Paper Citation Record · LEDGER

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics

As of 19 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:1908.03242.

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

pith.paper-citation-record.v1
1908.03242 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:24:30.293448Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

33 of 33 outbound references displayed

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verified fuzzy28
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5b3052f4-9c37-4b83-92a8-24cbbbe56e7d · outbound

This paper cites Network function virtualization: State-of-the-art and re- search challenges,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Network function virtualization: State-of-the-art and re- search challenges,

Reference 1

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Observation b462cb0c-c8b7-4d9e-9a44-0d0a5e2d85c0 · outbound

This paper cites Software-deﬁned networking,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Software-deﬁned networking,

Reference 2

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Observation 5b24916f-f7b3-4b12-be20-6136e77e926f · outbound

This paper cites Massive mimo for 5g,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Massive mimo for 5g,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.771610Z

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Observation 9defc4d7-45d4-4af1-9ce6-69eeaa6e05b4 · outbound

This paper cites Industry 4.0 — Wikipedia, the free encyclope- dia,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Industry 4.0 — Wikipedia, the free encyclope- dia,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.754933Z

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Observation 4f03e22c-b0a4-4a06-8d79-ca09f237707a · outbound

This paper cites An introduction to network slicing,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics An introduction to network slicing,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.740835Z

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Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Unresolved cited work

Reference 6

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Observation c5c124de-3853-4c45-b6c3-cc4fb708df99 · outbound

This paper cites Network optimization and control,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Network optimization and control,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.712910Z

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Observation 4e823df2-6459-4eb0-8bb8-51d6c4e583dc · outbound

This paper cites Srikant and L.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Srikant and L

Reference 8

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Observation 64c7d836-de1f-4e27-b0e2-04302eb42373 · outbound

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Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Unresolved cited work

Reference 9

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Source-reported events for the cited work

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Observation 7ae419f0-9f93-4b24-9959-1e8a4317df0a · outbound

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

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Mastering the game of go without human knowledge,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.679313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6ebdbc88-2449-4e10-a56b-839521ff3664 · outbound

This paper cites Network slicing for 5g: Challenges and opportunities,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Network slicing for 5g: Challenges and opportunities,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.668044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0768acfc-6488-4e6e-80ec-dec9e0f7bcb5 · outbound

This paper cites Network slicing to enable scalability and ﬂexibility in 5g mobile networks,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Network slicing to enable scalability and ﬂexibility in 5g mobile networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.654997Z

Source-reported events for the cited work

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Observation 64aaf372-8ef0-4fea-8054-9eef3f1ddad9 · outbound

This paper cites Resource man- agement with deep reinforcement learning,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Resource man- agement with deep reinforcement learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.640243Z

Source-reported events for the cited work

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Observation 0fe46aa9-8d5c-4d61-a3ec-876358e088a6 · outbound

This paper cites Simple statistical gradient-following algorithms for connectionist reinforcement learning,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Simple statistical gradient-following algorithms for connectionist reinforcement learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.626128Z

Source-reported events for the cited work

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Observation 80cb833c-7d8f-4aa5-9f6e-896a4e3bf2d6 · outbound

This paper cites Rectiﬁer nonlinearities improve neural network acoustic models,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Rectiﬁer nonlinearities improve neural network acoustic models,

Reference 15

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Source-reported events for the cited work

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Observation 9fafb2d5-f75e-42db-982e-eca8eeb5f463 · outbound

This paper cites Statistical workload injector for mapreduce (swim).

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Statistical workload injector for mapreduce (swim)

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.599195Z

Source-reported events for the cited work

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Observation 892b26ee-15b1-40cb-ac93-a0b17f809b01 · outbound

This paper cites Http/2-based adaptive streaming of hevc video over 4g/lte networks,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Http/2-based adaptive streaming of hevc video over 4g/lte networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.585586Z

Source-reported events for the cited work

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Observation 94194db0-3517-4f45-9a10-7605e79365b6 · outbound

This paper cites Adam: A method for stochastic optimization,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Adam: A method for stochastic optimization,

Reference 18

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This paper cites A new approach for allocating buffers and bandwidth to heterogeneous, regulated trafﬁc in an atm node,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics A new approach for allocating buffers and bandwidth to heterogeneous, regulated trafﬁc in an atm node,

