Complexity reduction of the MLSD/MLSDE receiver using the adaptive state allocation algorithm
Bibliographic record
Abstract
The idea of adaptive state allocation (ASA) algorithm is used in this paper to substantially reduce the computational complexity of the maximum-likelihood sequence detection and estimation (MLSD/MLSDE) receiver without a significant degradation in its performance. In the ASA algorithm, the total number of states assigned to the trellis and the number of states selected from the entire set are changed adaptively based on the short-term power of the channel impulse response (CIR) or its estimate. The ASA algorithm is a combination of two methods: adaptive threshold (AT) and adaptive state partitioning (AP). In the AT method, a threshold value is formulated based on the probability of removing the correct state in the trellis diagram. At each time, only the paths whose costs are less than the minimum cost (corresponding to the best survivor path) plus the threshold value are retained and are extended to the next trellis stage. The AT method significantly reduces the computational complexity of the regular MLSDE mostly at high signal-to-noise ratio (SNR) with a negligible loss in performance. Simulation results for fading channels show that the AT method typically selects one trellis state (the minimum possible number of states) at high SNRs. In the AP method, the branch metrics are fused and diffused adaptively by using the Kullback-Leibler (KL) distance metric invoked for quantifying the differences between the probability density functions of the correct and incorrect branch metrics in the trellis. The adaptation is done such that the channel coefficients with short-term power less than a threshold are assumed to be zero in computing the branch metrics. The AP method decreases the computational complexity of the regular MLSDE at low SNRs.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".