Performance of multiple-input and multiple-output orthogonal frequency and code division multiplexing systems in fading channels
Bibliographic record
Abstract
In broadband downlink transmission, orthogonal frequency-division multiplexing (OFDM) combined with code-division multiple access (CDMA) is a prospective technique for high-data rate transmission in future wireless communication systems. By adding spatial diversity, multiple-input and multiple-output orthogonal frequency and code division multiplexing (MIMO-OFCDM) offers superior performance relative to both traditional OFDM systems and single-input and single-output OFCDM (SISO-OFCDM) systems. In this study, the authors present an analytical study and investigation of a MIMO-OFCDM downlink system that hires orthogonal variable spreading factor codes to spread each transmitted symbol in both time and frequency domains. Different gain combining schemes are employed in the frequency domain to recover the data symbols of the desired code channels, and space–time block coding is used to achieve spatial diversity. The more general Ricean fading channel is used to model the MIMO channel. The OFCDM system employs Alamouti transmit diversity scheme with multiple receive antennas. For systems without multi-code interference (MCI), analytical bit-error rate results are obtained and compared with simulation results. The authors also investigate the effect of correlation in frequency domain, where we verify that minimum mean-square error frequency combining is more robust to MCI than equal-gain combining.
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 | 0.008 |
| 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 | 0.001 |
| 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".