key technologies, and testbeds,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model On the road to 6G: Visions, requirements, key technologies, and testbeds,

Reference 1

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Observation 3853a9da-8acd-490c-8d32-82f7fe5cd4e7 · outbound

This paper cites Dynamic task offloading and resource allocation for ultra-reliable low-latency edge computing,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Dynamic task offloading and resource allocation for ultra-reliable low-latency edge computing,

Reference 2

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source=pdf_text observed=2026-06-29T23:34:08.173177Z digest=sha256:49d8723b62c47ab3d16b257cbd7774396ffe51235cc8b230b6effed983bfeddd

Observation e9f534d8-c5ec-4c26-8762-736a43f0de7d · outbound

This paper cites Adversarial machine learning threat analysis and remediation in open radio access network (O-RAN),.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Adversarial machine learning threat analysis and remediation in open radio access network (O-RAN),

Reference 3

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source=pdf_text observed=2026-06-29T23:34:08.173177Z digest=sha256:2761c120b80d4077ea35414e238d46a32a9285cf24ef026f22c1c8c8605921ec

Observation a21ab92c-0922-4bd6-a02c-1c9b4a7905dd · outbound

This paper cites Adversarial Machine Learning Threat Analysis and Remediation in Open Radio Access Network (O-RAN).

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Adversarial Machine Learning Threat Analysis and Remediation in Open Radio Access Network (O-RAN)

Reference 4

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arxiv_id, observed 2026-06-29T23:44:03.274790Z

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Observation f8a8c657-d2c2-4ed4-88df-6cba4c4437fa · outbound

This paper cites ColO- RAN: Developing machine learning-based xApps for open RAN closed- loop control on programmable experimental platforms,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model ColO- RAN: Developing machine learning-based xApps for open RAN closed- loop control on programmable experimental platforms,

Reference 5

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Observation b2591fce-ea15-440b-87fd-3821d88dda0b · outbound

This paper cites OrchestRAN: Network automation through orchestrated intelligence in the open RAN,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model OrchestRAN: Network automation through orchestrated intelligence in the open RAN,

Reference 6

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Observation f55016c8-7fb7-4206-a1ea-ab7b33c1d18f · outbound

This paper cites OpenRANet: Neuralized Spectrum Access by Joint Subcarrier and Power Allocation with Optimization-based Deep Learning.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model OpenRANet: Neuralized Spectrum Access by Joint Subcarrier and Power Allocation with Optimization-based Deep Learning

Reference 7

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Observation 621b1dc3-0507-40c0-8bf7-989ea490026f · outbound

This paper cites Space exploration holdings, LLC request for deployment and operating authority for the spaceX Gen2 NGSO satellite system,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Space exploration holdings, LLC request for deployment and operating authority for the spaceX Gen2 NGSO satellite system,

Reference 8

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Observation e3dfcc05-d6ee-47dd-b25c-af6c277fdc4a · outbound

This paper cites Coded Water-Filling for Multi-User Interference Cancellation.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Coded Water-Filling for Multi-User Interference Cancellation

Reference 9

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source=pdf_text observed=2026-06-29T23:34:08.173177Z digest=sha256:fa9dd818f1e739be5afce496ca17d5de52180b066b3ed5b7568545c0c36c6cae

Observation 440dbed6-241e-485a-86e6-35b2b62c3ab6 · outbound

This paper cites A survey on nongeostationary satellite systems: The communication perspective,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model A survey on nongeostationary satellite systems: The communication perspective,

Reference 10

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Observation 785932dd-2f1c-482a-b268-414f22ba40dc · outbound

This paper cites Satellite-5G integration: A network perspective,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Satellite-5G integration: A network perspective,

Reference 11

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Observation f9629557-a12f-49a0-85db-5a45431280c7 · outbound

This paper cites Exploring the.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Exploring the

Reference 12

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Observation ef916985-7228-4b00-a305-01111299d9a7 · outbound

This paper cites An O-RAN approach to spectrum sharing between commercial 5G and government satellite systems,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model An O-RAN approach to spectrum sharing between commercial 5G and government satellite systems,

Reference 13

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source=pdf_text observed=2026-06-29T23:34:08.173177Z digest=sha256:6847374a7f11e237a004f96b289f17bc0a36e80e1e1d7303d34b8b3fc97ca7d8

Observation d0e01abe-1fb0-4796-8c10-db1d4c04ca62 · outbound

This paper cites an unresolved cited work.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Unresolved cited work

Reference 14

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Observation 6a4e2430-4e3a-4bd2-b747-408b4bf91334 · outbound

This paper cites Optimum power allocation for parallel Gaussian channels with arbitrary input distributions,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Optimum power allocation for parallel Gaussian channels with arbitrary input distributions,

Reference 15

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Observation 58d602a6-faf6-407a-a841-532b2ff29d05 · outbound

