Lattice Sequential Decoder for Coded MIMO Channel: Performance and Complexity Analysis
Bibliographic record
Abstract
In this paper, the performance limit of lattice sequential decoder for lattice space-time coded MIMO channel is analysed. We determine the rates achievable by lattice coding and sequential decoding applied to such channel. The diversity-multiplexing tradeoff (DMT) under lattice sequential decoding is derived as a function of its parameter---the bias term. The bias parameter is critical for controlling the amount of computations required at the decoding stage. Achieving low decoding complexity requires increasing the value of the bias term. However, this is done at the expense of losing the optimal tradeoff of the channel. We show how such a decoder can bridge the gap between lattice decoder and low complexity decoders. Moreover, the computational complexity of lattice sequential decoder is analysed. Specifically, we derive the tail distribution of the decoder's computational complexity in the high signal-to-noise ratio regime. Similar to the conventional sequential decoder used in discrete memoryless channel, our analysis reveals that the tail distribution of such low complexity decoder is of a Pareto-type. Also, the tail exponent of the complexity distribution is shown to be equivalent to the DMT achieved by such coding and decoding schemes. We show analytically how minimum-mean square-error decision feed-back equalization can significantly improve the tail exponent and as a consequence reduce computational complexity. An interesting result shows that there exists a cut-off multiplexing gain for which the average computational complexity of the decoder remains bounded as long as we operate below such value.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".