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Paper Citation Record · LEDGER

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks

As of 10 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:2507.14155.

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

pith.paper-citation-record.v1
2507.14155 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:14:07.077003Z

measured 57 of 57 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

57 of 57 outbound references displayed

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  • verified fuzzy47
  • unresolved9
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  • malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b9c99daa-f368-4453-a992-b0d43f019b1b · outbound

This paper cites A Secure and Resilient 6G Architecture Vision of the German Flagship Project 6G-ANNA,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks A Secure and Resilient 6G Architecture Vision of the German Flagship Project 6G-ANNA,

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation eea616ba-7ed5-4d6b-93d3-c4e016186c86 · outbound

This paper cites Framework and overall objectives of the future d evelopment of IMT for 2030 and beyond,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Framework and overall objectives of the future d evelopment of IMT for 2030 and beyond,

Reference 2

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raw_fallback, observed 2026-08-06T20:14:07.970349Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 51b8b8ba-63a8-4d72-be6b-becbe59e3133 · outbound

This paper cites Multi-Agent Reinforcement Learning for Dynamic R esource Management in 6G in-X Subnetworks,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Multi-Agent Reinforcement Learning for Dynamic R esource Management in 6G in-X Subnetworks,

Reference 3

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 13de00d0-307a-4d42-be65-652c09850132 · outbound

This paper cites Extreme Communication in 6G: Vision and Challenges for ‘in -X’ Subnetworks,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Extreme Communication in 6G: Vision and Challenges for ‘in -X’ Subnetworks,

Reference 4

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raw_fallback, observed 2026-08-06T20:14:07.942139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation f89eb1b9-3f1d-4c0d-bbbf-3d658bbe5a6a · outbound

This paper cites Towards 6G in-X subnetworks with sub-mill isecond communication cycles and extreme reliability,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Towards 6G in-X subnetworks with sub-mill isecond communication cycles and extreme reliability,

Reference 5

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raw_fallback, observed 2026-08-06T20:14:07.927292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.838190Z digest=sha256:529603cc41f574d64db056968a284932d2914225a1dd755cfd35a572d4038581

Observation 72d1138c-b3e2-4bdb-8088-f0559e16e2b1 · outbound

This paper cites Interference prediction in wireless networks: Stochastic geometry meet s recursive filtering,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Interference prediction in wireless networks: Stochastic geometry meet s recursive filtering,

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.842738Z digest=sha256:22b6093574bf7171d849742492d04e3853dad69e3c22ebebb509d6a0e2a69ca6

Observation f575a27d-047f-4eb9-b12d-f2ec22f011c2 · outbound

This paper cites Probabilisti c Interference Prediction for Dynamic 6G In-X Sub-Networks,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Probabilisti c Interference Prediction for Dynamic 6G In-X Sub-Networks,

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.847832Z digest=sha256:95c86545864f87cbcf5bed7622ce42db650cb632ceffc5a946424dd844879de1

Observation be6ac10f-e7a2-40ea-b51b-f3ba8754dfb7 · outbound

This paper cites Ex- perimental evidence for heavy tailed interference in the Io T,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Ex- perimental evidence for heavy tailed interference in the Io T,

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.851807Z digest=sha256:1e3c734c50706cb0c9cb382d2e86f535ef12c1e58c9a9ec8f4b498cc0c9c574c

Observation 2ea19ab1-d10c-41b0-b809-d324d4b3e507 · outbound

This paper cites A Nonlinear Autoregressive Neural Network for Interference Prediction and Resource Allocation in URLLC Scenarios,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks A Nonlinear Autoregressive Neural Network for Interference Prediction and Resource Allocation in URLLC Scenarios,

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.855990Z digest=sha256:6183bf5dc71ed80e084fb90c16f7200adaab1f08559cefedf5091a6770cf5e61

Observation 752e29dc-f234-463a-91a3-430653523b7f · outbound

This paper cites Predictive resource allocation for URLLC us ing em- pirical mode decomposition,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Predictive resource allocation for URLLC us ing em- pirical mode decomposition,

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.861285Z digest=sha256:d1f90b171342f6b75887165793df1c012f7e7a4de9f1d4ffb06ac4e8fa79214f

Observation 499bafa3-8c23-487d-a5f1-f52bbed48c0a · outbound

This paper cites Decomposition Based Interference Management Framework for Local 6G Netwo rks,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Decomposition Based Interference Management Framework for Local 6G Netwo rks,

Reference 11

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raw_fallback, observed 2026-08-06T20:14:07.837326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 74d8299e-a2bb-4e7f-8b27-16e658ef7315 · outbound

