Coded performance of spread space-spectrum multiple access for the MIMO forward link transmission
Bibliographic record
Abstract
In this paper, the coded performance of a previously proposed space-time multiuser multiplexing scheme called the spread space-spectrum multiple access (SSSMA) (Ng, BK et al.) is investigated for the forward link MIMO system. The key feature of SSSMA is that the number of user-channels is increased by exploiting additional degrees of freedom offered by multiple antennas. The large MIMO data pipe is divided and allocated to multiple coded user-channels over the entire transmission time interval and bandwidth. Thus, unlike the orthogonal multiple access scheme, the spatial multiple access interference (MAI) exists in SSSMA and is mitigated through the use of the space-time diagonal (STD) spreading sequences. When coding is introduced, each user may employ a user-specific optimal inter-leaver. It is theoretically shown that for two transmit antennas and optimal interleaving, the full diversity criterion is satisfied for all multiuser codeword pairs, thereby suggesting that spreading is an effective means to achieve spatial diversity at high bandwidth-efficiency. With suboptimal detector such as interference-cancelling receiver based on turbo processing, it is shown that the SSSMA offers near-capacity performance that is superior to many well-known MIMO transmission schemes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".