Bibliographic record
Abstract
Foschini quantified the large capacity of the multiple-input multiple-output (MIMO) wireless channel and showed how this capacity could be achieved using a layered and coded space-time architecture. Unfortunately, his proposed diagonal Bell Laboratories Space-Time (D-BLAST) detection algorithm has proved awkward to implement. Attention has instead focused on the simpler vertically-layered architecture. The well-known vertical MIMO detectors, such as zero forcing (ZF), minimum mean squared error (MMSE), maximum likelihood (ML), vertical BLAST (V-BLAST), and several versions of sphere decoding (SD), offer different trade-offs between computational complexity and performance. V-BLAST offers intermediate, but clearly suboptimal performance that has a computational complexity that grows linearly in the number of transmitted layers and in the size M of the symbol constellation. Fouladi Fard, Alimohammad and Cockburn recently proposed a parallel V-BLAST algorithm, which we call F-BLAST, that offers performance that approaches that of optimal ML at the cost of performing V-BLAST in parallel for all M possible values of the symbol in the layer with the weakest expected signal-to-noise ratio. Here we revisit the performance of F-BLAST and show how the degree of parallelism can be reduced while maintaining performance that greatly exceeds that of V-BLAST. The data parallel structure of the new detection algorithms, and their simpler control structure compared to SD, should offer implementation advantages.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".