Paper Citation Record · LEDGER

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach

As of 4 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2604.21585.

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

pith.paper-citation-record.v1
2604.21585 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-09T20:43:27.488476Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+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

33 of 33 outbound references displayed

verified exact1
verified fuzzy31
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3f61e06b-124b-4f77-9b11-733a436d1ffc · outbound

This paper cites Aligning Beam with Imbalanced Multi-modality: A Generative Federated Learning Approach.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Aligning Beam with Imbalanced Multi-modality: A Generative Federated Learning Approach

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.790903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5a146d32-00a1-448b-a2e2-13fa6d7ce8b2 · outbound

This paper cites Millimeter-Wave Vehicular Communication to Support Massive Automotive Sensing.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Millimeter-Wave Vehicular Communication to Support Massive Automotive Sensing

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.724414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation dfe46089-3055-4547-8401-794b94ec1564 · outbound

This paper cites Millimeter-wave beamforming as an enabling technology for 5G cellular communications: theoretical feasibility and prototype results.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Millimeter-wave beamforming as an enabling technology for 5G cellular communications: theoretical feasibility and prototype results

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.800498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c1250cf7-c33b-4a87-9321-036e32c75fd7 · outbound

This paper cites Estimating Doubly-Selective Chan- nels for Hybrid mmWave Massive MIMO Systems: A Doubly-Sparse Approach.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Estimating Doubly-Selective Chan- nels for Hybrid mmWave Massive MIMO Systems: A Doubly-Sparse Approach

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.746130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 86115901-a325-41cc-8c60-da2c9525fcc5 · outbound

This paper cites Integrated Sensing and Commun. (ISAC) for Vehicular Communication Networks (VCN).

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Integrated Sensing and Commun. (ISAC) for Vehicular Communication Networks (VCN)

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.794153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8017ec77-5a35-4aca-98b7-6edc50ab73e8 · outbound

This paper cites Linear transmit processing in MIMO communications systems.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Linear transmit processing in MIMO communications systems

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.713346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8bf09ce6-9ee4-4f36-8219-b7e273d8e01f · outbound

This paper cites A Matrix-Inverse-Free Implementation of the MU-MIMO WMMSE Beamforming Algorithm.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach A Matrix-Inverse-Free Implementation of the MU-MIMO WMMSE Beamforming Algorithm

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.743096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c52716df-3e95-4891-8ea8-860dbf27b6d5 · outbound

This paper cites The Roadmap to 6G: AI Empowered Wireless Networks.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach The Roadmap to 6G: AI Empowered Wireless Networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.780493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4a8178f1-b569-47e7-965a-d44a54df20f6 · outbound

This paper cites Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.784074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5f420aac-4f6f-4ce3-bf1b-db0a7a05c8ea · outbound

This paper cites Learning User Scheduling and Hybrid Precoding with Sequential Graph Neural Network.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Learning User Scheduling and Hybrid Precoding with Sequential Graph Neural Network

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.756492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0cfe228d-89dc-47da-b78e-39dedb7bde78 · outbound

This paper cites Improving Beam Alignment Accuracy in mmWave Communication Systems With Auxiliary Tasks.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Improving Beam Alignment Accuracy in mmWave Communication Systems With Auxiliary Tasks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.787605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation eed498a2-3c10-4b85-92c7-26de87f8418d · outbound

This paper cites Energy-Efficient and Intelligent ISAC in V2X Networks with Spiking Neural Networks-Driven DRL.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Energy-Efficient and Intelligent ISAC in V2X Networks with Spiking Neural Networks-Driven DRL

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.763109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5404fd2a-f137-4b4d-9de3-cfe07a573576 · outbound

This paper cites Scenario-Adaptive Meta- Learning for mmWave Beam Alignment.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Scenario-Adaptive Meta- Learning for mmWave Beam Alignment

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.710443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 78ab3c25-2c7b-448f-9ee3-6a467fdddcc4 · outbound

This paper cites Integrated Sensing and Communications Toward Proactive Beamforming in mmWave V2I via Multi-Modal Feature Fusion (MMFF).

