Paper Citation Record · LEDGER

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access

As of 16 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:1908.08401.

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

pith.paper-citation-record.v1
1908.08401 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:18:31.591620Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

35 of 35 outbound references displayed

verified exact2
verified fuzzy30
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1b972793-fb57-4db5-bab3-e2ed72bf0998 · outbound

This paper cites Heuristic search value iterati on for POMDPs,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Heuristic search value iterati on for POMDPs,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:33.884054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b8ebc246-f6a9-437b-aa3a-cf3ff3cf0607 · outbound

This paper cites MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-14T12:18:31.808594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4d303e5c-1b41-48a8-80a0-0f8c887fa2fd · outbound

This paper cites Monte-carlo planning in large P OMDPs,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Monte-carlo planning in large P OMDPs,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:33.872174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f2323576-bd8c-4cbe-8aaf-feaf115921ca · outbound

This paper cites Monte carlo POMDPs,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Monte carlo POMDPs,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:33.829093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0cb8b584-5458-441a-807b-c2b3bcb602cc · outbound

This paper cites Natural actor -critic,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Natural actor -critic,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:33.621330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0fe2d0e9-cd12-4bbd-951d-5908ff58f24a · outbound

This paper cites an unresolved cited work.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-14T12:18:33.608284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d5da5475-f51e-4f35-b075-c0e0243ad8e6 · outbound

This paper cites Deep reinforcement learning for dynamic multichannel access in wireless networks,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Deep reinforcement learning for dynamic multichannel access in wireless networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:33.595816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e383e177-7f8c-4773-b15d-a43431b1c72d · outbound

This paper cites Full spectrum sharing in cogni tive radio networks toward 5G: A survey,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Full spectrum sharing in cogni tive radio networks toward 5G: A survey,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:33.397010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e5595a79-a782-4420-aa32-3fd8a1889c91 · outbound

This paper cites Decentralized co gnitive MAC for opportunistic spectrum access in ad hoc netw orks: A POMDP framework,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Decentralized co gnitive MAC for opportunistic spectrum access in ad hoc netw orks: A POMDP framework,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:33.316794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 84d18d07-7070-4449-881f-9d6efdcf82df · outbound

This paper cites A restless bandit formulation of opp ortunistic access: Indexablity and index policy,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access A restless bandit formulation of opp ortunistic access: Indexablity and index policy,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:33.299822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e393985d-7066-471d-bd38-925f8284d025 · outbound

This paper cites On myopic sensi ng for multi-channel opportunistic access: structure, opt imality, and performance,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access On myopic sensi ng for multi-channel opportunistic access: structure, opt imality, and performance,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:33.283231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 37baff21-9322-48e6-9c24-1e5ffac265ea · outbound

This paper cites Optimality of myopic sensing in multichannel oppo rtunistic access,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Optimality of myopic sensing in multichannel oppo rtunistic access,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:33.053265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 810463cb-db1d-4671-9cad-112f9c7b4604 · outbound

This paper cites Oppor tunistic spectrum access in unknown dynamic environment: A game-theoretic stochastic learning solution,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Oppor tunistic spectrum access in unknown dynamic environment: A game-theoretic stochastic learning solution,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:32.976681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation eb12247f-4d2d-4904-bd67-924771b41e44 · outbound

This paper cites Stochastic ga me-theoretic spectrum access in distributed and dynamic en vironment,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Stochastic ga me-theoretic spectrum access in distributed and dynamic en vironment,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:32.962349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0914dfc4-8d27-4066-8d01-6b7eb36ef91f · outbound

This paper cites Optimally probing cha nnel in opportunistic spectrum access,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Optimally probing cha nnel in opportunistic spectrum access,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:32.949352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 205be30d-167f-4755-b234-01eda8bac9ac · outbound

This paper cites Human-level control through deep reinforcement learnin g,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Human-level control through deep reinforcement learnin g,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:32.877666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 062340f9-938f-4c53-9dee-6cc503586946 · outbound

This paper cites Mastering the game of go without human knowledge,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Mastering the game of go without human knowledge,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:32.759798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 631267c1-0cfc-41f3-9aca-6e2c2199a8c8 · outbound

This paper cites Applications of Deep Reinforcement Learning in Communications and Networking: A Survey.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Reference 18

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Source-reported events for the cited work

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Observation d14ad593-80df-4412-9520-7eff368cf9b2 · outbound

This paper cites Deep Learning in Mobile and Wireless Networking: A Survey.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Deep Learning in Mobile and Wireless Networking: A Survey

Reference 19

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Source-reported events for the cited work

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Observation 4edfe897-adf8-4095-a6ff-8a21e06ec124 · outbound

