Transmission Schemes and Power Allocation for Multiuser Massive MIMO Relaying
Bibliographic record
Abstract
This paper proposes and analyzes the performance of two simple transmission protocols for a multiuser massive multiple input multiple-output relaying system, where K single-antenna users transmit data to a massive-antenna destination through an N-antenna relay node. In the first transmission protocol, the relay does not need to know the CSI. It just amplifies and forwards the received signals to the destination. In the second protocol, the relay first estimates the channels from all users. It then uses the maximum-ratio combining (MRC) technique to combine all received signals and forwards them to the destination. In both protocols, the destination estimates the channels and employs MRC to decode the signals. We propose an efficient channel estimation method at the destination in which the destination estimates only the effective channels gains. As a consequence, the channel estimation overhead does not depend on the numbers of relay and destination antennas. We derive closed-form expressions for the spectral efficiency of the two transmission protocols. These results allow us to further analyze the system performance and to allocate the transmit powers. Particularly, a max-min power control algorithm is proposed which selects the transmit powers at the relay and users to maximize the lowest spectral efficiency of all users. We show that, by using the max-min power allocation algorithm, the spectral efficiency can be increased significantly, compared to uniform power allocation. Furthermore, if the distance between the users and the relay is large, the first transmission protocol is better and vice versa if this distance is small.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".