This paper cites Practical algorithms for a family of waterfilling solutions,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Practical algorithms for a family of waterfilling solutions,

Reference 16

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source=pdf_text observed=2026-06-29T23:34:08.173177Z digest=sha256:8e3440e010bbe591f3656300350b9e52bf0464b70e417643698b035095e64710

Observation 306667aa-87e3-4fa8-8231-1f3aa211aad8 · outbound

This paper cites New viewpoint and algorithms for water-filling solutions in wireless communications,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model New viewpoint and algorithms for water-filling solutions in wireless communications,

Reference 17

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source=pdf_text observed=2026-06-29T23:34:08.173177Z digest=sha256:4d9a72fb94ce8fb02b12218fc3982e1ee93fecf4c66445fc82355077183da010

Observation f5995887-d121-42f8-a2e0-82a5da20a52d · outbound

This paper cites Wireless max–min utility fairness with general monotonic constraints by Perron–Frobenius theory,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Wireless max–min utility fairness with general monotonic constraints by Perron–Frobenius theory,

Reference 18

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source=pdf_text observed=2026-06-29T23:34:08.173177Z digest=sha256:1f4602596af223d039181f032ebac6b7a9f38b876fbce4ec95b3d2a89a5cc6a4

Observation fa5536b5-82a6-47d0-bd3a-0557acb6086b · outbound

This paper cites A first-order primal-dual algorithm for convex problems with applications to imaging,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model A first-order primal-dual algorithm for convex problems with applications to imaging,

Reference 19

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source=pdf_text observed=2026-06-29T23:34:08.173177Z digest=sha256:d9cb1f53418135be8453de737d0a989c7af558539bc7516e1966e2b9e1fc6eb1

Observation 0afee0ef-61a9-4920-a669-c83c96b10669 · outbound

This paper cites Adaptive Primal-Dual Hybrid Gradient Methods for Saddle-Point Problems.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Adaptive Primal-Dual Hybrid Gradient Methods for Saddle-Point Problems

Reference 20

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source=pdf_text observed=2026-06-29T23:34:08.173177Z digest=sha256:b51568e10281fc78c654e058442b35e7632cb5e95ee0eebb01f233ab22957489

Observation cf327671-5224-4132-86bf-dbcc3b14db4f · outbound

This paper cites Yang and M.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Yang and M

Reference 21

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doi, observed 2026-06-29T23:44:02.675730Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 77ee1a9d-f5ec-42d2-88c5-b53480175a07 · outbound

This paper cites Learning to optimize: Training deep neural networks for interference management,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Learning to optimize: Training deep neural networks for interference management,

Reference 22

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Observation f823225a-1f9b-4f82-bea6-9c5b4fb3b793 · outbound

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

Adversarial Water-Filling: Theory, Algorithms and Foundation Model WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence

Reference 23

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Observation c02783c4-8a36-4439-bd16-38501cebaeef · outbound

This paper cites Adversarial water-filling: Minimax resource allocation optimization with proximal decomposition in open ran,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Adversarial water-filling: Minimax resource allocation optimization with proximal decomposition in open ran,

Reference 24

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Observation c950eb3d-e1d1-495e-8835-20718f0cf3ad · outbound

This paper cites an unresolved cited work.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Unresolved cited work

Reference 25

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source=pdf_text observed=2026-06-29T23:34:08.173177Z digest=sha256:4e3b486098ca3854b5028017e0d3646a9bf57f2eea122b3167d4eda0511b069f

Observation 915c1aef-300e-43a4-ac5b-c0c72f622f2a · outbound

This paper cites Tse and P.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Tse and P

Reference 26

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Observation fda0238d-55ef-4f9c-a7e1-e1cd904666c9 · outbound

This paper cites Iterative water-filling for Gaus- sian vector multiple-access channels,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Iterative water-filling for Gaus- sian vector multiple-access channels,

Reference 27

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Observation a7ec7663-735d-4a5f-9756-521f949f08f1 · outbound

This paper cites Dual methods for nonconvex spectrum optimization of multicarrier systems,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Dual methods for nonconvex spectrum optimization of multicarrier systems,

Reference 28

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Observation ef74ae80-5356-4a47-b3c1-3e6f37e7dc19 · outbound

This paper cites Minimax and convex-concave games,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Minimax and convex-concave games,

Reference 29

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Observation 2038a9db-37dd-4ccd-b59c-92d159615f4a · outbound

This paper cites Max-min resource alloca- tion with application to anti-jamming,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Max-min resource alloca- tion with application to anti-jamming,

Reference 30

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source=pdf_text observed=2026-06-29T23:34:08.173177Z digest=sha256:840d8523608ac0491698264007858f9f0430ed41b52ac816abc2c5290ec85264

