Paper Citation Record · LEDGER

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder

As of 20 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2504.20777.

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

pith.paper-citation-record.v1
2504.20777 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:26:02.057378Z

measured 51 of 51 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T01:09:14.747643Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T01:09:17.082069Z

Reference resolution

50 of 50 outbound references displayed

verified exact1
verified fuzzy37
unresolved12
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fc56bbee-6d9d-4977-8015-d9a35fd118ed · outbound

This paper cites An overview of signal processing techniques for millimeter wave MIMO systems,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder An overview of signal processing techniques for millimeter wave MIMO systems,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T05:26:01.853884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4f254a1-f8aa-45e1-9897-136057cb173a · outbound

This paper cites MIMO-OFDM wireless systems: basics, perspectives, and challenges,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder MIMO-OFDM wireless systems: basics, perspectives, and challenges,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.662770Z

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 b40dcb8f-11b7-4a92-8806-dc50c48c7e51 · outbound

This paper cites A survey on MIMO-OFDM systems: Review of recent trends,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder A survey on MIMO-OFDM systems: Review of recent trends,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.652815Z

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 49486ebf-0005-4edf-9319-5df2330e8943 · outbound

This paper cites Exploiting burst-sparsity in massive MIMO with partial channel support information,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Exploiting burst-sparsity in massive MIMO with partial channel support information,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.642089Z

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 29bd97e6-b6a6-4951-a26d-6090f5cf8a55 · outbound

This paper cites Joint burst LASSO for sparse channel estimation in multi-user massive MIMO,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Joint burst LASSO for sparse channel estimation in multi-user massive MIMO,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.632124Z

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 75d1c924-287e-471e-8011-8a7b1536f50a · outbound

This paper cites Downlink channel estimation in multiuser massive MIMO with hidden Markovian sparsity,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Downlink channel estimation in multiuser massive MIMO with hidden Markovian sparsity,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T05:26:01.877889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f2fb740-f7ae-437d-b481-72eebcad3d58 · outbound

This paper cites Dahlman, S.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Dahlman, S

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T05:26:01.881357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 385e03fd-c158-4dc2-af36-22c03b556d05 · outbound

This paper cites Deterministic pilot de- sign for sparse channel estimation in MISO/multi-user OFDM systems,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Deterministic pilot de- sign for sparse channel estimation in MISO/multi-user OFDM systems,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.608220Z

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 cf027f7b-77f8-48c3-9719-8fa633954afa · outbound

This paper cites Adaptive pilot allocation for estimating sparse uplink MU-MIMO- OFDM channels,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Adaptive pilot allocation for estimating sparse uplink MU-MIMO- OFDM channels,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.597077Z

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 b0b4494f-f103-4d67-9e24-d6f44ea2d22e · outbound

This paper cites Optimized pilot placement for sparse channel estimation in OFDM systems,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Optimized pilot placement for sparse channel estimation in OFDM systems,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.585237Z

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 5943948a-7e70-46c9-aae9-49010b075be1 · outbound

This paper cites An efficient pilot design scheme for sparse channel estimation in OFDM systems,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder An efficient pilot design scheme for sparse channel estimation in OFDM systems,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.573284Z

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 18552fbb-6fb8-4e73-8375-d55ca2a40eb5 · outbound

This paper cites Channel estimation for wideband mmWave MIMO OFDM system exploiting block sparsity,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Channel estimation for wideband mmWave MIMO OFDM system exploiting block sparsity,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.561811Z

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 a1c79905-d3de-4fd6-96ea-7d1b768e5d37 · outbound

This paper cites Bayesian learning aided simultaneous row and group sparse channel estimation in orthogonal time frequency space modulated MIMO systems,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Bayesian learning aided simultaneous row and group sparse channel estimation in orthogonal time frequency space modulated MIMO systems,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.550668Z

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 bd7c9cab-3248-48ba-a253-b9b627a30ca4 · outbound

This paper cites Channel estimation and localization for mmwave systems: A sparse bayesian learning approach,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Channel estimation and localization for mmwave systems: A sparse bayesian learning approach,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.538531Z

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 7a9bcf04-bb30-4b82-895b-992bd17ced60 · outbound

This paper cites Massive MIMO-OFDM channel estimation via structured turbo compressed sensing,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Massive MIMO-OFDM channel estimation via structured turbo compressed sensing,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.524721Z

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 cb94678d-ea3d-410e-ad5f-203d86580703 · outbound

This paper cites Weighted sum-rate maximization using weighted MMSE for MIMO- BC beamforming design,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Weighted sum-rate maximization using weighted MMSE for MIMO- BC beamforming design,

