An enhanced multilevel ML-DFT codebook algorithm for hybrid beamforming of millimetre wave MIMO systems
Bibliographic record
Abstract
Millimetre Wave (mmWave) wireless communication is considered an enabling technology to allow 5G cellular achieving high data rates. Large-scale antenna arrays can be adopted to compensate the huge path loss at higher frequencies. MIMO precoding cannot be performed only at baseband due to high power consumption of signal mixers and analogue-to-digital converters. Therefore, hybrid analogue-digital architecture at transceiver is considered as a cost-effective precoding scheme. However, the optimal design of such hybrid precoders and combiners needs to be further investigated. In this paper, a maximum likelihood (ML) beamforming technique is used for estimating signal's direction of departure in the presence of random noise. Moreover, an enhanced Orthogonal Mapping-based Matching Pursuit (OMBMP) algorithm is proposed. Layered orthogonal codebook is used to adjust base stations beamformers. Simulation results demonstrate that the proposed architecture provides acceptable performance gain with almost 90% of the performance of optimal full-digital precoder with great reduced 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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".