Paper Citation Record · LEDGER

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning

As of 10 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2507.12031.

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

pith.paper-citation-record.v1
2507.12031 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:02:05.434541Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

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

30 of 30 outbound references displayed

verified exact2
verified fuzzy28
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 422ee44d-7c09-4da8-9dfb-d4754fa2b09f · outbound

This paper cites Extreme ultra-reliable and low-latency communication,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Extreme ultra-reliable and low-latency communication,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.197377Z

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.

Observation 07ea9c2a-a859-45fd-9b19-3729541c892e · outbound

This paper cites Autonomous interference mapping for industrial Internet of things networks over unlicensed bands: Identifying cross- technology interference,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Autonomous interference mapping for industrial Internet of things networks over unlicensed bands: Identifying cross- technology interference,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.186835Z

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.

Observation 960fc10c-3418-4141-83cd-195d305e565e · outbound

This paper cites Extreme communication in 6G: Vision and challenges for ‘in-X’ subnetworks,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Extreme communication in 6G: Vision and challenges for ‘in-X’ subnetworks,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.175824Z

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.

Observation 220c71ee-f788-424b-8899-60d4183f221e · outbound

This paper cites Popescu and C.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Popescu and C

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.165913Z

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.

Observation 0e92e50f-b2ef-4e2c-aa99-0c0a6f8d46cc · outbound

This paper cites Enhanced interference management for 6G in-X subnetworks,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Enhanced interference management for 6G in-X subnetworks,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.154737Z

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.

Observation 9173e94e-9a2b-4925-ab4d-1d451addf256 · outbound

This paper cites A survey on spectrum management in cognitive radio networks,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning A survey on spectrum management in cognitive radio networks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.143914Z

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.

Observation 2d9a4859-b400-4449-89e9-03b8022a9f48 · outbound

This paper cites Kim, Interference mitigation in wireless communications.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Kim, Interference mitigation in wireless communications

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.132965Z

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.

Observation fea0dc85-eccf-4986-b3ab-18a296e7fef9 · outbound

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

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Interference prediction for low-complexity link adaptation in beyond 5G ultra-reliable low-latency communications,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.123572Z

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.

Observation 3b842381-9258-4f43-a8eb-33f895b3c855 · outbound

This paper cites A predictive interference management algorithm for URLLC in beyond 5G networks,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning A predictive interference management algorithm for URLLC in beyond 5G networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.113640Z

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.

Observation cfb4ccf5-d4a6-4d24-8c08-419bcc383e6d · outbound

This paper cites Reliable interference prediction and management with time-correlated traffic for URLLC,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Reliable interference prediction and management with time-correlated traffic for URLLC,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.103963Z

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.

Observation 3ca0a333-ecfa-470e-ad1b-8dd88ac5048a · outbound

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

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Ultra-High Reliability by Predictive Interference Management Using Extreme Value Theory

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:02:05.680359Z

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.

Observation ccc1f831-d6fc-4f6e-90ef-fcb500dae956 · outbound

This paper cites Extreme value theory-based predictive interference management for 6G subnetworks with transformer,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Extreme value theory-based predictive interference management for 6G subnetworks with transformer,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.093790Z

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.

Observation 1e4aa351-1af6-422c-a9f4-3b96e6c35429 · outbound

This paper cites Resource management in wireless networks via multi-agent deep reinforcement learning,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Resource management in wireless networks via multi-agent deep reinforcement learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.082709Z

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.

Observation 8657e532-99e2-4548-9242-03d6393688ba · outbound

This paper cites Learning-based energy-efficient resource management by heterogeneous RF/VLC for ultra-reliable low-latency industrial IoT networks,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Learning-based energy-efficient resource management by heterogeneous RF/VLC for ultra-reliable low-latency industrial IoT networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.072196Z

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.

Observation d976758e-8a92-42fc-b3db-c8ff545e7520 · outbound

This paper cites Toward deep Q-network-based resource allocation in industrial Internet of things,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Toward deep Q-network-based resource allocation in industrial Internet of things,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.061085Z

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.

