Iterative Receiver Design with Joint Channel Estimation and Synchronization for Coded MIMO-OFDM over Doubly Selective Channels
Bibliographic record
Abstract
The paper introduces a turbo (iterative) receiver design for joint channel estimation, synchronization and soft decoding in convolutional-coded multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems over time- and frequency-selective (doubly selective) channels. Employing the complex-exponential basis expansion model (CE-BEM) for representing doubly selective channels, a maximum likelihood (ML) objective function of carrier frequency offset (CFO) and MIMO time-varying channel responses (BEM coefficients) is formulated to develop a semi-blind ML framework for joint time-variant channel estimation and synchronization. To reduce the overhead of pilot signals without sacrificing estimation accuracy, the soft bit information from a soft-input soft-output (SISO) decoder is exploited in computing soft estimates of data symbols to be functioned as pilots for further enhancing the estimation accuracy after CFO and channel acquisition phase (initial coarse estimation) using pilots. In other words, the resulting semi-blind ML estimation scheme operates in conjunction with soft decoding process in a (iteratively) progressive manner to exploit remarkable gains of turbo processing (iterative extrinsic information exchange). Simulation results show that the proposed turbo joint channel estimation and synchronization scheme offers high estimation accuracy that approaches Cramer-Rao lower bounds (CRLBs) over a wide range of CFO values under low signal-to-noise ratio (SNR) conditions.
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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 | 0.002 | 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) | 0.001 | 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".