Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T17:43:52.350061Z
Paper Citation Record · LEDGER
Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach
As of 18 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2507.10634.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
52 of 52 outbound references displayed
- verified exact2
- verified fuzzy42
- unresolved8
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:43:53.099651Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 1f626ae6-ea1d-4c7f-8f19-8cc02ca773b9 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:43:53.086405Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 209eae88-40e1-4935-b96d-99701e6006cb · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:43:53.073410Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:43:53.059822Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7116869b-29f6-4af6-95b8-4193405cfc32 · outbound
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
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T17:43:53.046756Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7eadab66-4fe9-4a1b-bd89-7c9db7effa8f · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:43:53.033757Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 1df70b02-e7c6-4089-8004-39face8806ba · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9ada4101-2616-4539-8b9f-d6a1ab5a7b00 · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 0856ec3d-5663-4255-bade-f4fb7e6cfa36 · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a6a13be7-6ee7-40e7-9cac-6ceeba40cfd9 · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:43:52.953802Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2720469f-26f9-4965-af5d-6ed096cbe6e3 · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 38e791eb-889f-47f4-856e-61f87aa23502 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:43:52.883639Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:43:52.855081Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 467f7ea7-aa6d-4524-8913-71739f5e4834 · outbound
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:43:52.838337Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:43:52.822224Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:43:52.762766Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:43:52.747766Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:43:52.326171Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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
Source-reported events for the cited work
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.