Observation 5032467f-8d3c-4cea-9996-076ff6e3ef85 · outbound

This paper cites Optimization theory based deep reinforcement learning for resource allocation in ultra-reliable wireless networked control systems,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Optimization theory based deep reinforcement learning for resource allocation in ultra-reliable wireless networked control systems,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.050502Z

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.

Observation 4d61fc3c-6e6e-48e5-87cc-a202f3718ba9 · outbound

This paper cites Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:02:05.534241Z

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.

Observation dc321361-cb7c-44df-9a85-26724d87c4e1 · outbound

This paper cites Multi-agent reinforcement learning for dynamic resource management in 6G in-X subnetworks,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Multi-agent reinforcement learning for dynamic resource management in 6G in-X subnetworks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.039624Z

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.

Observation e8b8c2de-f640-4e79-acee-9d4203cdb520 · outbound

This paper cites Onboard spectral analysis for low-complexity IoT devices,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Onboard spectral analysis for low-complexity IoT devices,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.027997Z

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.

Observation 5f552695-995b-4ac1-8a63-90a324ce3492 · outbound

This paper cites Co-designing wireless networked control systems on IEEE 802.15.4-based links under Wi-Fi interference,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Co-designing wireless networked control systems on IEEE 802.15.4-based links under Wi-Fi interference,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.016576Z

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.

Observation 51ebd6e8-f80f-4a2e-b3ce-7761e43e7521 · outbound

This paper cites A categorical framework of manufacturing for industry 4.0 and beyond,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning A categorical framework of manufacturing for industry 4.0 and beyond,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:06.005618Z

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.

Observation 0b348797-2ce2-447e-bd11-62b6323fcb57 · outbound

This paper cites Towards defining industry 5.0 vision with intelligent and softwarized wireless network architectures and services: A survey,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Towards defining industry 5.0 vision with intelligent and softwarized wireless network architectures and services: A survey,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:05.994974Z

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.

Observation 3c8ab6e9-b3a4-4be2-9e1d-34dc44383a3a · outbound

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

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Channel coding rate in the finite blocklength regime,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:05.984499Z

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.

Observation 81f55392-f4e9-4a7f-831e-d71777b48b54 · outbound

This paper cites Reliability and delay analysis of 3-dimensional net- works with multi-connectivity: Satellite, HAPs, and cellular communi- cations,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Reliability and delay analysis of 3-dimensional net- works with multi-connectivity: Satellite, HAPs, and cellular communi- cations,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:05.973368Z

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.

Observation 22cfbb9f-aa2e-47c9-8e67-e7bf5f856f82 · outbound

This paper cites Mission reliability for URLLC in wireless networks,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Mission reliability for URLLC in wireless networks,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:05.962893Z

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.

Observation d7f04054-d403-4489-9c03-a37b2bce522c · outbound

This paper cites A comparison among deterministic packet- dropouts models in networked control systems,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning A comparison among deterministic packet- dropouts models in networked control systems,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:05.951958Z

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.

Observation 32101fdc-132f-4123-8672-14f288313708 · outbound

This paper cites Meta reinforcement learning for resource alloca- tion in aerial active-RIS-assisted networks with rate-splitting multiple access,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Meta reinforcement learning for resource alloca- tion in aerial active-RIS-assisted networks with rate-splitting multiple access,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:05.939932Z

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.

Observation fb9ab62a-4a09-411f-9a16-6313adb3728a · outbound

This paper cites Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:05.926944Z

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.

Observation c2c3a8c3-b05f-48c3-893f-48fc97d2e7d8 · outbound

This paper cites Stable-baselines3: Reliable reinforcement learning implementations,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Stable-baselines3: Reliable reinforcement learning implementations,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:05.916351Z

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.

Observation e7c2e428-9aca-4582-a174-20f952c93e99 · outbound

This paper cites Exploration in deep reinforcement learning: A survey,.

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning Exploration in deep reinforcement learning: A survey,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:02:05.857389Z

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.

Pith citing papers

No inbound Pith citation observations are available.