Performance issues in successive interference cancellation with reference symbol assisted channel estimation
Bibliographic record
Abstract
Direct sequence code division multiple access (DS-CDMA) is a leading candidate scheme to be used in the second generation cellular and personal communication systems. A novel multi-stage successive interference cancellation scheme is proposed which operates on the reverse CDMA link using BPSK modulation, coherent detection and reference symbols to obtain channel estimates. The results of a single cell multi-user investigation show that the traffic capacity can be increased. However, the total traffic capacity is somewhat disappointing because the channel estimates are corrupted significantly by interference from symbols not yet demodulated and cancelled by the receiver. The transmitted signal structure is, therefore, modified to decrease that interference. The results of a single cell analysis of the modified cancellation scheme demonstrate that the system's traffic capacity reaches approximately 80% of that of a multistage successive interference cancelling receiver operating on perfect channel parameters. The multi-cell results show that for hexagonal cell geometry with path loss exponent of 4 and without any forward error correction coding the capacity of the system is between 1.5 and 2.5 times that using the conventional matched filter receiver.
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 | 0.004 | 0.031 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".