Fast Iterative Hybrid Precoding and Combining With Momentum Gradient Descent and Newton’s Method for Millimeter Wave MIMO Systems
Bibliographic record
Abstract
Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems have attracted much attention from both researchers and industry professionals, as they are seen as a suitable solution to the growing demand for cellular services in fifth-generation (5G) and sixth-generation (6G) wireless communication systems. In mmWave MIMO systems, iterative hybrid precoding/combining algorithms, which use a combination of analog and digital precoders, have gained significant interest because they perform comparably to fully digital precoding/combining while operating at reduced complexity due to a lower number of radio frequency (RF) components. However, the problem with these algorithms is that their convergence requires a substantial number of iterations. This paper solves this problem and introduces a fast convergence iterative hybrid precoding/combining algorithm using momentum and Newton’s method (FIHB-MN) for mmWave MIMO systems. Simulation results demonstrate the faster convergence of the algorithm’s objective function compared to other iterative methods in the literature. Moreover, FIHB-MN provides performance similar to unconstrained digital precoding and combining with only a few iterations. The simulation results also confirm that the spectral efficiency as well as the bit error rate (BER) performances of the proposed FIHB-MN algorithm outperforms other hybrid beamforming methods in the literature, all while maintaining low computational complexity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".