Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-25T12:57:40.582343Z
Paper Citation Record · LEDGER
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin
As of 12 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:1907.01523.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
50 of 50 outbound references displayed
- verified exact6
- verified fuzzy39
- unresolved5
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation bd7aebcf-6dde-4a51-8be3-add73ef62338 · outbound
This paper cites The number of subcarriers allocated to the kth user is denoted as N ξ m,k.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin The number of subcarriers allocated to the kth user is denoted as N ξ m,k
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.800741Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 3d0becd6-13fc-4a51-9ad6-d1d7c5fa87fa · outbound
This paper cites an unresolved cited work.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Unresolved cited work
Reference 2
- Resolution
- unresolved
- raw_fallback, observed 2026-05-25T13:00:50.853273Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation ea43b9d4-fab3-47ac-acee-c3afa96ac6d2 · outbound
This paper cites If the small-scale channel gain is above the threshold, the n the packets are offloaded to the MEC with probability one.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin If the small-scale channel gain is above the threshold, the n the packets are offloaded to the MEC with probability one
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.860420Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation a297cf30-f4c8-48ba-8063-e659438e2fa4 · outbound
This paper cites We consider an offloading policy that does not depend on the current small-scale channel gain.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin We consider an offloading policy that does not depend on the current small-scale channel gain
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.808831Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 572bdb7e-d79d-4785-a710-379431374231 · outbound
This paper cites an unresolved cited work.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Unresolved cited work
Reference 5
- Resolution
- unresolved
- raw_fallback, observed 2026-05-25T13:00:50.948885Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation f886d981-e34b-4a21-9d76-d5a03accda8b · outbound
This paper cites The E2E delay of a packet when offloading to the MEC s erver should satisfy the following constraint, 1 + Dmc, u k ≤ Dmax, u, (11) where data transmission occupies one slot.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin The E2E delay of a packet when offloading to the MEC s erver should satisfy the following constraint, 1 + Dmc, u k ≤ Dmax, u, (11) where data transmission occupies one slot
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.953077Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation bcb87c13-0789-4e99-b603-81451efdb658 · outbound
This paper cites (14) Besides, the processing rate should not exceed the maximal c omputing capacity of the server, C b k ≤ C max, b k.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin (14) Besides, the processing rate should not exceed the maximal c omputing capacity of the server, C b k ≤ C max, b k
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.964453Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 52b51ed3-20e7-44fc-bca8-bd3ca7b7718f · outbound
This paper cites an unresolved cited work.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Unresolved cited work
Reference 8
- Resolution
- unresolved
- raw_fallback, observed 2026-05-25T13:00:50.841299Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 4254287f-3c83-4c3b-9c23-d9136ad572c9 · outbound
This paper cites (16) Otherwise, constraint (13) should be satisfied.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin (16) Otherwise, constraint (13) should be satisfied
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.934231Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 8e6f2e56-cb6a-4a56-a8f8-280705a3d6b2 · outbound
This paper cites an unresolved cited work.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Unresolved cited work
Reference 10
- Resolution
- unresolved
- raw_fallback, observed 2026-05-25T13:00:50.833536Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation ad66a0a4-7998-4f16-98f3-29f5a75530f9 · outbound
This paper cites Then, the energy consumption per bit is ηloc, b k = Eloc, b k / ¯bb k.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Then, the energy consumption per bit is ηloc, b k = Eloc, b k / ¯bb k
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.930571Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation fa997bf8-7dce-4381-96d1-4736583ae709 · outbound
This paper cites To find the optimal offloading probability, we optimize gth, u k by the following three steps to meet all the constraints in pr oblem (23).