Observation a165c3f9-800d-4e80-a49e-98a1900245b5 · outbound

This paper cites An anti-jamming multiple access channel game using latency as metric,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model An anti-jamming multiple access channel game using latency as metric,

Reference 31

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Observation db04c83c-4ddb-4d0e-b9bb-f013554926fa · outbound

This paper cites Learning to continuously optimize wireless resource in a dynamic environment: A bilevel optimization perspective,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Learning to continuously optimize wireless resource in a dynamic environment: A bilevel optimization perspective,

Reference 32

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Observation 2b4d4003-dd54-426e-bdd1-e8f9cc15ce68 · outbound

This paper cites Multi-agent deep reinforcement learning for dynamic power allocation in wireless networks,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Multi-agent deep reinforcement learning for dynamic power allocation in wireless networks,

Reference 33

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Observation ebdac446-6e13-4c4d-a05c-a08ca0a92b81 · outbound

This paper cites Graph neural networks for scalable radio resource management: Architecture design and theoretical analysis,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Graph neural networks for scalable radio resource management: Architecture design and theoretical analysis,

Reference 34

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Observation 8dd44fdf-f7da-4310-b76e-733d67edb09c · outbound

This paper cites Knowledge-driven resource allocation for wireless networks: A WMMSE unrolled graph neural network ap- proach,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Knowledge-driven resource allocation for wireless networks: A WMMSE unrolled graph neural network ap- proach,

Reference 35

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source=pdf_text observed=2026-06-29T23:34:08.173177Z digest=sha256:4b72e1b917b26791334a86b764601754a7be2872913eaeed8ba24a439f53ac73

Observation 634b74e9-8578-4a95-9daf-518f5b49c637 · outbound

This paper cites Deep Sets.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Deep Sets

Reference 36

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 735f985d-aa16-4d8c-91dd-459455b51e7d · outbound

This paper cites Set transformer: A framework for attention-based permutation-invariant neu- ral networks,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Set transformer: A framework for attention-based permutation-invariant neu- ral networks,

Reference 37

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Observation d9e937bf-8d90-4bc2-83fd-46c2e0f71299 · outbound

This paper cites Perceiver: General perception with iterative attention,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Perceiver: General perception with iterative attention,

Reference 38

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Observation d9ebec8f-b736-421a-a447-22555ee9e4d1 · outbound

This paper cites OpenRAN gym: AI/ML development, data collection, and testing for O-RAN on PAWR platforms,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model OpenRAN gym: AI/ML development, data collection, and testing for O-RAN on PAWR platforms,

Reference 39

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source=pdf_text observed=2026-06-29T23:34:08.173177Z digest=sha256:6d43dbcc4a747e1376b5ef9eebf89c802a26e73e923fbac5b81115e516228cdc

Observation d1167a21-a228-4ce2-938b-fb6b71d7018d · outbound

This paper cites Boyd and L.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Boyd and L

Reference 40

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source=pdf_text observed=2026-06-29T23:34:08.173177Z digest=sha256:e6e6f133f5062aae6a262b874bb44bf3267ed53eb33d81d78c233abe7374730f

Observation 77c69a5e-b606-49a6-a3ca-c89ca9f6add2 · outbound

This paper cites Spectrum management in multiuser cognitive wireless networks: Optimality and algorithm,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Spectrum management in multiuser cognitive wireless networks: Optimality and algorithm,

Reference 41

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Observation a25a0c09-6405-4349-a7b8-a9bb4ccf18e8 · outbound

This paper cites Mutual information and minimum mean-square error in Gaussian channels,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Mutual information and minimum mean-square error in Gaussian channels,

Reference 42

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source=pdf_text observed=2026-06-29T23:34:08.173177Z digest=sha256:d811fee481e3880ba21a024ee96d6f9423671dce4521771e451690fc0467b840

Observation d442fbcf-4ed4-4dc5-a32e-6c8cbf7259ac · outbound

This paper cites Proximal algorithms,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Proximal algorithms,

Reference 43

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source=pdf_text observed=2026-06-29T23:34:08.173177Z digest=sha256:11eade5c989ebfbefc097fdb0b0b019704c1716792efcbe31fe07fdbbdf1e5f7

Observation d96b023d-331c-4d51-9039-631c3bd8eda2 · outbound

This paper cites Prox-method with rate of convergence O(1/t) for variational inequalities with Lipschitz continuous monotone operators and smooth convex-concave saddle point problems,.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model Prox-method with rate of convergence O(1/t) for variational inequalities with Lipschitz continuous monotone operators and smooth convex-concave saddle point problems,

Reference 44

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source=pdf_text observed=2026-06-29T23:34:08.173177Z digest=sha256:96a2d878ff8c5c4f17f5233e33d8195b54532f45f89ae276d7fbf18e998bdf3f

Pith citing papers

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