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Paper Citation Record · LEDGER

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing

As of 16 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2412.05556.

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

pith.paper-citation-record.v1
2412.05556 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:38:57.723472Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

21 of 21 outbound references displayed

  • verified exact1
  • verified fuzzy13
  • unresolved7
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f5800ba6-636f-413e-b01b-b65e16914027 · outbound

This paper cites Deep learning for massive mimo csi feedback,.

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing Deep learning for massive mimo csi feedback,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:38:58.043136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T20:38:57.631438Z digest=sha256:48953926bd7606a35359cdff0b4c7095b6a6ae73c9bc05c488275b9a0eae3126

Observation 010ef018-941a-42ec-8464-a17b680b729c · outbound

This paper cites Enabling large intelligent surfaces with compressive sensing and deep learning,.

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing Enabling large intelligent surfaces with compressive sensing and deep learning,

Reference 2

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unresolved
no resolver link, observed 2026-08-11T20:38:57.636273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:38:57.636273Z digest=sha256:ef22a14b0347ec24a8c93b6a99dd0a06b2e3cc09c2e4726cf3d2858601306ffc

Observation ac052bbe-33e1-4887-b10c-61b57b9a793d · outbound

This paper cites Deep learning coordinated beamforming for highly-mobile millimeter wave systems,.

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing Deep learning coordinated beamforming for highly-mobile millimeter wave systems,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:38:58.017632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T20:38:57.640953Z digest=sha256:8af7bb71c466459c59a75c1a89fe6de5677024353e625e9fcdf209478f388d8d

Observation fa65346f-d1e6-46ab-b80b-961b87f4f600 · outbound

This paper cites Deep learning for mmwave beam and blockage prediction using sub-6 ghz channels,.

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing Deep learning for mmwave beam and blockage prediction using sub-6 ghz channels,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:38:58.003581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T20:38:57.645609Z digest=sha256:f8216d7d2782921c813f309cb7a4711db96c3406e1cc29471c975c703f3ef519

Observation fc707938-4da0-438b-95bb-c14574a9efdd · outbound

This paper cites Deep reinforcement learning for 5g networks: Joint beamforming, power control, and inter- ference coordination,.

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing Deep reinforcement learning for 5g networks: Joint beamforming, power control, and inter- ference coordination,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:38:57.988778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T20:38:57.650454Z digest=sha256:c30cb3bd81b99f9a7b3e32c2ce86edb2658682fedf493d41a815301826bc372b

Observation 234864c8-5637-47ea-8549-c6032b62419d · outbound

This paper cites Learnable Wireless Digital Twins: Reconstructing Electromagnetic Field with Neural Representations.

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing Learnable Wireless Digital Twins: Reconstructing Electromagnetic Field with Neural Representations

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-11T20:38:57.654924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:38:57.654924Z digest=sha256:907b25500604078bc8ce0b99b5dca6bf564fc48a2854c7aed71bb7e7f968c30e

Observation 402797e5-f3ec-4c3c-a2a0-0e79b1c96bc2 · outbound

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

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing Large Wireless Model (LWM): A Foundation Model for Wireless Channels

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-11T20:38:57.660660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:38:57.660660Z digest=sha256:4fd3f44baa2e8296d04a3a0934f4ab19909ff5e60ed1e64f0bf652b4bc5b63fd

Observation 29aae2ff-eb9d-4754-8d8e-493b9964f9ad · outbound

This paper cites Millimeter wave channel modeling and cellular capacity evaluation,.

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing Millimeter wave channel modeling and cellular capacity evaluation,

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T20:38:57.665367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:38:57.665367Z digest=sha256:ab93f5e598e5d65f3da6d20d6a14e9f4097f10c307e07bdeda7eb994f56f298f

Observation df364d56-1d28-482c-818c-25b9fab3c679 · outbound

This paper cites Platforms for advanced wireless research (pawr),.

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing Platforms for advanced wireless research (pawr),

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:38:57.966560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T20:38:57.670292Z digest=sha256:b1c9ae5f9d1c8e57df8273c0039f0304a19c7d84cdd11598e95ed3dd462d8695

Observation 02047985-1cb4-412e-9b1e-dc771b04d2d4 · outbound

This paper cites 3GPP TR 38.901 V16.1.0: Study on channel model for frequencies from 0.5 to 100 ghz,.

