Swarm Intelligence based Power Allocation in Hybrid Millimeter-Wave Massive MIMO Systems
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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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".