Optimal power allocation for massive MU-MIMO downlink TDD systems
Bibliographic record
Abstract
This paper considers a massive multi-user MIMO downlink time-division duplex system where a large number of antennas at the base station serves single-antenna users in the same time-frequency resource. In the downlink channel, we assume that users obtain an efficient information on the channel state, with the aid of pilot sequences transmitted by BS, to decode the data signals. It is assumed that there is a channel reciprocity between the downlink channel and uplink channel in time-division duplex mode. In this case, users first transmit a pilot sequence to BS, then BS estimates the CSI and precede beamforming training sequences for users. Each user uses minimum mean-square error channel estimation to obtain the estimation of the effective channel gains. Then, users receive the data signal from BS in the rest of the channel coherence time. A lower bound on the capacity is derived in the downlink channel to evaluate the spectral efficiency when BS employs maximum ratio transmission precoding. We also propose a new method of power allocation among the pilot sequences and data signals during a length of coherence time of channel in order to maximize the spectral efficiency for a given total energy budget. The benefits of the optimal power allocation method is verified by the results obtained through simulation. It is shown that more data signal power should be used at high signal-to-noise ratios.
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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) | 0.000 | 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".