Turbo receiver for MIMO-CDMA systems employing parity bit selected and permutation spreading
Bibliographic record
Abstract
In this paper, a turbo receiver for MIMO-CDMA systems employing parity bit selected and permutation spreading is proposed. In such systems, spreading codes used by transmit antennas are dependent on the transmitted data. In the proposed system, convolutional coding is used as an outer code, while the parity bit selected scheme is used as the inner code. Detection and decoding are performed iteratively for each detected bit. When parity bit selected spreading is used, the parity bits generated by a linear block encoder are used to select a spreading code from a set of orthogonal spreading sequences. The selected spreading code is then used to spread the signals in all transmit antennas. In contrast, in permutation spreading, a permutation of orthogonal spreading sequences is used in each transmit antenna. In the proposed receiver, soft information passes between the detector and the decoder on multiple iterations. Detection is performed using the received signal from all receive antennas in combination with the extrinsic likelihood provided by a SISO decoder. The turbo receiver is further extended to a multiple user system, where the MAI is estimated in each iteration and subtracted out from the received signal. Simulations show a significant improvement in BER when a turbo receiver is used in these systems.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".