Reference 16

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Source-reported events for the cited work

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Observation fc31b653-78e0-404b-8744-ca34cb2bf3e2 · outbound

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

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T05:26:01.919278Z

Source-reported events for the cited work

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Observation 0410f3ca-aea4-4c77-b71c-9df0dce5d3c4 · outbound

This paper cites Perahia and R.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Perahia and R

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.502399Z

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 b6e607e3-f52f-4136-8334-929ba8694c15 · outbound

This paper cites Beamforming techniques for massive MIMO systems in 5G: overview, classification, and trends for future research,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Beamforming techniques for massive MIMO systems in 5G: overview, classification, and trends for future research,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.494378Z

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 e2eab8d8-6900-44a9-8838-58fbabb2aa6d · outbound

This paper cites Advancing 5G connectivity: a comprehensive review of MIMO anten- nas for 5G applications,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Advancing 5G connectivity: a comprehensive review of MIMO anten- nas for 5G applications,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.485350Z

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 859f5a20-061c-4f7a-bafc-0ae3a0287b19 · outbound

This paper cites Limited feedback-based block diagonal- ization for the MIMO broadcast channel,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Limited feedback-based block diagonal- ization for the MIMO broadcast channel,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.473795Z

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 44cd4748-6148-40f9-8c8e-7599839286a5 · outbound

This paper cites Generalized channel inversion methods for multiuser MIMO systems,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Generalized channel inversion methods for multiuser MIMO systems,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.462449Z

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 bb2433a4-50ed-43f8-8d93-2ae91ee6c209 · outbound

This paper cites Linear transmit processing in MIMO communication systems,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Linear transmit processing in MIMO communication systems,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.451190Z

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 1ca5adfa-dd6e-4a83-8899-a6150a7dc7c3 · outbound

This paper cites Channel quantization for block diagonalization with limited feedback in multiuser MIMO downlink channels,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Channel quantization for block diagonalization with limited feedback in multiuser MIMO downlink channels,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.439023Z

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 c8dac0ee-c292-487a-a037-11941553e2ff · outbound

This paper cites Robust MMSE beamforming for multiuser MISO systems with limited feedback,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Robust MMSE beamforming for multiuser MISO systems with limited feedback,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.428472Z

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 5bf9559f-fab3-4f90-9664-abca2166f896 · outbound

This paper cites Robust transceiver optimization in downlink multiuser MIMO systems,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Robust transceiver optimization in downlink multiuser MIMO systems,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.418103Z

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 30fae6ec-b902-4cf3-a68c-4af0ad8f07a0 · outbound

This paper cites Multiple antenna MMSE based downlink precoding with quantized feedback or channel mismatch,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Multiple antenna MMSE based downlink precoding with quantized feedback or channel mismatch,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.407419Z

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 8a3e4dda-d562-4af7-bda4-1ede49476b85 · outbound

This paper cites Robust sum rate maximization in the multi-cell MU-MIMO downlink,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Robust sum rate maximization in the multi-cell MU-MIMO downlink,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.396710Z

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 2aa95dab-06eb-454c-bbd9-cb6c1a989927 · outbound

This paper cites Sub- band versus space-delay precoding for wideband mmWave channels,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Sub- band versus space-delay precoding for wideband mmWave channels,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.386374Z

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 61f71d0c-20eb-4cfc-8f12-60f312261ead · outbound

This paper cites Cross-subcarrier precoder design for massive MIMO-OFDM downlink,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Cross-subcarrier precoder design for massive MIMO-OFDM downlink,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.376397Z

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 ad2a8e2d-572e-4c33-8cd1-60c1142050e2 · outbound

This paper cites Deep learning for distributed channel feedback and multiuser precoding in FDD massive MIMO,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Deep learning for distributed channel feedback and multiuser precoding in FDD massive MIMO,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.366118Z

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 43456d57-b7f4-4e34-a7ea-4cba8b6918e0 · outbound

This paper cites Deep learning-based limited feedback designs for MIMO systems,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Deep learning-based limited feedback designs for MIMO systems,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.355102Z

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 101bb0d9-1970-42f7-a0eb-e99021916ad5 · outbound

This paper cites Deep learning- based hybrid precoding for FDD massive MIMO-OFDM systems with a limited pilot and feedback overhead,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Deep learning- based hybrid precoding for FDD massive MIMO-OFDM systems with a limited pilot and feedback overhead,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.341861Z

