Cascaded doubly-selective channel estimation in multi-relay AF OFDM transmissions
Bibliographic record
Abstract
This paper studies the problem of channel estimation in amplify-and-forward (AF) multi-relay transmissions over time- and frequency-selective (doubly selective) channels. To avoid two separate channel estimation processes of source-to-relay and relay-to-destination links, a cascaded doubly selective channel model is formulated to characterize the source-to-relay-to-destination (SRD) channel. Time-varying SRD channel gains are projected onto different basis expansion functions to attain dimension reduction in the formulated channel model. In estimating the cascaded SRD channel responses at the destination, the presence of multiple relays gives rise to the ambiguity problem due to the use of a single pilot signal transmitted from the source. To circumvent this problem, time-variant amplifying factors at relays are introduced to be used in maximum-likelihood-based cascaded multi-relay channel estimation. Simulation results show that basis expansion models (BEMs) and time-variant amplifying factors can efficiently facilitate a single estimation process of different cascaded doubly selective SRD channels in a multi-relay system. Furthermore, for a fixed pilot overhead, a relationship between space diversity gains (with multiple relays) and channel estimation accuracy is also numerically illustrated.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".