Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction | Paper Citation Record

Observation 6a73c9a7-c929-4610-88c2-b4a93deafcd5 · outbound

This paper cites Learning to refine LLRs: Modular neural augmentation for MIMO-OFDM receivers,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Learning to refine LLRs: Modular neural augmentation for MIMO-OFDM receivers,

Reference 1

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Observation fb9b06f5-bc1a-4458-bbf7-a4d21b3771e0 · outbound

This paper cites On the road to 6G: Visions, requirements, key technologies, and testbeds,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction On the road to 6G: Visions, requirements, key technologies, and testbeds,

Reference 2

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Observation 90cfd4a6-4efa-4254-b968-c7c7205218b9 · outbound

This paper cites A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,

Reference 3

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Observation c812ea4f-f2b2-433b-9df4-fa86a9bd1184 · outbound

This paper cites Massive MIMO detection techniques: A survey,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Massive MIMO detection techniques: A survey,

Reference 4

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Observation 501a9d59-0d92-45cb-a4f2-b2a41899b484 · outbound

This paper cites Fifty years of MIMO detection: The road to large-scale MIMOs,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Fifty years of MIMO detection: The road to large-scale MIMOs,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation 58014aa3-b4bf-4098-ab63-1163ac5c2f80 · outbound

This paper cites A sphere decoding algorithm for MIMO channels,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction A sphere decoding algorithm for MIMO channels,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation badb8c1a-5e02-4631-b0d6-10213e24bead · outbound

This paper cites Iterative soft interference cancellation for multiple antenna systems,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Iterative soft interference cancellation for multiple antenna systems,

Reference 7

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Observation e77ea595-3427-4ff3-aaad-bab0c8a35362 · outbound

This paper cites RF impairments in wireless transceivers: Phase noise, CFO, and IQ imbalance – A survey,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction RF impairments in wireless transceivers: Phase noise, CFO, and IQ imbalance – A survey,

Reference 8

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Observation ee4866c0-8845-4d3b-b1f9-c463c0ec65a6 · outbound

This paper cites Channel estimation and performance analysis of one-bit massive MIMO systems,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Channel estimation and performance analysis of one-bit massive MIMO systems,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation c228b923-c8e8-44ff-b74a-2faa7d934de3 · outbound

This paper cites Asymptotic task-based quantization with application to massive MIMO,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Asymptotic task-based quantization with application to massive MIMO,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation 0e4c2c4d-ccb3-4546-ac49-be4f29197352 · outbound

This paper cites Iterative methods for cancellation of intercarrier interference in OFDM systems,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Iterative methods for cancellation of intercarrier interference in OFDM systems,

Reference 11

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Observation 5a6ca796-516e-40bc-837b-74bd5cdcc025 · outbound

This paper cites Digital postcompensation using volterra series transfer function,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Digital postcompensation using volterra series transfer function,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation bd5775b6-f4d7-46f4-b7a5-a80485deffd2 · outbound

This paper cites Deep learning for wireless communications: An emerging interdisciplinary paradigm,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Deep learning for wireless communications: An emerging interdisciplinary paradigm,

Reference 13

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Observation 9820f8e0-1f93-44bf-89d1-41ba01cef17d · outbound

This paper cites Neural network detection of data sequences in communication systems,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Neural network detection of data sequences in communication systems,

Reference 14

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Observation 69ff14f5-01c3-41f2-9682-19ef1e4a79d3 · outbound

This paper cites Machine learning for MU-MIMO receive processing in OFDM systems,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Machine learning for MU-MIMO receive processing in OFDM systems,

Reference 15

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Observation 9dc4c42a-a15a-449b-80b2-2984d8a9dfa5 · outbound

This paper cites A deep learning receiver for non-linear transmitter,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction A deep learning receiver for non-linear transmitter,

Reference 16

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Observation 36440237-3f35-4c7d-af43-deb18fdec802 · outbound

This paper cites DeepRx: Fully convolutional deep learning receiver,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction DeepRx: Fully convolutional deep learning receiver,

Reference 17

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Observation 2bdf6190-311d-4e37-8919-1286bfed8549 · outbound

This paper cites Deep learning OFDM receivers for improved power eﬀiciency and coverage,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Deep learning OFDM receivers for improved power eﬀiciency and coverage,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation 487c23de-66a7-4aac-8bde-80e3e2ac614c · outbound