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin To find the optimal offloading probability, we optimize gth, u k by the following three steps to meet all the constraints in pr oblem (23)
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.938468Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 7040f7da-3ceb-482b-808f-ee018b4ea9b4 · outbound
This paper cites an unresolved cited work.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Unresolved cited work
Reference 13
- Resolution
- unresolved
- raw_fallback, observed 2026-05-25T13:00:50.945416Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 0e172e89-9b1a-4794-a356-c45de4aba473 · outbound
This paper cites We denote the index of the AP with the highest output as m∗ k = arg maxm∈M ˆβ ξ m,k.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin We denote the index of the AP with the highest output as m∗ k = arg maxm∈M ˆβ ξ m,k
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.941815Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation c3dd4e9d-e362-4772-98e1-e1c43791ad96 · outbound
This paper cites Since only one user changes the scheme, this method is referr ed to as one step exploration.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Since only one user changes the scheme, this method is referr ed to as one step exploration
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.971648Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation c3a6efc2-2fe1-4fdc-8ae9-6dc7be7c013f · outbound
This paper cites The user association schemes generated with this method ar e denoted as β(µ OS + 1), ..., β(µ OS + µ RE), where µ RE is the number of schemes generated with the method.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin The user association schemes generated with this method ar e denoted as β(µ OS + 1), ..., β(µ OS + µ RE), where µ RE is the number of schemes generated with the method
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.922179Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation a61a6357-5c12-452e-9ad5-cc8d76e508a3 · outbound
This paper cites Study on scenarios and requir ements for next generation access technologies.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Study on scenarios and requir ements for next generation access technologies
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.849395Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 8dd602f0-5b81-4ee5-94c3-97cd66d982f8 · outbound
This paper cites Latency critical IoT applications in 5G: Perspective on t he design of radio interface and network architecture.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Latency critical IoT applications in 5G: Perspective on t he design of radio interface and network architecture
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.918145Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation f544fa15-d5f7-48f5-b924-593daa678325 · outbound
This paper cites A sur vey on mobile edge computing: The communication perspective.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin A sur vey on mobile edge computing: The communication perspective
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.926747Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 7d397ebc-ed74-4c23-9d05-32dd884b96b2 · outbound
This paper cites Cross-layer optimizat ion for ultra-reliable and low-latency radio access networ ks.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Cross-layer optimizat ion for ultra-reliable and low-latency radio access networ ks
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.910520Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation d44ad964-c7b9-4433-bbf0-ef0dde07e199 · outbound
This paper cites Quasi-st atic multiple-antenna fading channels at finite blocklengt h.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Quasi-st atic multiple-antenna fading channels at finite blocklengt h
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.906907Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 49b1bf6a-fe07-4ed9-a4d8-bc484c14b4e8 · outbound
This paper cites Energy-Efficient Joint Offloading and Wireless Resource Allocation Strategy in Multi-MEC Server Systems.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Energy-Efficient Joint Offloading and Wireless Resource Allocation Strategy in Multi-MEC Server Systems
Reference 22
- Resolution
- verified exact
- local_arxiv, observed 2026-05-25T13:00:50.140973Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 439162ea-7ac2-496a-b78e-3e22a927e5d4 · outbound
This paper cites Energy-latency tradeoff for energy-aware offloading in mobile edge computing networks.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Energy-latency tradeoff for energy-aware offloading in mobile edge computing networks
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.864098Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation ab959c9c-3e70-4575-a25a-8ee85c9f9712 · outbound
This paper cites Asynchronous Mobile-Edge Computation Offloading: Energy-Efficient Resource Management.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Asynchronous Mobile-Edge Computation Offloading: Energy-Efficient Resource Management
Reference 24
- Resolution
- verified exact
- local_arxiv, observed 2026-05-25T13:00:50.121811Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 560d340e-3aaa-4f87-87d0-1b8f91d67321 · outbound
This paper cites Exploiting future radio res ources with end-to-end prediction by deep learning.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Exploiting future radio res ources with end-to-end prediction by deep learning
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.892382Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 4e52a130-6627-4af8-b96b-c8cc30ad9c7e · outbound
This paper cites Applications of deep reinforcement learning in communic ations and networking: A survey.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Applications of deep reinforcement learning in communic ations and networking: A survey
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.856935Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 7d0912a0-25e1-4c0a-b3fe-9bef55ff6082 · outbound
This paper cites APM: Driving value with the digital twin.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin APM: Driving value with the digital twin
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.875729Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation d3b3bc13-8e75-414e-a130-85b867602dda · outbound
This paper cites Energy-ef ficient joint offloading and wireless resource allocation strategy in multi-MEC server systems.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Energy-ef ficient joint offloading and wireless resource allocation strategy in multi-MEC server systems
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.880119Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation f60fdeae-fe12-4b47-bffd-4553126453c4 · outbound
This paper cites Wireless networks for mo bile edge computing: Spatial modelling and latency analysi s.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Wireless networks for mo bile edge computing: Spatial modelling and latency analysi s
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.868060Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 83643794-8764-4f76-b6f9-7b156765b0e2 · outbound
This paper cites Joint resource allocati on and user association for heterogeneous services in multi -access edge computing networks.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Joint resource allocati on and user association for heterogeneous services in multi -access edge computing networks
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.871910Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 74c119eb-ee2a-4de5-bbdf-db2d1fecf3b3 · outbound
