Paper Citation Record · LEDGER

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers

As of 21 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2506.20248.

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

pith.paper-citation-record.v1
2506.20248 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:59:31.684624Z

measured 26 of 26 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 2026-08-06T17:46:28.387011Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T17:46:30.090771Z

Reference resolution

25 of 25 outbound references displayed

verified exact0
verified fuzzy24
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 67321059-0f39-4ff3-b64a-06dd4420b1de · outbound

This paper cites An introduction to deep learning for the physical layer,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers An introduction to deep learning for the physical layer,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T22:59:31.616119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 505c5b9e-c5fa-48f9-8995-12daf895264d · outbound

This paper cites 3GPP TR 38.843 – Study on Artificial Intelligence (AI)/Machine Learning (ML) for NR air interface,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers 3GPP TR 38.843 – Study on Artificial Intelligence (AI)/Machine Learning (ML) for NR air interface,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.939267Z

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.

Observation bd941626-5fe3-4877-a1d6-ca0eba8b551d · outbound

This paper cites 3GPP TR 38.744 V1.0.0: Study on Artificial Intelligence (AI)/Machine Learning (ML) for mobility in NR,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers 3GPP TR 38.744 V1.0.0: Study on Artificial Intelligence (AI)/Machine Learning (ML) for mobility in NR,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.930198Z

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.

Observation c250af1d-65c3-45c6-b131-7e3b9c8d8558 · outbound

This paper cites Machine Learning-enhanced Receive Processing for MU-MIMO OFDM Sys- tems,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers Machine Learning-enhanced Receive Processing for MU-MIMO OFDM Sys- tems,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.919904Z

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.

Observation 451ea967-178f-4405-a042-b6191e39adf7 · outbound

This paper cites DeepRx MIMO: Convolutional MIMO Detection with Learned Multiplicative Transformations,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers DeepRx MIMO: Convolutional MIMO Detection with Learned Multiplicative Transformations,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.910558Z

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.

Observation bbe225b6-5225-4f99-99cc-a06f978ba143 · outbound

This paper cites Adaptive NN-based OFDM Receivers: Computational Complexity vs. Achievable Performance,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers Adaptive NN-based OFDM Receivers: Computational Complexity vs. Achievable Performance,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.900568Z

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.

Observation d6231466-ed83-4a65-a195-274749483f49 · outbound

This paper cites iDeepRx Enabled 100 Gb/s DFT-s-OFDM Data Transmission Over 220 GHz Testbed,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers iDeepRx Enabled 100 Gb/s DFT-s-OFDM Data Transmission Over 220 GHz Testbed,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.891397Z

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.

Observation 57885922-fe7b-400c-a273-5330a2e30673 · outbound

This paper cites DeepRx: Fully convolu- tional deep learning receiver,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers DeepRx: Fully convolu- tional deep learning receiver,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.881324Z

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.

Observation 6461cb08-2371-40c1-99cf-fb4c978de6e7 · outbound

This paper cites Hybrid- deeprx: Deep learning receiver for high-evm signals,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers Hybrid- deeprx: Deep learning receiver for high-evm signals,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.871014Z

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.

Observation 8965f382-96fb-432f-9f3f-b8147f78dcf0 · outbound

This paper cites A Neural Receiver for 5G NR Multi-User MIMO,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers A Neural Receiver for 5G NR Multi-User MIMO,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.861088Z

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.

Observation 7f92088d-d425-4258-91ab-ccb81a1e377b · outbound

This paper cites End-to-End Learning for OFDM: From Neural Receivers to Pilotless Communication,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers End-to-End Learning for OFDM: From Neural Receivers to Pilotless Communication,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.852066Z

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.

Observation 25bfdcd3-a36d-47ba-b438-2d547f8e7646 · outbound

This paper cites Waveform Learning for Next-Generation Wireless Communica- tion Systems,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers Waveform Learning for Next-Generation Wireless Communica- tion Systems,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.843162Z

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.