This paper cites Deep learning based OFDM physical-layer receiver with multidilated convolutions,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Deep learning based OFDM physical-layer receiver with multidilated convolutions,

Reference 19

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Observation d52f1f5a-7fbe-45d4-acc9-ea4dccd57e2a · outbound

This paper cites Joint detection and decoding: A graph neural network approach,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Joint detection and decoding: A graph neural network approach,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation ac6b5032-b591-4ab2-9c52-d241df482f2d · outbound

This paper cites Graph Neural Networks-Enhanced expectation propagation algorithm for MIMO turbo receiver,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Graph Neural Networks-Enhanced expectation propagation algorithm for MIMO turbo receiver,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation a11b3bf6-7db9-45cb-bddc-9646fe40e7f0 · outbound

This paper cites Hypernetwork- enhanced GEPNet for MIMO-OFDM receiver with imperfect CSI,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Hypernetwork- enhanced GEPNet for MIMO-OFDM receiver with imperfect CSI,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation 479c7f7a-a840-405b-b2a2-cd1a3c72aeab · outbound

This paper cites A neural receiver for 5G NR multi-user MIMO,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction A neural receiver for 5G NR multi-user MIMO,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation 62d9e105-9920-407e-be23-7b6c2e880efb · outbound

This paper cites Unsupervised linear and nonlinear channel equalization and decoding using variational autoencoders,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Unsupervised linear and nonlinear channel equalization and decoding using variational autoencoders,

Reference 24

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Observation 3c91a990-3edf-4b3e-b5cd-ffff9ef84839 · outbound

This paper cites Comm-transformer: A robust deep learning-based receiver for OFDM system under TDL chan- nel,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Comm-transformer: A robust deep learning-based receiver for OFDM system under TDL chan- nel,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation e74aeeb0-c9c3-4d32-9e54-03e8f4ca7c4f · outbound

This paper cites Convolutional self-attention-based multi-user MIMO demapper,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Convolutional self-attention-based multi-user MIMO demapper,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation f859d0b3-6114-4332-b505-c93c2000d96a · outbound

This paper cites Deep learning in wireless communication receivers: A survey,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Deep learning in wireless communication receivers: A survey,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation 4bfae09b-079d-46e5-a7db-ef90279c88f4 · outbound

This paper cites Design of a standard-compliant real-time neural receiver for 5G NR,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Design of a standard-compliant real-time neural receiver for 5G NR,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation 2fc87c2c-7fc4-47b1-930f-fe61606a361b · outbound

This paper cites Adaptive and flexible model-based AI for deep receivers in dynamic channels,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Adaptive and flexible model-based AI for deep receivers in dynamic channels,

Reference 29

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Observation cfcea480-ad9c-45fb-96b1-f2ec583ed2ff · outbound

This paper cites Model-based deep learning,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Model-based deep learning,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation 8347586d-b4a9-49de-8e84-909c1dd4b8cb · outbound

This paper cites Comprehensive review of deep unfolding techniques for next-generation wireless communication systems,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Comprehensive review of deep unfolding techniques for next-generation wireless communication systems,

Reference 31

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Observation af61a6be-d611-4d17-ba86-8bd4e507f31e · outbound

This paper cites Deep Unfolding for Communications Systems: A Survey and Some New Directions.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Deep Unfolding for Communications Systems: A Survey and Some New Directions

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-06-30T02:24:12.982017Z

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Observation cd71c3d3-e8e4-4347-8e10-e73c793c81f8 · outbound

This paper cites Deep unfolding: Recent developments, theory, and design guidelines.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Deep unfolding: Recent developments, theory, and design guidelines

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-06-30T02:24:12.976011Z

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Observation 324a72ed-65c4-4a85-b487-2b7155f38fd2 · outbound

This paper cites A comprehensive survey of knowledge-driven deep learning for intelligent wireless network optimization in 6G,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction A comprehensive survey of knowledge-driven deep learning for intelligent wireless network optimization in 6G,

Reference 34

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Observation a847720d-ef88-4f4d-941c-5b0cce499dbc · outbound

This paper cites EqDeepRx: Learning a Scalable and Interference Mitigating MIMO Receiver.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction EqDeepRx: Learning a Scalable and Interference Mitigating MIMO Receiver

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-08-07T01:36:13.958205Z

Source-reported events for the cited work

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Observation c3111566-53b3-4b72-b632-60f10ffce9e9 · outbound

