Paper Citation Record · LEDGER

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems

As of 22 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 2 inbound Pith citation observations for arXiv:2411.09104.

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

pith.paper-citation-record.v1
2411.09104 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:10:19.927542Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:49:02.334599Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-12T14:49:02.390256Z

Reference resolution

30 of 30 outbound references displayed

verified exact0
verified fuzzy19
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e1b3d04d-c6a9-40af-addb-93c93935d494 · outbound

This paper cites Multi-user continuous-aperture ar- ray communications: How to learn current distribution?.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Multi-user continuous-aperture ar- ray communications: How to learn current distribution?

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:10:20.591542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8e5fcdd6-cbe1-4fe6-a4fb-7c50a4129ee1 · outbound

This paper cites A tutorial on holographic MIMO communications—part I: Channel modeling and channel estimation,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems A tutorial on holographic MIMO communications—part I: Channel modeling and channel estimation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:10:20.568691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0f6479d4-8790-4aa8-8a71-f4a112bb6c89 · outbound

This paper cites Holographic MIMO surfaces for 6G wireless networks: Opportunities, challenges, and trends,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Holographic MIMO surfaces for 6G wireless networks: Opportunities, challenges, and trends,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:10:20.547194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cb667f0a-156a-45ae-a10f-a01e4d34f2ad · outbound

This paper cites Beyond massive MIMO: The potential of data transmission with large intelligent surfaces,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Beyond massive MIMO: The potential of data transmission with large intelligent surfaces,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T21:10:19.781656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97e99079-0f6c-4276-9abd-f0356ed3f4b4 · outbound

This paper cites Reconfig- urable holographic surface-enabled multi-user wireless communications: Amplitude-controlled holographic beamforming,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Reconfig- urable holographic surface-enabled multi-user wireless communications: Amplitude-controlled holographic beamforming,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T21:10:19.786979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93c8c05b-a696-463c-bf46-d44a0f986028 · outbound

This paper cites Continuous aperture array (CAPA)-based wireless communications: Capacity characterization,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Continuous aperture array (CAPA)-based wireless communications: Capacity characterization,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T21:10:19.792978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30796988-969f-41f2-ad8a-e1a1a6ed5c93 · outbound

This paper cites Diversity and Multiplexing for Continuous Aperture Array (CAPA)-Based Communications.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Diversity and Multiplexing for Continuous Aperture Array (CAPA)-Based Communications

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T21:10:19.799240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05c03ae7-d441-4ba8-9267-59456be5b49a · outbound

This paper cites Mutual information for electromagnetic information theory based on random fields,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Mutual information for electromagnetic information theory based on random fields,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:10:20.491646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c2dab3bd-f5dc-4eb0-88d1-b778d95f46ba · outbound

This paper cites Fourier plane-wave series expansion for holographic MIMO communications,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Fourier plane-wave series expansion for holographic MIMO communications,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:10:20.464000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f53137bc-8656-4e4d-8d34-ab4f4e26c20f · outbound

This paper cites On the spectral efficiency of multi-user holographic MIMO uplink transmission,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems On the spectral efficiency of multi-user holographic MIMO uplink transmission,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:10:20.443833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 17705bc0-be78-4f27-980c-7f7d776e8733 · outbound

This paper cites Communicating with large intelligent surfaces: Fundamen- tal limits and models,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Communicating with large intelligent surfaces: Fundamen- tal limits and models,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T21:10:19.821725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71553af4-15f5-43a6-a458-1886115a8ea8 · outbound

This paper cites Wavenumber-division multiplexing in line-of-sight holographic MIMO communications,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Wavenumber-division multiplexing in line-of-sight holographic MIMO communications,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:10:20.409254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 75df68e3-da9e-4879-a845-9dcc1f6dfb80 · outbound

This paper cites Pattern-division multiplexing for multi-user continuous-aperture MIMO,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Pattern-division multiplexing for multi-user continuous-aperture MIMO,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T21:10:19.833391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c3c0763-5ce8-40a8-a419-155ec481e4e6 · outbound

This paper cites Beamforming Optimization for Continuous Aperture Array (CAPA)-based Communications.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Beamforming Optimization for Continuous Aperture Array (CAPA)-based Communications

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T21:10:19.840013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48365105-10a3-4d5c-aef1-082140c50268 · outbound

