Energy‐efficient pilot and data power allocation in massive multi‐user multiple‐input multiple‐output communication systems
Bibliographic record
Abstract
This study aims to improve the energy efficiency of time‐division duplexing massive multi‐user multiple‐input multiple‐output communication systems for both uplink and downlink transmissions. By using minimum mean square error channel estimation, two novel pilot–data power allocation schemes are proposed to minimise the total uplink and downlink transmit power under per‐user signal‐to‐interference‐plus‐noise ratio (SINR) requirement and per‐user power consumption constraints. The proposed schemes take into account the maximum‐ratio combining and zero‐forcing (ZF) detectors in the uplink transmission together with maximum‐ratio transmission and ZF precoder in the downlink transmission. In order to simplify the proposed optimisation problems, lower bounds of the average SINR are derived and used in the power allocation algorithms. The key contribution of this study lies in formulating the original energy‐efficient power allocation problem and converting such a complicated optimisation problem to a geometric programming problem. Computer simulation validates the tightness of the derived SINR lower bounds and shows that the proposed schemes can save up to 78% of the total power as compared with the equal power allocation among all the mobile users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".