Energy-Efficient Throughput Maximization in mmWave MU-Massive-MIMO-OFDM: Genetic Algorithm based Resource Allocation
Bibliographic record
Abstract
This paper develops a new genetic algorithm based resource allocation (GA-RA) technique for energy-efficient throughout maximization in multi-user massive multiple-input multiple-output (MU-mMIMO) systems using orthogonal frequency division multiplexing (OFDM) based transmission. We employ a hybrid precoding (HP) architecture with three stages: (i) radio frequency (RF) beamformer, (ii) baseband (BB) precoder, (iii) resource allocation (RA) block. First, a single RF beamformer block is built for all subcarriers via the slow time-varying angle-of-departure (AoD) information. For enhancing the energy efficiency, the RF beamformer aims to reduce the hardware cost/complexity and total power consumption via a low number of RF chains. Afterwards, the reduced-size effective channel state information (CSI) is utilized in the design of a distinct BB precoder and RA block for each subcarrier. The BB precoder is developed via regularized zero-forcing technique. Finally, the RA block is built via the proposed GA-RA technique for throughput maximization by allocating the power and subcarrier resources. The illustrative results show that the throughput performance in the MU-mMIMO-OFDM systems is greatly enhanced via the proposed GA-RA technique compared to both equal RA (EQ-RA) and particle swarm optimization based RA (PSO-RA). Moreover, the performance gain ratio increases with the increasing number of subcarriers, particularly for low transmission powers.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".