Implementation of enhanced CDMA utilizing low complexity joint detection with iterative processing
Bibliographic record
Abstract
Computationally efficient joint detection in CDMA packetized communication is considered. The joint detection is based on iterative cancellation and utilizes only low complexity individual data receivers. The savings in complexity compared to other alternatives proposed in the literature is due to an encoding scheme known as partition spreading that can be decoded using algorithms that are similar to well-known turbo and sum-product decoding. Besides having a low computational complexity, the technique offers near-far resistant performance and can achieve higher system loads than conventional CDMA. The low complexity of these component receivers allows a large number of users to be implemented onto a single FPGA. A Virtex-IV on a Lyrtech Development board is used to implement a test bed for this PS-CDMA system. The implementation focuses on area optimization to give 50 users in a single Virtex-IV with 84% slice utilization and a maximum aggregate throughput of 192Mb/s. The measured performance of this prototype is compared against theoretical results on PS-CDMA in environments with varying power levels. An FPGA resource-performance analysis is given.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".