This paper cites Joint Model and Data-Driven Two-Stage Uplink Interference Predi ction in URLLC Scenarios,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Joint Model and Data-Driven Two-Stage Uplink Interference Predi ction in URLLC Scenarios,

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.870872Z digest=sha256:9378dc16c0e72dcf195bf4e0207013db0fb0a3284cbce5c79e1aada986ac0070

Observation c9cc4dfb-5f20-4896-9f69-b504f26b379a · outbound

This paper cites A Predictive Interference Management Algorithm for URLLC in Beyond 5G Networks,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks A Predictive Interference Management Algorithm for URLLC in Beyond 5G Networks,

Reference 13

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raw_fallback, observed 2026-08-06T20:14:07.807744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.876188Z digest=sha256:609d1d43b24a773c01c5d87844af7860ba75fd6a51b6bb97d022cd1c1719f8fd

Observation ca4725c5-cab0-4221-baf0-99c2b976644b · outbound

This paper cites Interference prediction for low-complexity link adaptat ion in beyond 5G ultra-reliable low-latency communications,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Interference prediction for low-complexity link adaptat ion in beyond 5G ultra-reliable low-latency communications,

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 39c89402-cfb6-403a-9d90-3acaeda2c728 · outbound

This paper cites Mathematical Modelling and Prediction of Interference Power in In-robot Subnetworks,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Mathematical Modelling and Prediction of Interference Power in In-robot Subnetworks,

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.886469Z digest=sha256:012d3f89141cec10dbcef3141526ac63cedf25b00f11d9362851c3f3f4cb20a0

Observation a0907224-2204-43fd-a6e0-3648743387b0 · outbound

This paper cites Deep learning for probabilistic interference predictions in mmwave netw orks,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Deep learning for probabilistic interference predictions in mmwave netw orks,

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation da551ec1-4519-49bb-84a5-b3326b34c250 · outbound

This paper cites Cooperative Interference Estimation Using LSTM-Based Federated Learn ing for In- X Subnetworks,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Cooperative Interference Estimation Using LSTM-Based Federated Learn ing for In- X Subnetworks,

Reference 17

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 96a6d020-bb6d-4ecc-b4f7-19c3295719f3 · outbound

This paper cites Interferenc e Prediction in Unconnected In-X Mobile 6G Subnetworks Using a Data-Driv en Approach,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Interferenc e Prediction in Unconnected In-X Mobile 6G Subnetworks Using a Data-Driv en Approach,

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation ba59760c-49d6-40ee-9766-c67de0c107bd · outbound

This paper cites Ultrareliable and low-latency wireless communication: Tail, risk, and scale,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Ultrareliable and low-latency wireless communication: Tail, risk, and scale,

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:14:06.904850Z digest=sha256:0938785f6298df576b5be62ac4965805120d33687f7eb6f7fe1a3f14d7294918

Observation cb7c0d13-7615-49a3-86f2-b8958a8ace11 · outbound

This paper cites Prediction of Rare Channel Conditions using B ayesian Statistics and Extreme V alue Theory,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Prediction of Rare Channel Conditions using B ayesian Statistics and Extreme V alue Theory,

Reference 20

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.909350Z digest=sha256:0d9e76a8edd81c5132564c7ac6ce3b3086fbfdbf96bb3027e84c4e5d6a6a7254

Observation ba2bb24a-1eed-4de5-a32c-1c1edd4f0c44 · outbound

This paper cites Ultra-High Reliability by Predictive Interference Management Using Extreme Value Theory.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Ultra-High Reliability by Predictive Interference Management Using Extreme Value Theory

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:14:06.913565Z digest=sha256:acacebcd6d6f079d1b04614c63d1f7d7cd02bb6fe55aabc28aa78c37470d5e7c

Observation 4621d303-ea64-4d1e-b0ab-9a8e80a2aba0 · outbound

This paper cites Extreme V alu e Theory- based Predictive Interference Management for 6G Subnetwor ks with Transformer,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Extreme V alu e Theory- based Predictive Interference Management for 6G Subnetwor ks with Transformer,

Reference 22

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.918517Z digest=sha256:8713399ca4d4c946d74415b03a4ebeba56b9cfdcf331212d96462e4b15f4d8de

Observation 7e755819-d2fc-471a-93d4-83f39e01cd0b · outbound

This paper cites A 5G Traffic Model for Industrial Use Cases,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks A 5G Traffic Model for Industrial Use Cases,

Reference 23

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source=pdf_text observed=2026-08-06T20:14:06.922553Z digest=sha256:148348b8e7007b926a48bf11bee3186d0bece4788ff10ef87bfd94677ab49247

