Energy-Efficient Massive MIMO Design: Optimal Number of Antennas Ensuring Guaranteed Bit Rate
Bibliographic record
Abstract
Massive multi-input multi-output (mMIMO) antenna systems deliver ultra-high data rates thanks to their favourable propagation and channel hardening phenomena. These benefits, however, come at the cost of higher base station (BS) total power consumption. Classically, such a challenge is tackled by deactivating some of the mMIMO antennas to optimize energy efficiency (EE). Optimizing EE can impact the mandated quality of service (QoS) requirements, such as guaranteed bit rate (GBR), which should be avoided. In this paper, we leverage the best response approach from the symmetric game theory to numerically solve the EE optimization for a multi-cell network and a given GBR constraint. We also propose a data traffic model that considers various user equipment capabilities and mobile data applications and translates them into a GBR level. Simulation results show that our algorithm can carefully allocate the optimal number of antennas to satisfy GBR requirements and best-possible EE targets.
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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 | 0.000 |
| 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) | 0.001 | 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 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".