Swarm Intelligence based Power Allocation in Hybrid Millimeter-Wave Massive MIMO Systems
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Bibliographic record
Abstract
This work proposes a novel swarm intelligence based power allocation (PA) technique for multi-user massive multiple-input multiple-output (MU-mMIMO) systems. For the downlink transmission, we consider the geometry-based millimeter-wave (mmWave) channel model. The base station (BS) employs a three-dimensional angular-based hybrid precoding (3D-AB-HP) technique requiring low channel state information (CSI) overhead. The 3D-AB-HP architecture consists of three stages: (i) radio frequency (RF) precoder, (ii) baseband (BB) precoder, (iii) multi-user PA block. First, the RF precoder is built via the slow time-varying angle-of-departure information to reduce the CSI overhead size as well as the number of RF chains. It is designed via low cost phase-shifters, which induces the constant modulus constraint at the RF-stage design. Second, the BB precoder utilizes the regularized zero-forcing technique for mitigating the inter-user interference. Third, at the multi-user PA block, we develop a novel particle swarm optimization based PA (PSO-PA) algorithm to maximize the spectral/energy efficiency. Both the BB precoder and the multi-user PA block are constructed via the reduced-size effective channel seen from the BB-stage. Illustrative results reveal that the 3D-AB-HP with PSO-PA can remarkably improve the spectral/energy efficiency compared to the equal PA (e.g., up to 88% at the low/medium transmit power regime). Also, it is shown that the proposed 3D-AB-HP significantly decreases the number of RF chains (e.g., 94.2%) and the CSI overhead size (e.g., 87.1%), while providing higher energy efficiency than the conventional single-stage fully-digital precoding.
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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 it