Observation bbba71e4-de4b-4451-aa8b-70e41c2dfa05 · outbound

This paper cites Advanc ed frequency resource allocation for industrial wireless control in 6G s ubnetworks,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Advanc ed frequency resource allocation for industrial wireless control in 6G s ubnetworks,

Reference 24

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raw_fallback, observed 2026-08-06T20:14:07.661258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.926616Z digest=sha256:2e56191cb1b7cec9bd14d71a56c6a5cf14bee862adb5b25c93d0f36e38b3e81a

Observation 2d11f161-ce76-443a-ad1d-afbe006f5a15 · outbound

This paper cites Enabling URLLC in 5G NR IIoT networ ks: A full-stack end-to-end analysis,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Enabling URLLC in 5G NR IIoT networ ks: A full-stack end-to-end analysis,

Reference 25

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raw_fallback, observed 2026-08-06T20:14:07.646714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.931028Z digest=sha256:14ffad380aab5d73841d4f31da89893eae52f632c2bb284aa2c91920eac4fd68

Observation c66b3bf3-ed28-4124-8c64-25f74730763c · outbound

This paper cites Distribut ed Scheduling in Multiple Access With Bursty Arrivals Under a Maximum Dela y Constraint,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Distribut ed Scheduling in Multiple Access With Bursty Arrivals Under a Maximum Dela y Constraint,

Reference 26

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raw_fallback, observed 2026-08-06T20:14:07.631569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.935188Z digest=sha256:70ad64d8ab51599b21be67f77eb13a9317770120e8e41e64f99c4bd95c9c83e3

Observation f421ee0a-589b-4ef7-bf0f-e9c062ad34d1 · outbound

This paper cites Service requirements for the 5G system,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Service requirements for the 5G system,

Reference 27

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raw_fallback, observed 2026-08-06T20:14:07.615657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.939376Z digest=sha256:5d126094382cbd5c2ffc3822383764d5707d6d857d933be04e85537e36cf728e

Observation 9eb52695-f76e-4d7d-88a3-88e7433a8165 · outbound

This paper cites Coexistence of Pull and Push Communication in Wireless Access for IoT Devices,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Coexistence of Pull and Push Communication in Wireless Access for IoT Devices,

Reference 28

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raw_fallback, observed 2026-08-06T20:14:07.599419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.943812Z digest=sha256:50fce1a379e028354da778cc3a9b923413dd8de57f8baa4a7478d50dc7a17e6d

Observation 1092683d-03b5-441e-9415-19214fe1c02f · outbound

This paper cites Study on communication for automation in vertic al domains (cav),.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Study on communication for automation in vertic al domains (cav),

Reference 29

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raw_fallback, observed 2026-08-06T20:14:07.582485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.947860Z digest=sha256:b0e3777d4230023a46b40f96be9b68287a6bb477fe4e980edd756ceef0ea5343

Observation 78a263fa-4f1c-4330-b6e8-d0ca5320f836 · outbound

This paper cites Novel sum-of-s inusoids simulation models for Rayleigh and Rician fading channels,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Novel sum-of-s inusoids simulation models for Rayleigh and Rician fading channels,

Reference 30

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raw_fallback, observed 2026-08-06T20:14:07.566180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.952116Z digest=sha256:d6f0c907845996b9e6e49477120b835d7624b6e6d43a6c9d202e66d7ebc238a1

Observation 14d35e69-f514-4239-9dfe-4f68385f928e · outbound

This paper cites 5G: Study on channel model for frequencies from 0 .5 to 100 GHz (3GPP TR 38.901 version 16.1.0 release 16),.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks 5G: Study on channel model for frequencies from 0 .5 to 100 GHz (3GPP TR 38.901 version 16.1.0 release 16),

Reference 31

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raw_fallback, observed 2026-08-06T20:14:07.550812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.956675Z digest=sha256:d03e3e4a2e110986f206a40a124f7fdbd908293ea5c48ddbe60817bd8dda1a53

Observation 1d679eaa-5f8d-4663-b78b-7353a4596f31 · outbound

This paper cites Effects of correlated sh adowing modeling on performance evaluation of wireless sensor netw orks,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Effects of correlated sh adowing modeling on performance evaluation of wireless sensor netw orks,

Reference 32

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raw_fallback, observed 2026-08-06T20:14:07.534932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.961348Z digest=sha256:619c767fdbc9766661fee77a6323e8c904b44b29165b50fbbe51309a17692c8a

