An Iterative Resource Allocation and Precoder Design for Wideband mmWave MIMO-OFDM System With Large-Scale Users
Bibliographic record
Abstract
For the scenario of large-scale users in millimeter wave (mmWave) multi-user multiple-input multiple-output (MU-MIMO)-orthogonal frequency division multiplexing (OFDM) systems, power allocation on each subcarrier (SC), association between SCs and users (SC-UE association) and precoder design are mutually dependent due to the hybrid MIMO-OFDM structure. This interdependence suggests that these factors should be jointly considered and optimized to make full use of both spatial and frequency diversity. In this paper, an iterative optimization framework is proposed for optimizing both the sum rate and the minimum user rate considering the beam squint effect. The optimization framework first performs a coarse SC-UE association based on an SC-level channel clustering, and then alternatively optimizes the hybrid analog and digital (HAD) precoder and SC-UE association in an iterative manner. The SC-UE association problems are solved by the greedy algorithm and heuristic method for sum rate and minimum rate optimization problems, respectively. The computational complexities of the proposed methods are evaluated and compared with several existing benchmarks. Numerical simulations show that the proposed methods outperform the corresponding benchmarks, while hold comparable running times by contrast due to its fast convergence rate.
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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 | 0.001 |
| 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) | 0.001 | 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".