Intelligent Subcarrier Allocation in Hybrid Beamforming Multi-User mMIMO-OFDM Systems
Bibliographic record
Abstract
This paper proposes a genetic-algorithm (GA)-based subcarrier allocation in orthogonal frequency division multiplexing (OFDM)-based hybrid beamforming multi-user massive multiple-input multiple-output (MU-mMIMO) systems. Our goal is to maximize the system sum-rate capacity under the total transmit power constraint through optimally selecting K <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</inf> users out of K available users to be served over each sub-carrier. Considering the energy-efficient hybrid beamforming architecture deployed at the base station (BS), the non-convex optimization problem is solved in four steps: (i) designing a radio frequency (RF) beamformer using slow time-varying angle-of-departure (AoD) information of users to generate the beams for all subcarriers, (ii) designing a baseband (BB) precoder for each subcarrier using the corresponding low-dimensional effective channel state information (CSI) seen from the BB stage based on regularized zero-forcing (RZF) technique, (iii) optimizing subcarrier allocation using GA with equal power allocation (EQ-PA) among users (iv) performing GA-based power allocation over each subcarrier to further improve the system sum-rate. Illustrative results indicate that the proposed algorithm performs significantly better than the random and greedy subcarrier allocation schemes in terms of the achieved sum-rate.
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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 | 0.001 |
| 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".