Observation e484df0e-87a5-4884-a374-00aa4326d004 · outbound

This paper cites Inter- ference data collection with beam information for ml-based interference prediction,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Inter- ference data collection with beam information for ml-based interference prediction,

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:14:07.520215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.965582Z digest=sha256:29a0baca2aba9f87f106cd6147b169e7e7a7befacbeb3817271da914754149fa

Observation 47c877b2-9303-4140-9d3e-8e486c7bf470 · outbound

This paper cites Channel coding rate in the finite blocklength regime,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Channel coding rate in the finite blocklength regime,

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-06T20:14:06.970039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:14:06.970039Z digest=sha256:cf81e27e9a9947b35cf92c6986729d01033388f1bc050e65521a75d652a9a4d3

Observation 8d0a22cd-c652-4298-9d61-73e3afb875c5 · outbound

This paper cites Improving QoS by predictive channel quality feedback for LTE,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Improving QoS by predictive channel quality feedback for LTE,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:14:07.493876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.974309Z digest=sha256:5a2be656ea74f163dad9ecc126e1478c3f460cbf0bfad5f018a71dace64546df

Observation acd4c513-aae0-41f1-bab3-a49d0616b59f · outbound

This paper cites Correlat ion matrix distance, a meaningful measure for evaluation of non-stati onary MIMO channels,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Correlat ion matrix distance, a meaningful measure for evaluation of non-stati onary MIMO channels,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:14:07.475892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.978935Z digest=sha256:bf28d8443e4dbe1673916027cc106137a63d1331e5691d8342f5071965741960

Observation 81e0c9f2-36d4-4063-ae0c-8a1ddabc622e · outbound

This paper cites E mpirical channel stationarity in urban environments,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks E mpirical channel stationarity in urban environments,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:14:07.461089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.983574Z digest=sha256:3b47a9c8bc14297291c42083b2f922929e0d2294b2212f3fa756e4e880dacac9

Observation 851d03a8-9c73-46e9-9855-6a2e942477e0 · outbound

This paper cites Distances and Riemannian metrics for s pectral density functions,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Distances and Riemannian metrics for s pectral density functions,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:14:07.446131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.988040Z digest=sha256:9212781dce7d5b3b0ffb40a386777864d8334acb877874d4db406839a665c520

Observation a187bf97-b216-42eb-abd8-f8d900e2d0cf · outbound

This paper cites Prob abilistic individual load forecasting using pinball loss guided LSTM ,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Prob abilistic individual load forecasting using pinball loss guided LSTM ,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:14:07.430355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:06.992686Z digest=sha256:31a3332e17f8857bc764fc8d3b6d909d9e37d01089d3222dc927ef21253d62fe

Observation 0e75856e-a7f7-4f68-a453-39fa27fec327 · outbound

This paper cites Conformalized quantile regression,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Conformalized quantile regression,

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-06T20:14:06.997523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:14:06.997523Z digest=sha256:ceec2abb835259ccf9413d576f2cbf539f79301e6f08ed9d7a8eaf21e2c70e93

Observation a9055c3a-c080-48dc-81cf-dd6e40402f17 · outbound

This paper cites V ovk, A.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks V ovk, A

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:14:07.404111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:07.001917Z digest=sha256:8f099a49b342c411bbb574651e047e13357f2efe36b8c6c728df07c4894a198c

Observation cc5f3594-4ec0-4cf3-870b-659e5227dc81 · outbound

This paper cites Conformal prediction for time series,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Conformal prediction for time series,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:14:07.389894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:07.006878Z digest=sha256:f9a234b776669b90c50448281aeec623e94d8942b4f108a8ebd59c331a5e0833

Observation cd015246-1b06-48ac-8c86-2061c6230cf3 · outbound

This paper cites Conf ormal time- series forecasting,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Conf ormal time- series forecasting,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:14:07.373614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:07.011387Z digest=sha256:a6ba15331b834ae48ccf462d8736418e0373191202a6173c37204f655de83178

Observation 02e5996e-693e-43b9-abf5-71a1a0268a50 · outbound

This paper cites A Tutorial on Conformal Predicti on.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks A Tutorial on Conformal Predicti on

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:14:07.358022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:07.015915Z digest=sha256:0e4bf8754898d735bd098fa8f49fa9467e169160abf422e52254c6903fa35f70

Observation 9ae939fc-f2ef-4915-9314-0be584f1f2ef · outbound

This paper cites Conformal prediction interval est imation and applications to day-ahead and intraday power markets,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Conformal prediction interval est imation and applications to day-ahead and intraday power markets,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:14:07.342693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:07.020187Z digest=sha256:356a6050150e6299ad3cec13f130aa14ea9b7590601b2a7c9bd896d5c7ad7360