This paper cites Reinforcem ent learning-based NOMA power allocation in the presence of smart jamming,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Reinforcem ent learning-based NOMA power allocation in the presence of smart jamming,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:32.746517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0ebf822e-23ce-4f69-8668-c4cbf2c1b0d4 · outbound

This paper cites Re inforcement learning for energy harvesting decode-and-fo rward two-hop communications,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Re inforcement learning for energy harvesting decode-and-fo rward two-hop communications,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:32.692341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cb04ee7b-33cd-493c-a1d6-1a6230562646 · outbound

This paper cites Deep q-learning based dynamic resource allocation for self-powe red ultra-dense networks,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Deep q-learning based dynamic resource allocation for self-powe red ultra-dense networks,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:32.548323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bea19510-a6c5-4a84-95ef-b14cf9b496aa · outbound

This paper cites A reinforcement learning- based resource allocation scheme for cloud robotics,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access A reinforcement learning- based resource allocation scheme for cloud robotics,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:32.534405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c41436ce-0159-457d-abe4-e5b9c1237507 · outbound

This paper cites Deep reinforcement learning for reso urce allocation in v2v communications,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Deep reinforcement learning for reso urce allocation in v2v communications,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:32.523266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 23977c01-ce6b-43d3-9662-522319646191 · outbound

This paper cites User scheduling and resource allocation in hetnets with hybrid energy supply: A n actor-critic reinforcement learning approach,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access User scheduling and resource allocation in hetnets with hybrid energy supply: A n actor-critic reinforcement learning approach,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:32.455523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 312efc86-ff35-4fd9-b6bb-247cc4292a19 · outbound

This paper cites Reinforcement Learning-based Resource Allocation in Fog RAN for IoT with Heterogeneous Latency Requirements.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Reinforcement Learning-based Resource Allocation in Fog RAN for IoT with Heterogeneous Latency Requirements

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-14T12:18:31.647423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ecda291c-2a52-4940-9aa3-226efb9b131d · outbound

This paper cites Deep-reinforcement lear ning multiple access for heterogeneous wireless networks,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Deep-reinforcement lear ning multiple access for heterogeneous wireless networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:32.387746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9eff475d-c9cb-40c4-bdc5-48a0b1652c25 · outbound

This paper cites Online learning for multi-channel opportunistic access over unknown Marko vian channels,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Online learning for multi-channel opportunistic access over unknown Marko vian channels,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:32.321004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4fdd4e1c-0a9f-457b-9dd7-3efd1917bc0b · outbound

This paper cites Model free dynam ic sensing order selection for imperfect sensing multichan nel cognitive radio networks: A Q-learning approach,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Model free dynam ic sensing order selection for imperfect sensing multichan nel cognitive radio networks: A Q-learning approach,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:32.291222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 065bccbe-cba5-4522-8ad5-43dfb1d7e6e5 · outbound

This paper cites Deep multi-user reinforcem ent learning for distributed dynamic spectrum access,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Deep multi-user reinforcem ent learning for distributed dynamic spectrum access,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:32.261692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d8cbea98-03d9-433d-b1c4-0fc8dd7bfbfa · outbound

This paper cites Deep reinforcement learning based dynamic channel allocation algorithm in multibeam sa tellite systems,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Deep reinforcement learning based dynamic channel allocation algorithm in multibeam sa tellite systems,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:32.122295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation dfbc972d-6b7f-4b3e-a794-d6b0d1e4fadc · outbound

This paper cites Multiagent Q-learning for aloha-like spectrum access in cognitive radio systems,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Multiagent Q-learning for aloha-like spectrum access in cognitive radio systems,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:32.055431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7d03cdf7-7976-4f34-af95-60a3a0212dd7 · outbound

This paper cites Distribu ted reinforcement learning based MAC protocols for autonom ous cognitive secondary users,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Distribu ted reinforcement learning based MAC protocols for autonom ous cognitive secondary users,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:31.949948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d0d2a0f6-ef47-4eef-a486-95c9e90daf12 · outbound

This paper cites Enhancing network performance in distributed cognitive radio networ ks using single- agent and multi-agent reinforcement learning,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Enhancing network performance in distributed cognitive radio networ ks using single- agent and multi-agent reinforcement learning,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:31.909927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f07c9ec6-f283-40b0-95ed-e223f473a08d · outbound

This paper cites Indexability of restless bandit pro blems and optimality of Whittle index for dynamic multichan nel access,.

A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access Indexability of restless bandit pro blems and optimality of Whittle index for dynamic multichan nel access,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:18:31.896764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

No inbound Pith citation observations are available.