Paper Citation Record · LEDGER

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference

As of 8 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2506.00452.

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

pith.paper-citation-record.v1
2506.00452 v5

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:11:14.846672Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T16:26:01.542907Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

verified exact1
verified fuzzy31
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5374db0f-3570-4ad6-8d3e-8d243348d5fc · outbound

This paper cites 5G: A tutorial overview of standards, trials, challenges, deployment, and practice,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference 5G: A tutorial overview of standards, trials, challenges, deployment, and practice,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:20.859480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 11053940-df07-494d-b9f3-02dd479c1869 · outbound

This paper cites 5G field trials: OFDM-based wave- forms and mixed numerologies,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference 5G field trials: OFDM-based wave- forms and mixed numerologies,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:20.712155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8112ec80-7434-4833-a8f4-0191ae082d5a · outbound

This paper cites Optimized waveforms for 5G–6G communication with sensing: Theory, simulations and experiments,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Optimized waveforms for 5G–6G communication with sensing: Theory, simulations and experiments,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:20.542768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2bb924a1-1289-4cc8-95a8-8dc5c0d05ecb · outbound

This paper cites OFDM channel estimation by singular value decomposition,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference OFDM channel estimation by singular value decomposition,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:20.360812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 208792d5-99d5-4d49-8a37-f1241ff24a2e · outbound

This paper cites Pilot-symbol-aided channel estimation for OFDM in wireless systems,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Pilot-symbol-aided channel estimation for OFDM in wireless systems,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:20.172323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 853d0534-bcc7-40f3-98dd-01264dff4551 · outbound

This paper cites Channel estimation for OFDM,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Channel estimation for OFDM,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:20.028056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6752fbac-41af-407b-af7c-6b5493e09025 · outbound

This paper cites Deep learning-based channel estimation,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Deep learning-based channel estimation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:19.877320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 13cd32d4-98ee-4e6c-b715-6ad6b6f0d84c · outbound

This paper cites Deep residual learning meets OFDM channel estimation,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Deep residual learning meets OFDM channel estimation,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:19.729704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1358b667-e52e-47d1-8ac1-52261912e7fc · outbound

This paper cites Low complexity channel estimation with neural network solutions,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Low complexity channel estimation with neural network solutions,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:19.606417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0c729265-0afa-4fd0-8e68-b1abd097c241 · outbound

This paper cites High dimensional channel estimation using deep generative networks,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference High dimensional channel estimation using deep generative networks,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T12:11:12.124407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ef6cf03-4100-4a4e-9615-f2d18c076444 · outbound

This paper cites Attention Is All You Need.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Attention Is All You Need

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T12:11:12.204585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cd44502-90c1-4497-b596-c4a77ea92793 · outbound

This paper cites Wireless channel estimation based on transformer and super-resolution,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Wireless channel estimation based on transformer and super-resolution,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:19.473975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation da58978b-e8a1-4913-9ca6-2109a9f7d592 · outbound

This paper cites Channelformer: Attention based neural solution for wireless channel estimation and effective online training,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Channelformer: Attention based neural solution for wireless channel estimation and effective online training,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:19.368220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7f8d2607-df55-4498-aadf-957951fad6c6 · outbound

This paper cites Rectified linear units improve restricted boltzmann machines,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Rectified linear units improve restricted boltzmann machines,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:19.237920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c7d54e01-46be-4425-9482-45b72756ce77 · outbound

This paper cites Efficient backprop,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Efficient backprop,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:19.173305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7db40ecf-6363-44ca-8489-bd7c50107e6c · outbound

This paper cites Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs).

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T12:11:12.717077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77062db4-aeab-4371-a60d-7be0ce59a145 · outbound

This paper cites Evaluation of pooling operations in convolutional architectures for object recognition,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Evaluation of pooling operations in convolutional architectures for object recognition,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:19.156790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3ec133aa-f2c4-46a7-9f31-ce0230670ae7 · outbound

This paper cites Optimization or architecture: How to hack Kalman filtering,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Optimization or architecture: How to hack Kalman filtering,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:19.140023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9a37cd37-609a-49d4-a590-33180ee04e8f · outbound

This paper cites Leveraging deep neural networks for massive MIMO data detection,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Leveraging deep neural networks for massive MIMO data detection,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:19.038187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0a2ffcd7-7ebc-4495-b639-074d9758f795 · outbound

This paper cites Model-based deep learning,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Model-based deep learning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:18.855945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1f46e9af-d1b2-4916-883e-698bc98e1d73 · outbound

This paper cites KalmanNet: Neural network aided kalman filtering for partially known dynamics,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference KalmanNet: Neural network aided kalman filtering for partially known dynamics,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:18.631222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1f1c71f7-16f2-4e18-82ba-2afed1dcad0a · outbound