This paper cites Deep learning- based low complexity MIMO detection via partial MAP,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Deep learning- based low complexity MIMO detection via partial MAP,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation 782db6bd-3b06-4d53-92d7-9405800d0cfc · outbound

This paper cites Learning successive interference cancellation for low-complexity soft-output MIMO detection,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Learning successive interference cancellation for low-complexity soft-output MIMO detection,

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-06-30T02:24:12.979139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bc3d1250-ac65-482f-8678-7edc567e90fc · outbound

This paper cites “Machine LLRning.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction “Machine LLRning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation 9c6b292f-f7cc-4e95-adae-726ebe1b662e · outbound

This paper cites Adaptive neural signal detection for massive MIMO,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Adaptive neural signal detection for massive MIMO,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation e3a309d2-76c9-40a2-9f79-4a65a39be9e4 · outbound

This paper cites Model-driven deep learning for MIMO detection,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Model-driven deep learning for MIMO detection,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation 7afc00a8-bd6f-478a-9e40-200a6670675c · outbound

This paper cites GNN-assisted BiG-AMP: Joint channel estimation and data detection for massive MIMO receiver,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction GNN-assisted BiG-AMP: Joint channel estimation and data detection for massive MIMO receiver,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation 2b81c50c-a00f-4aee-8226-733e0f95b6f1 · outbound

This paper cites DeepSIC: Deep soft interference cancellation for multiuser MIMO detection,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction DeepSIC: Deep soft interference cancellation for multiuser MIMO detection,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation 781340c0-2c83-430e-bbb0-5125ae45a948 · outbound

This paper cites Deep learning based successive interference cancellation for the non-orthogonal downlink,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Deep learning based successive interference cancellation for the non-orthogonal downlink,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation aee72f9e-37b1-4f6d-a7a0-74e58a8f3351 · outbound

This paper cites Model-based deep learning receiver design for rate-splitting multiple access,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Model-based deep learning receiver design for rate-splitting multiple access,

Reference 44

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Observation 0dbfeb23-8107-4440-b2c0-08d025aa0205 · outbound

This paper cites Study on channel model for frequencies from 0.5 to 100 GHz,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Study on channel model for frequencies from 0.5 to 100 GHz,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation c3b3a1df-4fea-4e44-ab38-b21eabbfeb2b · outbound

This paper cites Hoydis, S.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Hoydis, S

Reference 46

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation e1a4ca11-c0b8-4dba-8e5c-e6645e71656a · outbound

This paper cites Quadriga: A 3-D multi-cell channel model with time evolution for enabling virtual field trials,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Quadriga: A 3-D multi-cell channel model with time evolution for enabling virtual field trials,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation 45309ca3-2603-4fec-85c4-ff08d96397e4 · outbound

This paper cites Joint estimation of carrier frequency offset and channel impulse response for linear periodic channels,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Joint estimation of carrier frequency offset and channel impulse response for linear periodic channels,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation b8e536e5-8af7-44b9-9f93-1b0336df0625 · outbound

This paper cites Asynchronous online adaptation via modular drift detection for deep receivers,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Asynchronous online adaptation via modular drift detection for deep receivers,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation fef81647-e34c-4e63-aa3d-50993d98e574 · outbound

This paper cites Algorithm unrolling: Inter- pretable, eﬀicient deep learning for signal and image processing,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Algorithm unrolling: Inter- pretable, eﬀicient deep learning for signal and image processing,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation ba46b7ef-a160-4610-9443-a6c38255fee3 · outbound

This paper cites Online learning of modular bayesian deep receivers: Single-step adaptation with streaming data,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Online learning of modular bayesian deep receivers: Single-step adaptation with streaming data,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation b89c9bba-cdaf-4a4a-bc57-2695369447bd · outbound

This paper cites Modular hypernetworks for scalable and adaptive deep MIMO receivers,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Modular hypernetworks for scalable and adaptive deep MIMO receivers,

Reference 52

Resolution: unresolved; no resolver link, observed 2026-06-30T02:22:21.501241Z

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Observation 7ffa15b4-8c6d-47d5-9329-f249731d45a8 · outbound

This paper cites Performance assessment of MIMO-BICM demodulators based on mutual information,.

Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction Performance assessment of MIMO-BICM demodulators based on mutual information,

Reference 53

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