Precoding for Multiuser MIMO Systems with Multiple Base Stations
Bibliographic record
Abstract
Future cellular networks are expected to support extremely high data rates and user capacities. This thesis investigates the downlink of a wireless cellular system that takes advantage of multiple antennas at base stations and mobile stations, frequency reuse across all cells, and cooperation among base stations. We identify asynchronous interference resulting from multi-cell communication as a key challenge, prove the existence of a downlink/uplink duality in that case, and present a linear precoding scheme that exploits this duality. Since this result is not directly extendable to orthogonal frequency division multiplexing (OFDM), we propose a `hybrid' algorithm for two cooperating base stations, which combines linear and nonlinear precoding. This algorithm minimizes the sum mean squared error of the system and is extendable to OFDM. Finally, we consider the problem of user selection for multiuser precoding in OFDM-based systems. We extend an available single-cell user selection scheme to multiple cooperating cells.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".