Constrained Tensor Decomposition-Based Hybrid Beamforming for Mmwave Massive MIMO-OFDM Communication Systems
Bibliographic record
Abstract
Hybrid beamforming design for OFDM systems is hugely challenging since their analog precoders and combiners are shared among all subcarriers. This paper proposes a novel two-stage joint hybrid precoder and combiner design for maximizing the average achievable sum-rate of frequency-selective millimeter-wave massive MIMO-OFDM systems. In the proposed approach, the analog precoder and combiner design is formulated as a constrained Tucker2 tensor decomposition problem, which allows maximizing the sum of the effective baseband gains over every subcarrier while suppressing inter-user and intra-user interferences. The solution is obtained by a projected alternate least-square-based algorithm, which is suitable for both single-user (SU) and multiuser (MU) systems. The digital precoder and combiner for SU systems is obtained from the effective baseband channel' SVD. In contrast, for MU systems, it is obtained from the regularized channel diagonalization method, which balances multiuser interference and noise suppression. Numerical simulation results validate the effectiveness of the proposed method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".