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Paper Citation Record · LEDGER

Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems

As of 16 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2502.00811.

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pith.paper-citation-record.v1
2502.00811 v2

Coverage vector

measured 27 of 27 reference resolution

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Reference resolution

27 of 27 outbound references displayed

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External citation measurements

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Outbound references

Observation 6749a0c4-9869-4996-974e-9930a58f238e · outbound

This paper cites An overview of signal processing techniques for joint communication and radar sensing,.

Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems An overview of signal processing techniques for joint communication and radar sensing,

Reference 1

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This paper cites Integrated sensing and communications: Toward dual-functional wire- less networks for 6G and beyond,.

Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Integrated sensing and communications: Toward dual-functional wire- less networks for 6G and beyond,

Reference 2

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This paper cites Joint radar and communication design: Applications, state-of-the-art, and the road ahead,.

Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Joint radar and communication design: Applications, state-of-the-art, and the road ahead,

Reference 3

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This paper cites A survey on fundamental limits of integrated sensing and communication,.

Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems A survey on fundamental limits of integrated sensing and communication,

Reference 4

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Observation 4b47f8b6-0450-4658-872c-4242f80699a2 · outbound

This paper cites Distributed compressive CSIT estimation and feedback for FDD multi-user massive MIMO systems,.

Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Distributed compressive CSIT estimation and feedback for FDD multi-user massive MIMO systems,

Reference 5

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This paper cites Closed-loop autonomous pilot and compressive CSIT feedback resource adaptation in multi-user FDD massive MIMO systems,.

Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Closed-loop autonomous pilot and compressive CSIT feedback resource adaptation in multi-user FDD massive MIMO systems,

Reference 6

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This paper cites Joint pilot optimization, target detection and channel estimation for integrated sensing and communication systems,.

Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Joint pilot optimization, target detection and channel estimation for integrated sensing and communication systems,

Reference 7

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Observation 17ed4414-5592-498e-acbd-f959e3a15410 · outbound

This paper cites Joint scattering environment sensing and channel estimation based on non-stationary Markov random field,.

Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Joint scattering environment sensing and channel estimation based on non-stationary Markov random field,

Reference 8

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Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Semiblind channel estimation and data detection for OFDM systems with optimal pilot design,

Reference 9

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Observation eee2b45c-6d30-4283-a4f2-02e8af5286c8 · outbound

This paper cites Bilinear generalized approxi- mate message passing-part I: Derivation,.

Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Bilinear generalized approxi- mate message passing-part I: Derivation,

Reference 10

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Observation 25668143-38e0-4882-a655-620ad4aca89d · outbound

This paper cites Bilinear gaussian belief propagation for large MIMO channel and data estimation,.

Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Bilinear gaussian belief propagation for large MIMO channel and data estimation,

Reference 11

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Observation 1caa4fa5-29ef-48a1-b629-3569233fc2f9 · outbound

This paper cites Turbo-like joint data-and-channel estimation in quantized massive mimo systems,.

Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Turbo-like joint data-and-channel estimation in quantized massive mimo systems,

Reference 12

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This paper cites Variational Bayes’ joint channel estimation and soft symbol decoding for uplink massive MIMO systems with low resolution ADCs,.

Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Variational Bayes’ joint channel estimation and soft symbol decoding for uplink massive MIMO systems with low resolution ADCs,

Reference 13

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This paper cites Massive MIMO-OFDM systems with low resolution ADCs: Cramér-rao bound, sparse channel estimation, and soft symbol decod- ing,.

Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Massive MIMO-OFDM systems with low resolution ADCs: Cramér-rao bound, sparse channel estimation, and soft symbol decod- ing,

Reference 14

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This paper cites TST-MUSIC for joint DOA- delay estimation,.

Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems TST-MUSIC for joint DOA- delay estimation,

Reference 15

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This paper cites Subspace constrained variational Bayesian inference for structured compressive sensing with a dynamic grid,.

Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Subspace constrained variational Bayesian inference for structured compressive sensing with a dynamic grid,

Reference 16

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Observation 79897e70-6d1e-4fac-a1f4-0f3836a1cca1 · outbound

This paper cites Cloud-assisted cooperative localization for vehicle platoons: A turbo approach,.

Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Cloud-assisted cooperative localization for vehicle platoons: A turbo approach,

Reference 17

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Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Joint channel parameter estimation and scatterers localization,

Reference 18

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This paper cites Robust recovery of structured sparse signals with uncertain sensing matrix: A Turbo-VBI approach,.

Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Robust recovery of structured sparse signals with uncertain sensing matrix: A Turbo-VBI approach,

Reference 19

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Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Factor graphs and the sum-product algorithm,

Reference 20

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Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems The variational approximation for Bayesian inference,

Reference 21

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Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Fast inverse-free sparse Bayesian learning via relaxed evidence lower bound maximization,

Reference 22

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This paper cites Successive Linear Approximation VBI for Joint Sparse Signal Recovery and Dynamic Grid Parameters Estimation.

Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Successive Linear Approximation VBI for Joint Sparse Signal Recovery and Dynamic Grid Parameters Estimation

Reference 23

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This paper cites Signal recovery from random mea- surements via orthogonal matching pursuit,.

Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Signal recovery from random mea- surements via orthogonal matching pursuit,

Reference 24

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Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Detection of the number of coherent signals by the MDL principle,

Reference 25

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Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Super-resolution toa estimation with diversity for indoor geolocation,

Reference 26

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This paper cites Wiley 5G Ref, 2021.

Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems Wiley 5G Ref, 2021

Reference 27

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