Paper Citation Record · LEDGER

Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling

As of 22 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2608.04155.

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

pith.paper-citation-record.v1
2608.04155 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T00:27:40.735798Z

measured 20 of 20 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

20 of 20 outbound references displayed

verified exact2
verified fuzzy15
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ec754e6a-67c9-45a6-99fe-36a406d945f4 · outbound

This paper cites Faster-than-Nyquist signalling,.

Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling Faster-than-Nyquist signalling,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:27:41.207539Z

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 44867e86-aab6-4a87-b380-e9ca13006471 · outbound

This paper cites Optimal decoding of linear codes for minimizing symbol error rate,.

Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling Optimal decoding of linear codes for minimizing symbol error rate,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:27:41.194080Z

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 d971ad05-df8e-47ba-9f59-0b1b6cf6b3ef · outbound

This paper cites Reduced-complexity receivers for strongly narrow- band intersymbol interference introduced by faster-than-Nyquist signaling,.

Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling Reduced-complexity receivers for strongly narrow- band intersymbol interference introduced by faster-than-Nyquist signaling,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:27:41.180704Z

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 907ce2bf-7131-44ca-a818-7d3ff155f7c6 · outbound

This paper cites Frequency-domain equalization of faster-than-Nyquist signaling,.

Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling Frequency-domain equalization of faster-than-Nyquist signaling,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:27:41.167482Z

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 2f3ff7e7-074a-4f6b-89e9-da5a53c93780 · outbound

This paper cites A very low complexity suc- cessive symbol-by-symbol sequence estimator for faster-than-Nyquist signaling,.

Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling A very low complexity suc- cessive symbol-by-symbol sequence estimator for faster-than-Nyquist signaling,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:27:41.153392Z

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 0eea34e2-7e1b-4dfd-b5fa-637f259dedc5 · outbound

This paper cites Reduced-complexity equalization for faster-than-Nyquist signaling: New methods based on Ungerboeck observation model,.

Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling Reduced-complexity equalization for faster-than-Nyquist signaling: New methods based on Ungerboeck observation model,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:27:41.138209Z

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 4fc461a1-fc66-4610-8b47-111dfe66c280 · outbound

This paper cites Deep learning-based signal detection technique for FTN signaling-based emergency alert communication system,.

Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling Deep learning-based signal detection technique for FTN signaling-based emergency alert communication system,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:27:41.123452Z

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 3f168d63-a43e-4ec0-b8e1-c52caec1c1d8 · outbound

This paper cites Receiver design for faster-than-Nyquist signaling: Deep-learning-based architectures,.

Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling Receiver design for faster-than-Nyquist signaling: Deep-learning-based architectures,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:27:41.108075Z

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 7c7ba561-a8f5-43df-8447-ec061db3aa70 · outbound

This paper cites A deep learning-aided detection method for FTN-based NOMA,.

Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling A deep learning-aided detection method for FTN-based NOMA,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:27:41.090662Z

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 8be42ba2-b59c-46f4-8b76-3784a0ae1dc9 · outbound

This paper cites Deep learning-based list sphere decoding for faster-than- Nyquist (FTN) signaling detection,.

Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling Deep learning-based list sphere decoding for faster-than- Nyquist (FTN) signaling detection,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:27:41.073762Z

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 bcaf3554-3df5-4cc6-a173-22ab42746799 · outbound

This paper cites FTN-based non-orthogonal signal detection technique with machine learning in quasi-static multipath channel,.

Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling FTN-based non-orthogonal signal detection technique with machine learning in quasi-static multipath channel,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:27:41.057709Z

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 6d1fd39d-7e98-460e-b4c4-d74b60beb2c0 · outbound

This paper cites A novel sum-product detection algorithm for faster-than-Nyquist signaling: A deep learning approach,.

Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling A novel sum-product detection algorithm for faster-than-Nyquist signaling: A deep learning approach,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:27:41.042374Z

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 41fdc78d-b511-4972-9a50-635f4fdf197a · outbound

This paper cites GRU- Based Sequence Detection for Faster-than-Nyquist Signaling,.

Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling GRU- Based Sequence Detection for Faster-than-Nyquist Signaling,

Reference 13

Resolution: metadata mismatch; raw_fallback, observed 2026-08-08T00:27:40.970364Z

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 913a0e9f-c39a-42cd-a165-f063148c8490 · outbound

This paper cites Faster-than-Nyquist equaliza- tion with convolutional neural networks,.

Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling Faster-than-Nyquist equaliza- tion with convolutional neural networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:27:41.027412Z

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 4408f2bd-cf09-4668-b3ab-c56734f1780a · outbound

This paper cites A novel CNN-based standalone detector for faster-than-Nyquist signaling,.

Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling A novel CNN-based standalone detector for faster-than-Nyquist signaling,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:27:41.012256Z

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 f6c4840c-a423-4525-b998-217b54071a66 · outbound

This paper cites Finding structure in time,.

Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling Finding structure in time,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-08T00:27:40.715944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bdda31c-b5dc-4f42-bac0-0e88e2a3123e · outbound

This paper cites A novel domain- aware CNN architecture for faster-than-Nyquist signaling detection,.

Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling A novel domain- aware CNN architecture for faster-than-Nyquist signaling detection,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T00:27:40.984368Z

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 15721438-45b4-4b35-8955-6c40081d061b · outbound

This paper cites A novel position-aware attention-enhanced Bi-GRU detector for faster-than-Nyquist signaling,.

Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling A novel position-aware attention-enhanced Bi-GRU detector for faster-than-Nyquist signaling,

Reference 18

Resolution: metadata mismatch; raw_fallback, observed 2026-08-08T00:27:40.898394Z

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 41572444-00b2-4a3a-9ea4-7e5fa35c0499 · outbound

This paper cites Self-Attention Transformer-Based Detector for Faster-than-Nyquist Signaling.

Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling Self-Attention Transformer-Based Detector for Faster-than-Nyquist Signaling

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-08T00:27:40.808456Z

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 08d76c39-a865-4240-a2ab-1afe131f1bcb · outbound

This paper cites A Comparative Analysis of MLP and Kolmogorov-Arnold Networks (KAN) for Faster-than-Nyquist (FTN) Signaling Detection.

Low-Complexity Recurrent Neural Network Detector for Faster-than-Nyquist Signaling A Comparative Analysis of MLP and Kolmogorov-Arnold Networks (KAN) for Faster-than-Nyquist (FTN) Signaling Detection

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-08T00:27:40.786424Z

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

No inbound Pith citation observations are available.