Reference 19

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Source-reported events for the cited work

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Observation 459b3874-edcc-4705-8c7b-d582b69e1afd · outbound

This paper cites Adaptable bandwidth planning using reinforcement learn- ing,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Adaptable bandwidth planning using reinforcement learn- ing,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.549201Z

Source-reported events for the cited work

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Observation fd60f96f-888e-4455-a19c-bafeff958f8b · outbound

This paper cites Adaptive call admission control under quality of service constraints: a reinforcement learning solution,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Adaptive call admission control under quality of service constraints: a reinforcement learning solution,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.532598Z

Source-reported events for the cited work

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Observation a296d2c1-51f1-4fc4-9471-9dc59c246f92 · outbound

This paper cites Adaptive provisioning of differentiated services networks based on reinforcement learning,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Adaptive provisioning of differentiated services networks based on reinforcement learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.516145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4431c254-2b7f-47dc-8fa3-284c8d54164e · outbound

This paper cites A reinforcement learning scheme for adaptive link allocation in atm networks,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics A reinforcement learning scheme for adaptive link allocation in atm networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.500091Z

Source-reported events for the cited work

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Observation 40aee79c-8758-4a13-a3ec-baa74d91c245 · outbound

This paper cites Self- learning cloud controllers: Fuzzy q-learning for knowledge evolution,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Self- learning cloud controllers: Fuzzy q-learning for knowledge evolution,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.484927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 66e9c899-af42-4447-84aa-dab9f661d79d · outbound

This paper cites Rlpas: Reinforcement learning-based proactive auto-scaler for resource provisioning in cloud environment,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Rlpas: Reinforcement learning-based proactive auto-scaler for resource provisioning in cloud environment,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.469015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 59bc275d-2abd-455c-bd0c-b9f11136c0e5 · outbound

This paper cites Optimising 5g infrastructure markets: The business of network slicing,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Optimising 5g infrastructure markets: The business of network slicing,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.450545Z

Source-reported events for the cited work

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Observation d4562295-a733-45e8-8c55-6af3516613d2 · outbound

This paper cites Slice as an evolutionary service: Genetic optimization for inter-slice resource management in 5g networks,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Slice as an evolutionary service: Genetic optimization for inter-slice resource management in 5g networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.435193Z

Source-reported events for the cited work

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Observation 9075c9b0-d4c6-4260-8d59-1922b660bb58 · outbound

This paper cites Software-deﬁned networks with mobile edge computing and caching for smart cities: A big data deep reinforcement learning approach,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Software-deﬁned networks with mobile edge computing and caching for smart cities: A big data deep reinforcement learning approach,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.418473Z

Source-reported events for the cited work

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Observation 03c38cfc-7c35-4b0a-bc93-7bb620ddb102 · outbound

This paper cites A deep reinforcement learning based framework for power-efﬁcient resource allocation in cloud rans,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics A deep reinforcement learning based framework for power-efﬁcient resource allocation in cloud rans,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.403040Z

Source-reported events for the cited work

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Observation 7e1e6feb-dbc0-456d-969f-681c3d9b3f83 · outbound

This paper cites Deep rein- forcement learning (drl)-based resource management in software-deﬁned and virtualized vehicular ad hoc networks,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Deep rein- forcement learning (drl)-based resource management in software-deﬁned and virtualized vehicular ad hoc networks,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.386105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0f7afb52-1ce1-4974-aa2b-0e8064805cf8 · outbound

This paper cites Deep reinforcement learning for network slicing,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Deep reinforcement learning for network slicing,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.371962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b8e61833-63ff-4ca6-b45b-8554af25a7dc · outbound

This paper cites Konda, Actor-critic Algorithms.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Konda, Actor-critic Algorithms

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:24:30.356908Z

Source-reported events for the cited work

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Observation 72fd06c6-15c1-483e-bb9c-3a5496f5eafb · outbound

This paper cites Playing atari with deep reinforcement learn- ing,.

Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics Playing atari with deep reinforcement learn- ing,

Reference 33

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Source-reported events for the cited work

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Pith citing papers

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