This paper cites Performance Optimization in Mobile-Edge Computing via Deep Reinforcement Learning.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Performance Optimization in Mobile-Edge Computing via Deep Reinforcement Learning
Reference 31
- Resolution
- verified exact
- local_arxiv, observed 2026-05-25T13:00:50.146860Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 8f738807-62b2-4437-b749-237fc2b82543 · outbound
This paper cites Learning-Based Computation Offloading for IoT Devices with Energy Harvesting.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Learning-Based Computation Offloading for IoT Devices with Energy Harvesting
Reference 32
- Resolution
- verified exact
- local_arxiv, observed 2026-05-25T13:00:50.153179Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 62b29131-6854-465d-9af5-0da550684cff · outbound
This paper cites Online learning for offloadin g and autoscaling in energy harvesting mobile edge computin g.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Online learning for offloadin g and autoscaling in energy harvesting mobile edge computin g
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.888533Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation ef92e09e-268d-4154-8f95-a8991200399a · outbound
This paper cites Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks
Reference 34
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T13:00:50.129466Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 10b3d8c3-e970-449a-9de0-5c5a24f84b81 · outbound
This paper cites Latency and reliab ility-aware task offloading and resource allocation for mob ile edge computing.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Latency and reliab ility-aware task offloading and resource allocation for mob ile edge computing
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.913868Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation c0273122-bf43-4c46-8ab8-fad7c51e13a8 · outbound
This paper cites Offloading schemes in mobile edge co mputing for ultra-reliable low latency communications.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Offloading schemes in mobile edge co mputing for ultra-reliable low latency communications
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.956779Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 8dfbe6d8-b71f-4d5e-a31f-59475d607b66 · outbound
This paper cites Energy efficiency of mobile clients in cloud computing.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Energy efficiency of mobile clients in cloud computing
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.884611Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 1ab6313c-e9df-4e7a-b0f7-2f33f361d830 · outbound
This paper cites Optimizati on of radio and computational resources for energy efficienc y in latency-constrained application offloading.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Optimizati on of radio and computational resources for energy efficienc y in latency-constrained application offloading
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.829738Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 076aba26-49f4-4e15-b4d1-7edd050266b8 · outbound
This paper cites Mobile-ed ge computing: Partial computation offloading using dynamic voltage scaling.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Mobile-ed ge computing: Partial computation offloading using dynamic voltage scaling
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.837480Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 4f803757-b85b-4d95-817e-1447c0432300 · outbound
This paper cites Harchol-Balter, Performance Modeling and Design of Computer Systems: Queue ing Theory in Action.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Harchol-Balter, Performance Modeling and Design of Computer Systems: Queue ing Theory in Action
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.812970Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 760b52e2-4f24-4e02-a67c-fb0e31f265c3 · outbound
This paper cites Delay analysi s and computing offloading of URLLC in mobile edge computing systems.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Delay analysi s and computing offloading of URLLC in mobile edge computing systems
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.822260Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation f585ba04-a677-48a2-a938-e5a2b67e5ebe · outbound
This paper cites Energy-optimal mobile cloud computing under stochastic wireless channel.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Energy-optimal mobile cloud computing under stochastic wireless channel
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.960716Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 3d767953-f0b3-401f-883e-e9c803a93705 · outbound
This paper cites On the Geo/D/1 a nd Geo/D/1/N queues.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin On the Geo/D/1 a nd Geo/D/1/N queues
Reference 43
- Resolution
- verified exact
- doi, observed 2026-05-25T13:00:50.041623Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation af32273d-e114-4a33-ab2d-1bfb6c2cb151 · outbound
This paper cites Delay analys is for wireless fading channels with finite blocklength chan nel coding.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Delay analys is for wireless fading channels with finite blocklength chan nel coding
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.804916Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 19d16406-0399-4b09-a6e5-a4c387c71ccc · outbound
This paper cites Optimizing resource allocation in the short blocklength regime for ultra-reliable and low-latency communications.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Optimizing resource allocation in the short blocklength regime for ultra-reliable and low-latency communications
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.900096Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 08938fe6-5222-43d2-b960-c4301d4938e1 · outbound
This paper cites Human-level control through deep reinforcement learnin g.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Human-level control through deep reinforcement learnin g
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.817245Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation ed3441d9-e7a9-4785-b5ab-e2e893be0627 · outbound
This paper cites Adam: A method for stochastic opt imization.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Adam: A method for stochastic opt imization
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.825953Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 0b0d4223-e62c-4db8-9692-92ef8b40f977 · outbound
This paper cites Evolved universal ter restrial radio access.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Evolved universal ter restrial radio access
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.896204Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation b3ab348a-f3c3-4648-b180-efa0839bb281 · outbound
This paper cites Burst iness aware bandwidth reservation for ultra-reliable and low-latency communications (URLLC) in tactile internet.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Burst iness aware bandwidth reservation for ultra-reliable and low-latency communications (URLLC) in tactile internet
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.845253Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 30b3a4a3-5433-4af1-a5a3-9ca6835c3450 · outbound
This paper cites Boyd and L.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin Boyd and L
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T13:00:50.967971Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Pith citing papers
No inbound Pith citation observations are available.