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing 3GPP TR 38.901 V16.1.0: Study on channel model for frequencies from 0.5 to 100 ghz,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:38:57.952943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T20:38:57.674748Z digest=sha256:86f8e4b9b46b0e4b32c6791def414d2fe74e462e39e3d15e4295041ff507952d

Observation 82e0a005-de6c-4b74-9058-8b1f0cd1c054 · outbound

This paper cites A novel millimeter-wave channel simulator and applications for 5g wireless communications,.

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing A novel millimeter-wave channel simulator and applications for 5g wireless communications,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:38:57.939401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T20:38:57.679036Z digest=sha256:8e430ec3f55d3c14795fff3344ed8d12524013f883a68a11daba0cc199084766

Observation b1055164-e4a4-4b4e-b889-ffe59178f161 · outbound

This paper cites Fraunhofer ise annual report 2023- 2024,.

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing Fraunhofer ise annual report 2023- 2024,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:38:57.924379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T20:38:57.683056Z digest=sha256:a5be46846104edecbd6ce23d7ec76b3fe9d6579e99f748d353dce37092881972

Observation 2cbf418b-6d5f-4ef5-a3ee-8ba88759a126 · outbound

This paper cites Wireless insite ray-tracing software,.

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing Wireless insite ray-tracing software,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:38:57.910831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T20:38:57.687987Z digest=sha256:c78ce7713875acca58a3a8fb145183c5657bea72cb6037f055c9632ba013a52a

Observation d2dee6ad-1307-470e-9f69-9580c11a192a · outbound

This paper cites Sionnart: High-fidelity ray tracing simulator for 6g research,.

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing Sionnart: High-fidelity ray tracing simulator for 6g research,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:38:57.896846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T20:38:57.692094Z digest=sha256:b4f07497b2d155238e58bd39fe197e16cdf130c49f01c27fe465364a7be71cce

Observation 3e03f7b4-01aa-4914-8f02-16bb2667511b · outbound

This paper cites DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications.

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-11T20:38:57.696132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:38:57.696132Z digest=sha256:377a1605195aa95157d2f62b633e39d32739dd0343d6d5111879bdce40a8835a

Observation 745e1aa5-f54f-4cff-9c66-0eeac89a3784 · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-11T20:38:57.701520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:38:57.701520Z digest=sha256:8107700e58db6cbd1c3ac50a854e6b9cb6980f34d2cb0f1ad829e24d879724b6

Observation 7bd55a79-8acc-4046-9bed-2d976ff845dd · outbound

This paper cites Visualizing data using t-sne,.

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing Visualizing data using t-sne,

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-11T20:38:57.705826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:38:57.705826Z digest=sha256:d5d86d9d31e0079c6cc839a387e7d4add5edcafab707d06affb852fa8f5a54c6

Observation 88952acb-99bf-4931-8b3b-c63f744c80d3 · outbound

This paper cites Villani, Optimal Transport: Old and New.

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing Villani, Optimal Transport: Old and New

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:38:57.873284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T20:38:57.710327Z digest=sha256:916102d1a6134727f6a19b547a4d5e67f035ceabf4db505cafd7d1406d95fb7d

Observation 58a40eef-5150-47b8-bd64-ddf5cfb41db1 · outbound

This paper cites Computational optimal transport,.

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing Computational optimal transport,

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:38:57.859703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T20:38:57.714311Z digest=sha256:799fe7314411c33e461c61b687ea6e5368f303ff36faecbe1a53280122dac0bb

Observation 6a7f2cbc-0cef-4ad1-ba93-5b4edc440680 · outbound

This paper cites The earth mover’s distance as a metric for image retrieval,.

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing The earth mover’s distance as a metric for image retrieval,

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:38:57.846257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T20:38:57.718538Z digest=sha256:6989f24e7a087cdd4fb416c3e5a333ab27bee9c060fb0a29f72a2612b79f2b4c

Observation ed062c13-c026-4057-ad37-4520f0ac5f0d · outbound

This paper cites Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis.

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis

Reference 21

Resolution
verified exact
local_arxiv, observed 2026-08-11T20:38:57.770362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-11T20:38:57.723472Z digest=sha256:a68cb9eef7a7e1e7d4058dd54ed245c7c403ac6525ac9ea35c76d2f635aa7a1f

Pith citing papers

No inbound Pith citation observations are available.