Asymptotic Spectral Efficiency of MIMO Ad hoc Networks
Bibliographic record
Abstract
In this correspondence, we present an asymptotic analysis of spectral efficiency of MIMO ad hoc networks. We consider a MIMO ad hoc network whereLlinks, each consisting of a transmitter/receiver pair, communicate independent information over the network, simultaneously. Each transmitter node hasNtantennas and each receiver node is equipped withNrantennas. It is assumed that for each user, perfect channel state information (CSI) is available at its transmitter and receiver. The exact asymptotic spectral efficiency of large MIMO ad hoc networks has been considered to be an unsolved problem until now. A lower bound has been found by Chen and Gans based on using beamforming as a sub-optimal transmission scheme. In this work, we show that the previously presented lower bound is also an upper bound when L goes to infinity, all links use full power and single-user decoding is used; hence, it represents the exact asymptotic spectral efficiency (ASE) of the network. Therefore, the network ASE is equal to the mean value of the largest eigenvalue of the channel covariance matrix which is upper bounded by (¿{Nt}+¿{Nr})2for large values ofNtandNr.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".