This paper cites Long short-term memory,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Long short-term memory,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T12:11:13.214852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17f68461-becb-4b5e-9abe-acf25a1cba40 · outbound

This paper cites Learning phrase representations using RNN encoder–decoder for statistical machine translation,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Learning phrase representations using RNN encoder–decoder for statistical machine translation,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:18.417904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4cf0cc1b-37af-4054-adf0-640e6117a452 · outbound

This paper cites An introduction to the Kalman filter,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference An introduction to the Kalman filter,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:18.135595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fbc60de4-ab7a-45df-96d9-e9294eb38c3f · outbound

This paper cites Split- KalmanNet: A robust model-based deep learning approach for state estimation,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Split- KalmanNet: A robust model-based deep learning approach for state estimation,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T12:11:13.433194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e4a4858-3c12-4f7b-b2ee-daf8cb5cc250 · outbound

This paper cites Kalmanformer: Using transformer to model the kalman gain in kalman filters,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Kalmanformer: Using transformer to model the kalman gain in kalman filters,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:17.694265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1dfc09ca-7910-4af6-a032-2e9c2fd9967f · outbound

This paper cites Artificial intelligence-aided Kalman filters: AI-augmented designs for Kalman- type algorithms,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Artificial intelligence-aided Kalman filters: AI-augmented designs for Kalman- type algorithms,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:17.355947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 69564d6f-bd9c-41ab-9501-9144c3aec2ca · outbound

This paper cites Energy-Efficient State Estimation with 1-Bit Sensing: A Bussgang-Kalman Framework for Internet of Things.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Energy-Efficient State Estimation with 1-Bit Sensing: A Bussgang-Kalman Framework for Internet of Things

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:11:15.101294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ecbda7af-5e3d-4f55-adfc-faff78387a5c · outbound

This paper cites Efficient MU-MIMO beamforming based on majorization-minimization and deep unfolding,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Efficient MU-MIMO beamforming based on majorization-minimization and deep unfolding,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:17.084893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 85d50f8c-e8d5-47ec-b75a-f802351585fa · outbound

This paper cites Adaptive neural signal detection for massive MIMO,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Adaptive neural signal detection for massive MIMO,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:16.873450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b2cbeefe-f68d-48aa-8955-db1aa25cbf05 · outbound

This paper cites The COST 2100 MIMO channel model,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference The COST 2100 MIMO channel model,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:16.630035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 82a6c998-895b-43bd-8dfc-a627e437497c · outbound

This paper cites Channel estimation techniques based on pilot arrangement in OFDM systems,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Channel estimation techniques based on pilot arrangement in OFDM systems,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:16.424338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 102b7728-9d3b-4d46-bffc-fe64eafbda89 · outbound

This paper cites Low-complexity 2D LMMSE channel estimation for ofdm systems,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Low-complexity 2D LMMSE channel estimation for ofdm systems,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:16.205534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d06d840e-d8e1-41fd-b0e5-f01bdc28ae6f · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Adam: A Method for Stochastic Optimization

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T12:11:14.283151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6795011-ac29-4d5e-b441-570df50ab764 · outbound

This paper cites Robust estimation of a location parameter,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Robust estimation of a location parameter,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T12:11:14.397121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36e903bd-1de8-4b10-b8f9-0a9a58805a02 · outbound

This paper cites NR; physical channels and modulation,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference NR; physical channels and modulation,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:15.913316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 796dc107-2908-4351-8549-ef547085488e · outbound

This paper cites Ha, https://github.com/TaeJun1999/Attention-aided-MMSE.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Ha, https://github.com/TaeJun1999/Attention-aided-MMSE

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:15.669882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5fcb37ad-7b71-4864-be11-998c277edea0 · outbound

This paper cites Sensing- aided channel estimation in ofdm systems by leveraging communication echoes,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Sensing- aided channel estimation in ofdm systems by leveraging communication echoes,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:15.472145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1d9fcbde-db76-499a-a165-0a057a243be6 · outbound

This paper cites Splitting messages in the dark- Rate-splitting multiple access for FDD massive MIMO without CSI feedback,.

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference Splitting messages in the dark- Rate-splitting multiple access for FDD massive MIMO without CSI feedback,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:15.337958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation 7874d84c-0a87-4836-950e-51cc95127bb0 · inbound

When 5G MIMO Scaling Breaks: Toward 6G Upper-Mid-Band Extreme MIMO cites this paper.

When 5G MIMO Scaling Breaks: Toward 6G Upper-Mid-Band Extreme MIMO Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-03T16:26:01.542907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.