Cyclic space-frequency filtering for BICM-OFDM systems with multiple co-located or distributed transmit antennas
Bibliographic record
Abstract
In this paper, we introduce cyclic space-frequency (CSF) filtering for orthogonal frequency division multiplexing (OFDM) systems using bit-interleaved coded modulation (BICM) and multiple transmit antennas for fading mitigation, and discuss the extension to orthogonal frequency division multiple access (OFDMA) systems. CSF filtering is a simple form of space-frequency coding (SFC) and may be viewed as a generalization of cyclic delay diversity (CDD) where the cyclic delays are replaced by CSF filters. CSF is applicable to both traditional multiple-input multiple-output systems with co-located transmit antennas and cooperative diversity systems with decode-and-forward relaying and distributed transmit antennas. Similar to CDD and in contrast to other SFC schemes, CSF filtering does not require any changes to the receiver compared to single-antenna transmission. Based on the asymptotic pairwise error probability of the overall system we derive an optimization criterion for the CSF filters for co-located and distributed transmit antennas, respectively. We show that the optimum CSF filters are independent of the interleaver if they do not exceed a certain length. If this length is exceeded, the adopted interleaver has to be carefully taken into account in the CSF filter design. For several special cases we derive closed-form solutions for the optimum CSF filters and for the general case we provide various CSF filter design methods. Our simulation results show that CSF filtering can achieve significant performance gains over existing CDD schemes.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".