Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach | Paper Citation Record

Observation edfbccaf-5af9-4670-80c6-ac08cce0d68d · outbound

This paper cites Massive mimo is a reality—what is next?: Five promising research directions for antenna arrays,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Massive mimo is a reality—what is next?: Five promising research directions for antenna arrays,

Reference 1

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This paper cites Massive MIMO for next generation wireless systems,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Massive MIMO for next generation wireless systems,

Reference 2

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This paper cites Terahertz Communications for 6G and Beyond Wireless Networks: Challenges, Key Advancements, and Opportunities,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Terahertz Communications for 6G and Beyond Wireless Networks: Challenges, Key Advancements, and Opportunities,

Reference 3

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Observation 3ea1a92a-75e4-4e4d-926a-8211f3bed2ca · outbound

This paper cites Energy-constrained modulation optimization,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Energy-constrained modulation optimization,

Reference 4

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This paper cites an unresolved cited work.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Unresolved cited work

Reference 5

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This paper cites SFDR-bandwidth limitations for high speed high resolution current steering CMOS D/A converters,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach SFDR-bandwidth limitations for high speed high resolution current steering CMOS D/A converters,

Reference 6

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This paper cites Uplink Achievable Rate for Massive MIMO Systems With Low-Resolution ADC,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Uplink Achievable Rate for Massive MIMO Systems With Low-Resolution ADC,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:53.020635Z

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This paper cites Low power analog-to-digital conversion in millimeter wave systems: Impact of resolution and band- width on performance,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Low power analog-to-digital conversion in millimeter wave systems: Impact of resolution and band- width on performance,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:53.007238Z

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Observation 97b1900d-b05e-4bd1-8661-b8311122eee6 · outbound

This paper cites Spectral Efficiency of Mixed-ADC Massive MIMO,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Spectral Efficiency of Mixed-ADC Massive MIMO,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.994038Z

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This paper cites Performance Analysis of Mixed-ADC Massive MIMO Systems Over Rician Fading Channels,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Performance Analysis of Mixed-ADC Massive MIMO Systems Over Rician Fading Channels,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.980661Z

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This paper cites An ADC-Aware Receiver Design for Multi-User MIMO Underlay System With Strong Cyclostationary Legacy Signal,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach An ADC-Aware Receiver Design for Multi-User MIMO Underlay System With Strong Cyclostationary Legacy Signal,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.967616Z

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Observation 17c8add3-6523-4748-b080-9a20dfc5d1c2 · outbound

This paper cites Spa- tial Characteristics of Distortion Radiated From Antenna Arrays With Transceiver Nonlinearities,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Spa- tial Characteristics of Distortion Radiated From Antenna Arrays With Transceiver Nonlinearities,

Reference 12

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Observation 7c6ae7f9-ddff-4f87-ba5f-3ff544f28e14 · outbound

This paper cites On one-bit quantized ZF precoding for the multiuser massive MIMO downlink,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach On one-bit quantized ZF precoding for the multiuser massive MIMO downlink,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.939706Z

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Observation c5db09b9-a59c-41d5-bc90-c0a8875b6c5f · outbound

This paper cites MMSE precoder for massive MIMO using 1-bit quantization,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach MMSE precoder for massive MIMO using 1-bit quantization,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.924893Z

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Observation 0beebcea-6f1d-4b54-94ca-51f363648c0d · outbound

This paper cites Quantized Precoding for Massive MU-MIMO,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Quantized Precoding for Massive MU-MIMO,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.910645Z

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This paper cites Nonlinear 1-bit precoding for massive MU-MIMO with higher- order modulation,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Nonlinear 1-bit precoding for massive MU-MIMO with higher- order modulation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.897428Z

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This paper cites Transmit processing with low resolution D/A-converters,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Transmit processing with low resolution D/A-converters,

Reference 17

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Observation d8c2f5fc-09ea-49ed-b84e-14c2babfb3b5 · outbound

This paper cites Energy Efficiency Maximization Precoding for Quantized Massive MIMO Systems,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Energy Efficiency Maximization Precoding for Quantized Massive MIMO Systems,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.870151Z

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Observation 46906cf7-daf5-4ad6-a0b2-7251d8d8ec44 · outbound

