Paper Citation Record · LEDGER

Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications

As of 15 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2412.01839.

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

pith.paper-citation-record.v1
2412.01839 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:56:30.392172Z

measured 17 of 17 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

17 of 17 outbound references displayed

verified exact4
verified fuzzy7
unresolved4
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cd416721-352d-4867-b891-c127c9fbb793 · outbound

This paper cites Efficient and Robust Reinforcement Learning with Uncertainty-based Value Expansion.

Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications Efficient and Robust Reinforcement Learning with Uncertainty-based Value Expansion

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-12T18:56:30.694556Z

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 0fcfa08c-5a26-4227-a504-b0665b4626f4 · outbound

This paper cites Reinforcement learning with experience replay and adaptation of action dispersion.

Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications Reinforcement learning with experience replay and adaptation of action dispersion

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-12T18:56:30.671695Z

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 ef1f2c0b-f410-441f-924a-6cf9e348a814 · outbound

This paper cites ”A deep reinforcement learning based framework for power- efficient resource allocation in cloud RANs.” In 2017 IEEE International Conference, 2017.

Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications ”A deep reinforcement learning based framework for power- efficient resource allocation in cloud RANs.” In 2017 IEEE International Conference, 2017

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:30.824835Z

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 0a1625fb-1727-4fd6-8a2d-95922ee45882 · outbound

This paper cites Huang, B.

Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications Huang, B

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:30.810269Z

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 7acb5b08-fcbb-46d9-a3d5-782b47cfeaca · outbound

This paper cites ”Reinforcement learning-based mobile edge com- puting and transmission scheduling for video surveillance.” IEEE Trans- actions on Emerging Topics in Computing , pages 1–1, 2021.

Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications ”Reinforcement learning-based mobile edge com- puting and transmission scheduling for video surveillance.” IEEE Trans- actions on Emerging Topics in Computing , pages 1–1, 2021

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:30.796730Z

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 93045c10-bdde-4bca-8474-050d2a607c58 · outbound

This paper cites Ming et al.

Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications Ming et al

Reference 6

Resolution: malformed identifier; no resolver link, observed 2026-08-12T18:56:30.340377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97a220b4-2ac9-4db0-9cc0-f86c95436268 · outbound

This paper cites an unresolved cited work.

Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-12T18:56:30.782448Z

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 e14b7e9d-26d9-49b9-a1e3-60a26da67f7e · outbound

This paper cites ”On-Policy vs.

Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications ”On-Policy vs

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:30.768582Z

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 f075a297-689f-4821-a92b-af8124ad58f8 · outbound

This paper cites an unresolved cited work.

Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications Unresolved cited work

Reference 9

Resolution: metadata mismatch; raw_fallback, observed 2026-08-12T18:56:30.566042Z

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 20e2609f-f719-4c92-8efa-1d3c3d3d5ed1 · outbound

This paper cites Intelligent Load Balancing and Resource Allocation in O-RAN: A Multi-Agent Multi-Armed Bandit Approach.

Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications Intelligent Load Balancing and Resource Allocation in O-RAN: A Multi-Agent Multi-Armed Bandit Approach

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-12T18:56:30.466431Z

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 788f4b48-644b-41a5-a48f-19923dce77d4 · outbound

This paper cites an unresolved cited work.

Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-12T18:56:30.753590Z

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 c812f641-fea1-4d54-a396-bf37e692e720 · outbound

This paper cites ”Deep reinforcement learning for resource management in network slicing.” IEEE Access , 6:74429–74441, 2018.

Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications ”Deep reinforcement learning for resource management in network slicing.” IEEE Access , 6:74429–74441, 2018

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:30.738756Z

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 6b812736-7971-481e-9d3f-105cbe8ef72b · outbound

This paper cites Proximal Policy Optimization Algorithms.

Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications Proximal Policy Optimization Algorithms

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T18:56:30.378088Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3dc1c6dc-a820-4435-b47f-71043dd4002b · outbound

This paper cites Sample Efficient Actor-Critic with Experience Replay.

Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications Sample Efficient Actor-Critic with Experience Replay

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T18:56:30.382713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfdfc393-8bcd-4cea-af1a-581a1dbc1797 · outbound

This paper cites [online] Available: https: //github.com/alibaba/clusterdata/tree/master/cluster-trace-v2018/.

Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications [online] Available: https: //github.com/alibaba/clusterdata/tree/master/cluster-trace-v2018/

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:30.724188Z

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 afc2e878-93db-4a95-b605-2d01a674f093 · outbound

This paper cites ”On-policy vs.

Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications ”On-policy vs

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:30.709295Z

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 6828eea2-8b0f-4de3-a7a6-7e8a442c42b4 · outbound

This paper cites Deep Reinforcement Learning-based Radio Resource Allocation and Beam Management under Location Uncertainty in 5G mmWave Networks.

Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications Deep Reinforcement Learning-based Radio Resource Allocation and Beam Management under Location Uncertainty in 5G mmWave Networks

Reference 2024

Resolution: verified exact; local_arxiv, observed 2026-08-12T18:56:30.587000Z

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.