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Paper Citation Record · LEDGER

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

As of 21 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 2026-08-21T06:32:19.484+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

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  • verified fuzzy37
  • unresolved12
  • parse uncertain0
  • malformed identifier0
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External citation measurements

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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

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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

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source=pdf_text observed=2026-08-16T05:26:01.860179Z digest=sha256:65272afdad73f5605fc431e0f87c26c5e8c3b6e1e0484beeae4a3162fcac3c0f

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

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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

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source=pdf_text observed=2026-08-16T05:26:01.870671Z digest=sha256:82d87b9482ab9d4962cf04bd43b8e7c9d6861f0f9a72696400e941d8d64e4cdc

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

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source=pdf_text observed=2026-08-16T05:26:01.874455Z digest=sha256:e64a481347501382723a608e6b3a2f4f00d0c51eb9e4ddf3e4cd897170548614

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

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source=pdf_text observed=2026-08-16T05:26:01.877889Z digest=sha256:052a6ea9cbf38ee6117602caad8be3cd6973df396f809ed50c380cbc502c8753

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

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source=pdf_text observed=2026-08-16T05:26:01.881357Z digest=sha256:480443ad8f38e71d032023703ec19ff972c89b30971fc19330a837a2f9cba4c2

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

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source=pdf_text observed=2026-08-16T05:26:01.884688Z digest=sha256:333ef0433577593f8396c26fde990a9ce96f0793b4cea7ad5015fa08b24c0b2e

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

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source=pdf_text observed=2026-08-16T05:26:01.887758Z digest=sha256:dfa425bfa707fa4e7e3d167b927172f868e0a71579cc9face18cd26ac8734281

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

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source=pdf_text observed=2026-08-16T05:26:01.890835Z digest=sha256:927f1a7ab0a87f7c7337c08fdb9de2d74a65cbdc20170957eb59486fc5b59303

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

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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

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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

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source=pdf_text observed=2026-08-16T05:26:01.901398Z digest=sha256:b58f7025f67a1bbd20706ae41413fe230033fd24e6c1843dbbbce056c63efc60

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

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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

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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=pdf_text observed=2026-08-16T05:26:01.915947Z digest=sha256:e7a2f1269a8240cc122e9e24ad56dc0fb8fe247bd0a4e67d85a89397411eabb1

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

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source=pdf_text observed=2026-08-16T05:26:01.919278Z digest=sha256:21b023bcf1ee7dd01269994faecc891c726097e6b97eeeb294fbd3948cef6289

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

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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

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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

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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

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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

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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

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-16T05:26:01.942158Z digest=sha256:6a95a6882ae456f8545c1bee57e8b05b22abdb42de4626ee0ad9fd9ca1ae3f8d

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

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-16T05:26:01.946483Z digest=sha256:fff74ab81803e593f2fce3cd2402aed640f0581e58e101b497fd65bf97450e8a

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

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source=pdf_text observed=2026-08-16T05:26:01.951349Z digest=sha256:eebc41da2201616a8dddaf5f7ee1a9bfb6478a1daa7fb272879d854bbe82f09a

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

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-16T05:26:01.955049Z digest=sha256:881619c72a9e64687f12a000d9640649b1b9ff055dfee9c5bdd80f86050f324c

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

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-16T05:26:01.959441Z digest=sha256:1811e9966f796c7382c1af69e0a1955a60b6e39656143ad8c4944579c6c4851a

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

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-16T05:26:01.964082Z digest=sha256:9a51a9287e838ad61ed48cbcad5f58127ce328c19a90b7d220511466ac6ce6be

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

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-16T05:26:01.969051Z digest=sha256:2699c990e9623a86ce382fcd55ea50a0fbdc2d18f7cf997510aeb7223e8cda89

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

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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

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source=pdf_text observed=2026-08-16T05:26:01.978882Z digest=sha256:ee72daf1627abe32a5007d25d2604da78741b8e2dce4f93debd515dc6c0f8cb7

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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-16T05:26:01.983280Z digest=sha256:b1ed4ef3602fce91f8f435205b94b412621210824333df20057990b45d97dd74

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

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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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-16T05:26:01.987548Z digest=sha256:141d4548642ea0812a7fd8b79269b5afc08cef75fb82506be032f77cb1641c6f

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

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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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-16T05:26:01.991733Z digest=sha256:06bc8ec9e318875419f7fa4397de14d92441792fe8560d015d08cdc53c95c3ff

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

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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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-16T05:26:01.994955Z digest=sha256:e4d5ccc3e0b79089327068a8a6b0aae935166a0f67dbb62615b8441e3bae850d

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

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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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-16T05:26:01.998505Z digest=sha256:bf75d53f908bbdd2f875450e0dd1c31a05f44bfaef4c3177db73d75fd3def9ee

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

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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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-16T05:26:02.003222Z digest=sha256:5eb4b30579051561b2c6370b01d9ce2c969322d18a6ab6e2afbeed205bb6d8da

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

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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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-16T05:26:02.007845Z digest=sha256:bdf5c100388b0db788d33d52806d2fd2b3e0b4193bc3e3143c62ffd715430295

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

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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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-16T05:26:02.011967Z digest=sha256:f873bb29588ef5f780a816d8e6465ff7439595aa4daebbe9869266d406e113ce

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-16T05:26:02.016393Z digest=sha256:b8ec04092729badd1b554c449e5604a1aa7f6e37dc1aa08f15b694b8e0bc9fdb

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:26:02.019857Z digest=sha256:cb54a341e16caef81d229c82fe2c557479538a6162b3da517a4bc87e1b121f79

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

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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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-16T05:26:02.026349Z digest=sha256:95707ea42efdba8c6bad3e0dce1645a681bcf241951444bb14b1792fb74fccbf

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:26:02.030247Z digest=sha256:011e92dcb17f26ccba1814e1ea4c4598385857443ffc8b7969f8342c734fd4a5

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-16T05:26:02.034395Z digest=sha256:338187482f13870e82ea70387b93e7c61609161327ea9a91738eefc7df1cadbe

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.

source=pdf_text observed=2026-08-16T05:26:02.038694Z digest=sha256:362b1bf332bfee20c7f0040091858981e69b41b58f46640a462cd68f6076202c

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.

source=pdf_text observed=2026-08-16T05:26:02.042551Z digest=sha256:ffd72eb7ecdbd9859c050c10c355ac04c42d0f7449aff1c374647db2fe366418

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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unresolved
no resolver link, observed 2026-08-16T05:26:02.046764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:26:02.046764Z digest=sha256:4fd02ae35a76c8ee9d2b40da35b098e61ac286a7a6e2966e8d3dbd135c4aa2a7

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

Resolution
unresolved
no resolver link, observed 2026-08-16T05:26:02.050339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:26:02.050339Z digest=sha256:20039909e3897d6693bfd71736a50b69c2b863f317cc6d420c9162262c905666

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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unresolved
no resolver link, observed 2026-08-16T05:26:02.054350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:26:02.054350Z digest=sha256:c9b158dc1e0e8bc7e228190a8abd83f540d922c726d7f6a7d5e8870d15ea6753

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-16T05:26:02.057378Z digest=sha256:f554f36d863e1cfa5a3abe27e8eae6eed0388d0e47a88457b9b2dd7eb0917b19

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T01:09:14.747643Z digest=sha256:fb547e3d3e59a79441aeb877ff4e77391f805a9438f8c902ba1abec9002ac72d