This paper cites Hardware Distortion Correlation Has Negligible Impact on UL Massive MIMO Spectral Efficiency,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Hardware Distortion Correlation Has Negligible Impact on UL Massive MIMO Spectral Efficiency,

Reference 19

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This paper cites Energy Efficiency of mmWave Massive MIMO Precoding With Low-Resolution DACs,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Energy Efficiency of mmWave Massive MIMO Precoding With Low-Resolution DACs,

Reference 20

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Observation 2ae4d251-5eb8-4594-8f65-f1a5b4a4a905 · outbound

This paper cites Unsuper- vised Learning-Based Fast Beamforming Design for Downlink MIMO,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Unsuper- vised Learning-Based Fast Beamforming Design for Downlink MIMO,

Reference 21

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Observation 4155485c-036e-4055-a581-4d3f8660a07d · outbound

This paper cites Model-Driven Beamform- ing Neural Networks,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Model-Driven Beamform- ing Neural Networks,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.805878Z

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Observation c1003877-911a-4464-9162-a8aa20ce37cc · outbound

This paper cites Deep Unfolding for Fast Linear Massive MIMO Precoders under a PA Consumption Model,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Deep Unfolding for Fast Linear Massive MIMO Precoders under a PA Consumption Model,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.791596Z

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Observation 07beca1c-b0c2-47dd-afdd-30ab97422e1c · outbound

This paper cites Toward Energy-Efficient Massive MIMO: Graph Neural Network Precoding for Mitigating Non- Linear PA Distortion,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Toward Energy-Efficient Massive MIMO: Graph Neural Network Precoding for Mitigating Non- Linear PA Distortion,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.777419Z

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Observation 35804797-78ac-4818-b62b-3c35cdf1db00 · outbound

This paper cites Self-Supervised Learning of Linear Precoders under Non-Linear PA Distortion for Energy-Efficient Massive MIMO Systems,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Self-Supervised Learning of Linear Precoders under Non-Linear PA Distortion for Energy-Efficient Massive MIMO Systems,

Reference 25

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Observation 73dc0420-7323-4fef-aad2-22bf9f5e6d4c · outbound

This paper cites Relational inductive biases, deep learning, and graph networks,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Relational inductive biases, deep learning, and graph networks,

Reference 26

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Observation c1f12d9a-54df-4e66-bf63-402adcbe3e8e · outbound

This paper cites Goodfellow, Y.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Goodfellow, Y

Reference 27

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Observation 94a3221d-02c6-4750-8b56-8928ed7b28e3 · outbound

This paper cites Understanding the performance of learn- ing precoding policies with graph and convolutional neural networks,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Understanding the performance of learn- ing precoding policies with graph and convolutional neural networks,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.723315Z

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Observation f951ef61-bf5f-43c1-b832-c69c951833fc · outbound

This paper cites Learning Precoding Policy: CNN or GNN?.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Learning Precoding Policy: CNN or GNN?

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.707425Z

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Observation 7f670754-68bc-430b-b074-41203ce5fc68 · outbound

This paper cites Neural-Network Optimized 1-bit Precoding for Massive MU-MIMO,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Neural-Network Optimized 1-bit Precoding for Massive MU-MIMO,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.694034Z

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Observation 960cc293-5c54-44b0-a225-ee79c62a930e · outbound

This paper cites 1-bit Massive MU-MIMO Precoding in VLSI,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach 1-bit Massive MU-MIMO Precoding in VLSI,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.680824Z

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Observation 9c8050b7-0232-4228-b7bf-744d92ddfde1 · outbound

This paper cites Deep Learning Based Interference Exploitation in 1-Bit Massive MIMO Precoding,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Deep Learning Based Interference Exploitation in 1-Bit Massive MIMO Precoding,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.667359Z

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Observation 3ef0f2ee-62ed-4bbc-9847-d2e58499c5bf · outbound

This paper cites Neural Combinatorial Optimization with Reinforcement Learning.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Neural Combinatorial Optimization with Reinforcement Learning

Reference 33

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Observation fd088de1-8f75-4e6d-9cff-3db3914e6a7e · outbound

This paper cites Neural Combinatorial Optimization: a New Player in the Field.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Neural Combinatorial Optimization: a New Player in the Field