Observation 8116c419-d560-4c44-861c-f7932112e222 · outbound

This paper cites Inductive confidence machines for regression,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Inductive confidence machines for regression,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:14:07.326941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:07.024832Z digest=sha256:f53d84aeb126500a3f8b6399f0c32576e313d14a70e68ec1e93f64dd0f3291b3

Observation 4e3d74ec-eebd-455a-afe9-0c29d9ea489b · outbound

This paper cites Ensemble Co nformalized Quantile Regression for Probabilistic Time Series Forecas ting,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Ensemble Co nformalized Quantile Regression for Probabilistic Time Series Forecas ting,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:14:07.309742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:07.030098Z digest=sha256:d82a256a2a153c5f66663bfe12016dad78e8d2daf800f3781552f5e1c8a0d0b2

Observation 7abd2636-f100-4486-bb21-f88edcc2d824 · outbound

This paper cites iTransformer: Inverted Transformers Are Effective for Time Series Forecasting.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-06T20:14:07.034185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:14:07.034185Z digest=sha256:f973696c5948e838401c23d03fc03ea240fabb718fb01e6d129d00d25a0ef5ec

Observation c0f8f272-aa2b-4adb-9aba-634c578b7484 · outbound

This paper cites A T ime Series is Worth 64 Words: Long-term Forecasting with Transf ormers,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks A T ime Series is Worth 64 Words: Long-term Forecasting with Transf ormers,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:14:07.293748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:07.038962Z digest=sha256:f45205d5235a59613441d35f11808265a83048c5c2640c97f814a654504dcdea

Observation 4a64dd70-c9cd-4b0c-b2a9-8997b078d66d · outbound

This paper cites Attention is All you Need,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Attention is All you Need,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:14:07.275836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:07.043421Z digest=sha256:b9c43c970b8a2f53b9edfcdac9eb0e759033b44f7794622b3c9f4edb8d9775e5

Observation 22149d6e-687b-4e0a-b294-05496b9113b5 · outbound

This paper cites R eversible instance normalization for accurate time-series forecast ing against distri- bution shift,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks R eversible instance normalization for accurate time-series forecast ing against distri- bution shift,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:14:07.261095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:07.048154Z digest=sha256:53164c00fbfa73d2e298eb308027a8cb3e46a4bfde006bf699a01bc5731c343c

Observation 6024a4e3-6e90-4eb2-96cc-9411d2a0e54a · outbound

This paper cites an unresolved cited work.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Unresolved cited work

Reference 52

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:14:07.245530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:07.053187Z digest=sha256:395536f4280fdede93e7a34e46693f6a9e15b97600f78903cde2ace638b45231

Observation 45d10c73-396a-4666-90a8-0214d2369a88 · outbound

This paper cites Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-06T20:14:07.058393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:14:07.058393Z digest=sha256:1b0fc44808a1e5859cb18107dbfef7c374f3371e839cadba64f0e3662f15745b

Observation 105d82ec-51e0-436f-a26f-acfc392ab87a · outbound

This paper cites Haan and A.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Haan and A

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:14:07.228522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:07.063288Z digest=sha256:d1942637373b4a37d370aa98a6d2efaff8c91be87656958a48ad7dd9ea1f9018

Observation d073b0d5-d2b2-4362-bec2-c7a7605d07ec · outbound

This paper cites Efficient parallel split learning over resource-constrai ned wireless edge networks,.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Efficient parallel split learning over resource-constrai ned wireless edge networks,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:14:07.209838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:14:07.067835Z digest=sha256:51025b058187af53828f55c46eece495a0b443c3602b4ae38665db6e5d23b804

Observation 0243994d-8025-4088-bf6b-bdd1d2d0265c · outbound

This paper cites A Survey on Activation Functions and their relation with Xavier and He Normal Initialization.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks A Survey on Activation Functions and their relation with Xavier and He Normal Initialization

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-06T20:14:07.072299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:14:07.072299Z digest=sha256:9601f2087103cdfd9fc9ce0b6e442b28cb57c543797a664d6389ab2060add619

Observation 800e026c-c1d6-47d9-a807-f3941340edbe · outbound

This paper cites an unresolved cited work.

Extreme Value Theory-based Distributed Interference Prediction for 6G Industrial Sub-networks Unresolved cited work

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-06T20:14:07.077003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:14:07.077003Z digest=sha256:9f80719a66d8b5f14ab925815b295b6188be5632028e0904f1b1d813778acd56

Pith citing papers

No inbound Pith citation observations are available.