Paper Citation Record · LEDGER

High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning

As of 14 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2411.12541.

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

pith.paper-citation-record.v1
2411.12541 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:27:42.992730Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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 exact0
verified fuzzy18
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bfd3cdf6-5b52-4ce5-af64-02870f739a85 · outbound

This paper cites Music Source Separation in the Waveform Domain.

High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning Music Source Separation in the Waveform Domain

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T17:27:42.865635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2bc3d858-0c34-4987-898b-9bfa4a77c70c · outbound

This paper cites A Systematic Review of EEG Source Localization Tech- niques and Their Applications on Diagnosis of Brain Abnormalities,.

High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning A Systematic Review of EEG Source Localization Tech- niques and Their Applications on Diagnosis of Brain Abnormalities,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:27:43.425789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 72fa6a3f-d914-4b27-a268-76a5ed73e77e · outbound

This paper cites Analysis of Financial Time Series Morphology with Amuse Algorithm and its Extensions,.

High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning Analysis of Financial Time Series Morphology with Amuse Algorithm and its Extensions,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:27:43.407395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3cc1de73-74ab-4ba9-a490-c1693c655464 · outbound

This paper cites Unmixing Methods Based on Nonnegativity and Weakly Mixed Pixels for Astronomical Hyperspectral Datasets,.

High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning Unmixing Methods Based on Nonnegativity and Weakly Mixed Pixels for Astronomical Hyperspectral Datasets,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:27:43.391485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1920cf81-4a72-4ffa-a0f9-0fffc0a58bcd · outbound

This paper cites A simple and practical underlay scheme for short-range secondary communication,.

High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning A simple and practical underlay scheme for short-range secondary communication,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:27:43.375809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3c6ee33a-ab96-41ef-8e4d-5087f2cc512f · outbound

This paper cites Enhanced Non-Preemptive Support of URLLC Using Spread Spectrum Underlay Signalling,.

High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning Enhanced Non-Preemptive Support of URLLC Using Spread Spectrum Underlay Signalling,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:27:43.358549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bb6854c5-5fa8-4b38-9904-104ec8c2fff5 · outbound

This paper cites Performance Analysis and Deep Learning Design of Underlay Cognitive NOMA- Based CDRT Networks with Imperfect SIC and Co-Channel Interfer- ence,.

High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning Performance Analysis and Deep Learning Design of Underlay Cognitive NOMA- Based CDRT Networks with Imperfect SIC and Co-Channel Interfer- ence,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:27:43.340819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 87a7df52-08ed-4f1d-a3d7-b296adae7725 · outbound

This paper cites A Comprehensive Survey on GNSS Interferences and the Application of Neural Networks for Anti- Jamming,.

High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning A Comprehensive Survey on GNSS Interferences and the Application of Neural Networks for Anti- Jamming,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:27:43.322762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4fe4d99e-8947-4457-b745-354567bffa1f · outbound

This paper cites A U-Net Architecture for Time-Frequency Interference Signal Separation of RF Waveforms,.

High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning A U-Net Architecture for Time-Frequency Interference Signal Separation of RF Waveforms,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:27:43.303856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 51c8f50c-a3fd-4f26-be87-ddfc25b0d645 · outbound

This paper cites Improving Data-Driven RF Signal Separation with SOI- Matched Autoencoders,.

High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning Improving Data-Driven RF Signal Separation with SOI- Matched Autoencoders,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:27:43.281324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 972fffed-5507-425a-8405-255d105afc48 · outbound

This paper cites DEMUCS for Data-Driven RF Signal Denoising,.

High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning DEMUCS for Data-Driven RF Signal Denoising,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:27:43.258677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d97de557-695e-483e-a217-e306931fe85a · outbound

This paper cites Linear Versus Non-Linear Interference Cancellation,.

High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning Linear Versus Non-Linear Interference Cancellation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:27:43.237297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1f6845da-693b-4272-ae01-bc4a53ebe0be · outbound

This paper cites Chapter 12 - Multi-User MIMO,.

High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning Chapter 12 - Multi-User MIMO,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:27:43.217040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ce3e3434-dfc2-447b-b705-8c56415c96de · outbound

This paper cites The Data-Driven Radio Frequency Signal Separation Challenge,.

High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning The Data-Driven Radio Frequency Signal Separation Challenge,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:27:43.198957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 59b64d71-e6e5-4e9d-b2b4-8ae72c15f803 · outbound

This paper cites Blind Co-Channel Interference Cancellation Using Fast Fourier Convolutions,.

High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning Blind Co-Channel Interference Cancellation Using Fast Fourier Convolutions,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:27:43.180434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6a5b5022-1aeb-479c-8ab0-f35da1cc4f1d · outbound

This paper cites ENS-Unet: End-to-End Noise Suppression U-Net for Brain Tumor Segmentation,.

High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning ENS-Unet: End-to-End Noise Suppression U-Net for Brain Tumor Segmentation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:27:43.163044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1c2574ba-acd4-4d5e-ae8e-149cf55e787b · outbound

This paper cites DNoiseNet: Deep Learning- Based Feedback Active Noise Control in Various Noisy Environments,.

High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning DNoiseNet: Deep Learning- Based Feedback Active Noise Control in Various Noisy Environments,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:27:43.144049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ca143df5-40b0-498f-9f45-1409d1c5605c · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T17:27:42.980622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d4599d7-4d49-41d6-a4ae-f481185fbf47 · outbound

This paper cites Conv-Tasnet: Surpassing Ideal Time– Frequency Magnitude Masking for Speech Separation,.

High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning Conv-Tasnet: Surpassing Ideal Time– Frequency Magnitude Masking for Speech Separation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:27:43.125022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ae6be9e0-3e32-4c99-81bc-01e7069cf876 · outbound

This paper cites A method to estimate the energy consumption of deep neural networks,.

High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning A method to estimate the energy consumption of deep neural networks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:27:43.104380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.