Hybrid Beamforming for mmWave Massive MIMO Systems Using Conditional Generative Adversarial Networks
Bibliographic record
Abstract
Massive multiple-input multiple-output (MIMO) systems operating in millimeter wave (mmWave) frequency bands are considered to be one of the key enablers of beyond-fifth-generation cellular systems. Although the highest spectral efficiency in such systems would be achieved using fully-digital precoding, the large number of antennas in massive MIMO systems makes using a radio frequency (RF) chain for each antenna expensive and currently infeasible in practice. A common alternative solution is hybrid beamforming, which combines analog beamforming and digital precoding and reduces the required number of RF chains. The primary goal of hybrid beamforming is to provide precoding performance as close as possible to that of a fully-digital precoder. In this work, we consider two variants of a generative adversarial network (GAN), namely a conditional GAN (CGAN) and Wasserstein CGAN (WCGAN) to develop the hybrid precoder. The CGAN is used to implement the (partially-connected) analog beamformer and the WCGAN is used for the digital precoder. Our simulation results demonstrate the proposed method yields an improvement in spectral efficiency of about 12–19% over some existing hybrid beamforming schemes and achieves up to 87% of the performance of fully-digital precoding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".