This paper cites Knowledge distillation-aided end-to- end learning for linear precoding in multiuser MIMO downlink systems with finite-rate feedback,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Knowledge distillation-aided end-to- end learning for linear precoding in multiuser MIMO downlink systems with finite-rate feedback,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:10:20.374339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c1edf01c-2ae1-4534-a493-cd2fc123e26b · outbound

This paper cites Deep learning methods for universal MISO beamforming,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Deep learning methods for universal MISO beamforming,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:10:20.354981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b5b0d754-fe83-4a4d-b74b-045c3f3711c5 · outbound

This paper cites Deep unsupervised learning for joint antenna selection and hybrid beamforming,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Deep unsupervised learning for joint antenna selection and hybrid beamforming,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:10:20.334201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b0592b07-a1d1-410f-9b93-ff49a6c42c1a · outbound

This paper cites Graph neural network aided power control in partially connected cell-free massive MIMO,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Graph neural network aided power control in partially connected cell-free massive MIMO,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T21:10:19.861245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ae30d9d-379f-47e9-9141-3ee85128643c · outbound

This paper cites Heterogeneous graph neural network for power allocation in multicarrier-division duplex cell-free massive MIMO systems,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Heterogeneous graph neural network for power allocation in multicarrier-division duplex cell-free massive MIMO systems,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:10:20.301215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 382953a1-1980-465f-abff-fb7904ea28ed · outbound

This paper cites Graph attention network-based precoding for reconfigurable intelligent surfaces aided wireless communication systems,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Graph attention network-based precoding for reconfigurable intelligent surfaces aided wireless communication systems,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:10:20.282639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 94bc8757-41c9-48a2-9488-f1c3d16463b5 · outbound

This paper cites GNN-based beamforming for sum-rate maximization in MU-MISO networks,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems GNN-based beamforming for sum-rate maximization in MU-MISO networks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:10:20.264745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 05e5c842-3536-49ac-bceb-56fe771f7c8b · outbound

This paper cites Multidimensional graph neural networks for wireless communications,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Multidimensional graph neural networks for wireless communications,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T21:10:19.884254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5041ccc-24a0-4722-968e-d9173a3ce6a4 · outbound

This paper cites Scalable multi- user precoding and pilot optimization with graph neural networks,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Scalable multi- user precoding and pilot optimization with graph neural networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:10:20.234836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 07b25adc-4688-421e-b613-615db3a44c59 · outbound

This paper cites Learning power allocation for multi-cell-multi- user systems with heterogeneous graph neural network,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Learning power allocation for multi-cell-multi- user systems with heterogeneous graph neural network,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:10:20.213474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d14b038f-1587-4525-9bc7-ca29b855527b · outbound

This paper cites Scalable spatial and geometric learning approach for joint power control and channel allocation,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Scalable spatial and geometric learning approach for joint power control and channel allocation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:10:20.194697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b67950e6-f68c-4354-bc81-0f66763ae1dd · outbound

This paper cites An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:10:20.174586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7d31aeac-5c12-4ef1-b732-dd339c1ba93c · outbound

This paper cites Optimal multiuser trans- mit beamforming: A difficult problem with a simple solution structure,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Optimal multiuser trans- mit beamforming: A difficult problem with a simple solution structure,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T21:10:19.910169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9fd7a5e-3056-42a5-89c7-50bd681e8b20 · outbound

This paper cites Learning resource allocation policy: Vertex-GNN or Edge-GNN?.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Learning resource allocation policy: Vertex-GNN or Edge-GNN?

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:10:20.141051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 55ea4597-6ba0-4705-9f73-cc9d70d4d357 · outbound

This paper cites Equivariance through parameter-sharing,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Equivariance through parameter-sharing,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:10:20.122192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ace484c4-a50c-4dc4-bdeb-6bceba112104 · outbound

This paper cites Recursive GNNs for learning precoding policies with size-generalizability,.

Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems Recursive GNNs for learning precoding policies with size-generalizability,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T21:10:19.927542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 5fd40d6c-1143-408b-aa11-796e81079f43 · inbound

Optimal Beamforming for Multi-User Continuous Aperture Array (CAPA) Systems cites this paper.

Optimal Beamforming for Multi-User Continuous Aperture Array (CAPA) Systems Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:49:02.395085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b1ce7690-8812-4ea4-b823-5a110a1aeac0 · inbound

Mutual Coupling in Continuous Aperture Arrays: Physical Modeling and Beamforming Design cites this paper.

Mutual Coupling in Continuous Aperture Arrays: Physical Modeling and Beamforming Design Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T22:24:10.744989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.