Observation aea0eb67-174a-4c2c-9afd-5644a7c6a520 · outbound

This paper cites End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraints,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraints,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.833601Z

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.

Observation b09c0708-8904-4920-bbee-f40a7fcdb8dc · outbound

This paper cites Deep learning-based pilotless spatial multiplexing,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers Deep learning-based pilotless spatial multiplexing,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.823625Z

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.

Observation 8599fc5f-f659-46a8-9146-24844feae672 · outbound

This paper cites End-to-end waveform learning through joint optimization of pulse and constellation shaping,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers End-to-end waveform learning through joint optimization of pulse and constellation shaping,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.813385Z

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.

Observation d59b267b-5841-4573-8b5c-0eacb93eaf76 · outbound

This paper cites End-to-End Learning for Uplink MU- SIMO Joint Transmitter and Non-Coherent Receiver Design in Fading Channels,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers End-to-End Learning for Uplink MU- SIMO Joint Transmitter and Non-Coherent Receiver Design in Fading Channels,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.803205Z

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.

Observation 7d5f3b5b-9229-4093-8251-8b73a38dc611 · outbound

This paper cites Partial-Data Superimposed Training With Data Precoding for OFDM Systems,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers Partial-Data Superimposed Training With Data Precoding for OFDM Systems,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.793827Z

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.

Observation 5c885de6-f207-46cd-9bb3-a91ae0084190 · outbound

This paper cites Superimposed Versus Regular Pilots for Hardware Impaired Rician- Faded Cell-Free Massive MIMO Systems,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers Superimposed Versus Regular Pilots for Hardware Impaired Rician- Faded Cell-Free Massive MIMO Systems,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.784647Z

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.

Observation 08f32d44-e1fb-4a0e-92a3-fcc24db335ce · outbound

This paper cites Superimposed Pilot Transmission in Cell-Free Massive MIMO With Non-Ideal RF Responses,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers Superimposed Pilot Transmission in Cell-Free Massive MIMO With Non-Ideal RF Responses,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.775816Z

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.

Observation 857aa579-27b9-471c-92c0-2a3d2a066dc5 · outbound

This paper cites AI-Driven Iterative Receiver for Superimposed Pilot Schemes in MIMO-OFDM Systems,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers AI-Driven Iterative Receiver for Superimposed Pilot Schemes in MIMO-OFDM Systems,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.765830Z

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.

Observation dda99084-d36c-423d-b287-39f263f2779c · outbound

This paper cites Interference cancellation based neural receiver for superimposed pilot in multi-layer transmission,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers Interference cancellation based neural receiver for superimposed pilot in multi-layer transmission,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.756126Z

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.

Observation dfa16aa5-7d61-49fb-b107-4c6acff68313 · outbound

This paper cites Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learning,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.746680Z

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.

Observation 5e764bcc-2196-403d-bb94-627e1ac3b1f8 · outbound

This paper cites Deep Learn- ing OFDM Receivers for Improved Power Efficiency and Coverage,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers Deep Learn- ing OFDM Receivers for Improved Power Efficiency and Coverage,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.736886Z

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.

Observation 526d2883-7d17-438d-a932-0078282395b3 · outbound

This paper cites Detection of Impaired OFDM Waveforms Using Deep Learning Receiver,.

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers Detection of Impaired OFDM Waveforms Using Deep Learning Receiver,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.726937Z

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.

Observation fff28b42-c66c-4c9e-9502-48d62be0be69 · outbound

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

Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers Sionna: An Open-Source Library for Next-Generation Physical Layer Research,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:59:31.716544Z

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.

Pith citing papers

Observation 76b040d7-9f20-498b-914c-1c556d25ea07 · inbound

Learning-Aided Iterative Receiver for Superimposed Pilots: Design and Experimental Evaluation cites this paper.

Learning-Aided Iterative Receiver for Superimposed Pilots: Design and Experimental Evaluation Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:46:30.095261Z

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.