On the capacity of MIMO Rician broadcast channels
Bibliographic record
Abstract
In this paper, a downlink communication system, in which a base station (BS) equipped with M antennas communicates with N (N Gt 1) single-antenna users, in a Rician fading environment is considered. The asymptotic (in terms of the number of users) sum-rate capacity of the system, as well as the capacity-achieving strategies, are derived. The main results of the paper are as follows: i) in the region of K = o(log N), where K denotes the Rician factor, the sum-rate capacity scales as M log(1 + P/Meta), where P denotes the SNR and eta =Deltalog N/1+K, which is achieved by zero-forcing beam-forming (ZFBF) along with a low-complexity user selection algorithm that considers only the scattered component of the userspsila channels, ii) in the region K = omega(log N), in the case of co-located transmit antennas, the capacity scales as log(1+MP), which is achieved by time division multiple access (TDMA), iii) in the region K = omega(log N), in the case of isotropically-distributed specular components, the sum-rate capacity behaves as M log(1 + P), which is achieved by ZFBF, along with a user selection algorithm that considers only the specular component of the userspsila channels.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".