AN EFFICIENT SIGNALING METHOD OVER MIMO BROADCAST SYSTEMS WITH MULTIPLE RECEIVE ANTENNAS
Bibliographic record
Abstract
A simple signaling method for multi-antenna broadcast channels is proposed. This method converts the interference matrix -- but not necessarily the channel matrix -- to a lowertriangular form. Dirty paper coding is used to cancel the remaining interference. The proposed scheme offers several desirable features in terms of: (i) accommodating users with different number of receive antennas, (ii) providing fairness and quality-of-service (QoS), (iii) requiring low feedback rate. The simulation results indicate that the achieved sum-rate is close to the sum-capacity of the underlying broadcast channel. An asymptotic analysis shows that the diversity order of the j data stream, 1 M is equal to NK(M j + 1), where M , N , and K indicate the number of transmit antennas, the number of receive antennas, and the number of users, respectively. Furthermore, it is shown that the throughput of this scheme scales as M log log(K) and asymptotically (K -# #) tends to the sum-capacity of the MIMO broadcast channel.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.003 | 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".