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:43:52.443757Z

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Observation e4a25126-f3cd-4932-a630-622b9edc6d78 · outbound

This paper cites Machine learning for combinato- rial optimization: A methodological tour d’horizon,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Machine learning for combinato- rial optimization: A methodological tour d’horizon,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.653642Z

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Observation cdbf9be0-1a23-4d00-a7ea-14ba37fec119 · outbound

This paper cites Towards One- shot Neural Combinatorial Solvers: Theoretical and Empirical Notes on the Cardinality-Constrained Case,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Towards One- shot Neural Combinatorial Solvers: Theoretical and Empirical Notes on the Cardinality-Constrained Case,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.640600Z

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Observation c0c93a25-e20c-4da6-aecc-9c7ab03c2cb5 · outbound

This paper cites A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.626904Z

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Observation ef586bef-94d7-4ff2-9bae-77eafd8fb777 · outbound

This paper cites Robust Predictive Quantization: Analysis and Design Via Convex Optimization,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Robust Predictive Quantization: Analysis and Design Via Convex Optimization,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.611467Z

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Observation d578c1e9-d15f-4aa9-8861-711a51af187c · outbound

This paper cites The Bussgang Decomposition of Non- linear Systems: Basic Theory and MIMO Extensions [Lecture Notes],.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach The Bussgang Decomposition of Non- linear Systems: Basic Theory and MIMO Extensions [Lecture Notes],

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.597591Z

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Observation 15eea809-7729-4120-a3e8-9533b5bc2920 · outbound

This paper cites Gersho and R.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Gersho and R

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.583489Z

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Observation 20780481-0f43-49d6-a416-e93acc8fabe9 · outbound

This paper cites OFDM and Its Wireless Applications: A Survey,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach OFDM and Its Wireless Applications: A Survey,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.570614Z

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Observation a2c484bd-c194-420c-bddd-fc1e805aa0a2 · outbound

This paper cites Quantizing for minimum distortion,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Quantizing for minimum distortion,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.556629Z

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Observation efec4992-d175-4786-a10a-3568b536c0d1 · outbound

This paper cites Adam: A Method for Stochastic Optimization,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Adam: A Method for Stochastic Optimization,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T17:43:52.314106Z

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Observation 7ba55efc-bcf3-4538-be3e-586a1a29ac39 · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Categorical Reparameterization with Gumbel-Softmax,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T17:43:52.322488Z

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Observation 5ad33448-76d1-4e33-a15e-8d681061475e · outbound

This paper cites Multilayer feedforward networks are universal approximators,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Multilayer feedforward networks are universal approximators,

Reference 45

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Observation c6a88b86-bc3f-4531-aa0a-20c70dd66cd4 · outbound

This paper cites Graph Representation Learning,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Graph Representation Learning,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.514725Z

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Observation ac39ec21-955c-43a3-8da4-58ac08a808fe · outbound

This paper cites Neural network accelerator comparison.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Neural network accelerator comparison

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.500321Z

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Observation 3765d421-528f-4d08-a964-1632cad46a40 · outbound

This paper cites A 40-nm 646.6tops/w sparsity-scaling dnn processor for on-device training,.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach A 40-nm 646.6tops/w sparsity-scaling dnn processor for on-device training,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.486591Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2fd8e147-7128-4477-b822-131e256890b7 · outbound

This paper cites GNN-based Precoder Design and Fine-tuning for Cell-free Massive MIMO with Real-world CSI.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach GNN-based Precoder Design and Fine-tuning for Cell-free Massive MIMO with Real-world CSI

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:43:52.408253Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bb473f65-0058-45f6-a136-199cc5c07277 · outbound

This paper cites A Comprehensive Survey of Continual Learning: Theory, Method and Application.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach A Comprehensive Survey of Continual Learning: Theory, Method and Application

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T17:43:52.345874Z

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Unavailable: canonical work link unavailable.

Observation 348406ef-871f-4b7c-b536-74f26171a9ec · outbound

This paper cites Overview of AI/ML related work in 3GPP.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Overview of AI/ML related work in 3GPP

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:43:52.473203Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 16075403-f42e-484f-85d0-9d8ecdce6da0 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach Adam: A Method for Stochastic Optimization

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-06T17:43:52.318273Z

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