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 9724cbd9-f38c-493e-9a09-3584e172ac68 · outbound

This paper cites A deep learning-based framework for low complexity multiuser MIMO precoding design,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder A deep learning-based framework for low complexity multiuser MIMO precoding design,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.329072Z

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 4224fecf-0598-4891-8754-66dfa2685c6f · outbound

This paper cites Deep learning for channel sensing and hybrid precoding in TDD massive MIMO OFDM systems,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Deep learning for channel sensing and hybrid precoding in TDD massive MIMO OFDM systems,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.316266Z

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 e43ca2bc-44b0-4c0f-8ffc-ba4a2b39c423 · outbound

This paper cites Deep unfolding of the weighted MMSE beamforming algorithm.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Deep unfolding of the weighted MMSE beamforming algorithm

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-16T05:26:02.158188Z

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 f8c38b01-f35c-4b90-a952-638cddcef9f3 · outbound

This paper cites Deep learning for multi-user MIMO systems: Joint design of pilot, limited feedback, and precoding,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Deep learning for multi-user MIMO systems: Joint design of pilot, limited feedback, and precoding,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.302901Z

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 ccf6af45-1723-4387-aec2-9a6d60075f00 · outbound

This paper cites Robust WMMSE precoder with deep learning design for massive MIMO,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Robust WMMSE precoder with deep learning design for massive MIMO,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.290586Z

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 4aeb0879-4ecb-4a54-9455-5b785711eb12 · outbound

This paper cites Model-driven deep learning for hybrid precoding in millimeter wave MU-MIMO system,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Model-driven deep learning for hybrid precoding in millimeter wave MU-MIMO system,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.275498Z

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 59bba19f-a359-4683-9db3-eed8d52cb11b · outbound

This paper cites Two- timescale end-to-end learning for channel acquisition and hybrid pre- coding,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Two- timescale end-to-end learning for channel acquisition and hybrid pre- coding,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.256579Z

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 5d71cc30-8b98-4c04-9b94-f728fd017b95 · outbound

This paper cites Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-16T05:26:02.019857Z

Source-reported events for the cited work

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Observation 1c100237-0ee2-4802-b815-741b427cb3b6 · outbound

This paper cites Simplified spatial correlation models for clustered MIMO channels with different array configura- tions,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Simplified spatial correlation models for clustered MIMO channels with different array configura- tions,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.233971Z

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 0bb83cdd-1e99-4bdf-86ad-24943a0b2ce5 · outbound

This paper cites Sionna: An Open-Source Library for Next-Generation Physical Layer Research.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Sionna: An Open-Source Library for Next-Generation Physical Layer Research

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-16T05:26:02.030247Z

Source-reported events for the cited work

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Observation 82b65ab1-9286-4b08-9782-c987fb550f6f · outbound

This paper cites Robust deep learning for uplink channel estimation in cellular network under inter-cell interference,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Robust deep learning for uplink channel estimation in cellular network under inter-cell interference,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.220225Z

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 849b9035-1af9-488c-979d-e8047b2f4cb4 · outbound

This paper cites Auto-Encoding Variational Bayes.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Auto-Encoding Variational Bayes

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-16T05:26:02.038694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68ecb10a-d4a3-47f6-aff0-0b5c417b6f13 · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Categorical Reparameterization with Gumbel-Softmax

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-16T05:26:02.042551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ef97a49-4d1a-42b0-bb55-589146ee3c33 · outbound

This paper cites TensorFlow: Large-scale machine learning on heterogeneous systems,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder TensorFlow: Large-scale machine learning on heterogeneous systems,

Reference 47

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Observation 3852240c-b79a-4703-ae9b-c1e9af0e5228 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Adam: A Method for Stochastic Optimization

Reference 48

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Observation 6c9e5111-b94f-4089-a3aa-d87f5c4a4768 · outbound

This paper cites Signal recovery from random mea- surements via orthogonal matching pursuit,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder Signal recovery from random mea- surements via orthogonal matching pursuit,

Reference 49

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Observation 654be2f8-5747-4115-9060-c989e77ad7e4 · outbound

This paper cites LASSO regression,.

Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder LASSO regression,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:26:02.180483Z

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Pith citing papers

Observation 8ba28d7c-e8a3-4a2e-87ec-3be046f18697 · inbound

DMRS-Based Uplink Channel Estimation for MU-MIMO Systems with Location-Specific SCSI Acquisition cites this paper.

DMRS-Based Uplink Channel Estimation for MU-MIMO Systems with Location-Specific SCSI Acquisition Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-07T01:09:17.159824Z

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