An Iterative Expectation-Maximization Algorithm Based Joint Estimation Approach for CDMA/OFDM Composite Radios
Bibliographic record
Abstract
In this paper, an innovative frequency domain joint estimation algorithm for synchronization parameter and channel impulse response (CIR) in direct sequence code division multiple access (DS-CDMA) systems is proposed. The algorithm is based on the expectation-maximization (EM) method. It can provide accurate estimation of channel state information and synchronization parameter for a DS-CDMA receiver even with a simple equalization module (e.g., an one-tap multiplier based frequency domain equalizer (FDE)), and a radio receiver with this approach performs better than a costly multi-tap multiplier based equalizer, such as the time domain equalizer (TDE). A generic receiver architecture based on the frequency domain equalization for a composite radio, which works in both CDMA and orthogonal frequency division multiplexing (OFDM) modes, is also proposed. The Cramer-Rao lower bound (CRLB) of the proposed estimator and its optimization scheme are derived. This architecture can be implemented with an iterative approach, and the results demonstrate that this adaptive receiver performs very well with a relatively low cost.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".