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Integrated Sensing and Communications Toward Proactive Beamforming in mmWave V2I via Multi-Modal Feature Fusion (MMFF)

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.759805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 917ce55b-ce53-443b-80f1-f061c392b290 · outbound

This paper cites Advancing Multi- Modal Beam Prediction with Cross-Modal Feature Enhancement and Dynamic Fusion Mechanism.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Advancing Multi- Modal Beam Prediction with Cross-Modal Feature Enhancement and Dynamic Fusion Mechanism

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.749869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7e2dad2d-8c1c-49e6-99f4-632e30e8356f · outbound

This paper cites Overhead-Free Blockage Detection and Precoding Through Physics-Based Graph Neural Networks: LIDAR Data Meets Ray Tracing.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Overhead-Free Blockage Detection and Precoding Through Physics-Based Graph Neural Networks: LIDAR Data Meets Ray Tracing

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.772593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5d9518d1-9444-4bab-accc-10e1ccc8edc9 · outbound

This paper cites Multimodal Visual Image Based User Association and Beamforming Using Graph Neural Networks.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Multimodal Visual Image Based User Association and Beamforming Using Graph Neural Networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.739700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 050e2423-f7f3-4e88-be1a-d9fc3ac5da11 · outbound

This paper cites SoM-Aided Online FDD Precoding via Heterogenous Multi-Modal Sensing: A Vertical Federated Learning Approach.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach SoM-Aided Online FDD Precoding via Heterogenous Multi-Modal Sensing: A Vertical Federated Learning Approach

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.797334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9483a772-5f65-49df-93c1-e0ae6d70e017 · outbound

This paper cites Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.704326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ae9cbb52-c777-437a-b191-b2a7534261a8 · outbound

This paper cites Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.727462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 24ae081f-88fe-4569-ad73-2c7f156bf198 · outbound

This paper cites FLASH- and-Prune: F ederated L earning for A utomated S election of H igh-Band mmWave Sectors using Model Pruning.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach FLASH- and-Prune: F ederated L earning for A utomated S election of H igh-Band mmWave Sectors using Model Pruning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.719089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a7a48556-df92-4248-8e9c-2298680a205e · outbound

This paper cites FedAttention: Fed- erated Attention-Based Fusion Learning for Multi-Modal Beamforming in IoV.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach FedAttention: Fed- erated Attention-Based Fusion Learning for Multi-Modal Beamforming in IoV

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.733136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1fa2e9c3-b929-4685-96f2-0729f6649761 · outbound

This paper cites On-the-Fly Modulation for Balanced Multimodal Learning.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach On-the-Fly Modulation for Balanced Multimodal Learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.707368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f2d152bc-1cbd-4c9e-b744-90813cc77b24 · outbound

This paper cites Learning End-to-End Hybrid Precoding for Multi-User mmWave Mobile System With GNNs.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Learning End-to-End Hybrid Precoding for Multi-User mmWave Mobile System With GNNs

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.803615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7c95a368-d094-4d74-b1ac-5f9c63c4a852 · outbound

This paper cites ZeroQ: A Novel Zero Shot Quantization Framework.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach ZeroQ: A Novel Zero Shot Quantization Framework

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.775647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3ac5ceac-0fdb-4b9a-857c-0c629b990f1f · outbound

This paper cites FLASH: F ederated Learning for A utomated S election of H igh-band mmWave Sectors.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach FLASH: F ederated Learning for A utomated S election of H igh-band mmWave Sectors

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.736491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 69ef2959-ba0a-4506-b336-f45d4d30474e · outbound

This paper cites Channel Modeling Aided Dataset Generation For AI-Enabled CSI Feedback: Advances, Challenges, and Solutions.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Channel Modeling Aided Dataset Generation For AI-Enabled CSI Feedback: Advances, Challenges, and Solutions

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.769098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1d3635b2-ec9c-43f9-806d-b2c97967de14 · outbound

This paper cites Communication-Efficient Learning of Deep Networks from Decentralized Data.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Communication-Efficient Learning of Deep Networks from Decentralized Data

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:01:06.456909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 88c63c9e-0987-45f6-992f-50f5bd7730a2 · outbound

This paper cites Multiverse at the Edge: Interacting Real World and Digital Twins for Wireless Beamforming.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Multiverse at the Edge: Interacting Real World and Digital Twins for Wireless Beamforming

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.806708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b0c258ca-0150-4514-80f9-04004a42e0a7 · outbound

This paper cites Beam Alignment and Tracking for Autonomous Vehicular Communication using IEEE 802.11ad-based Radar.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Beam Alignment and Tracking for Autonomous Vehicular Communication using IEEE 802.11ad-based Radar

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.730343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ff3eb49e-e817-4711-8eb9-9452fcf9060f · outbound

This paper cites On the Bures–Wasserstein distance between positive definite matrices.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach On the Bures–Wasserstein distance between positive definite matrices

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.753181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b58bdfa8-2627-4164-bdc2-3c2eea5220df · outbound

This paper cites Statistical Aspects of Wasserstein Distances.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Statistical Aspects of Wasserstein Distances

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T09:06:07.716017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5073bdec-126c-485e-b138-41624b498cba · outbound

This paper cites an unresolved cited work.

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-05-24T09:06:07.766086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

No inbound Pith citation observations are available.