A New ICI Matrices Estimation Scheme Using Hadamard Sequence for OFDM Systems
Bibliographic record
Abstract
The intercarrier interference (ICI) matrix for the orthogonal frequency division multiplexing (OFDM) systems usually has a fairly large dimension. The traditional least-square solution based on the pseudo-inverse operation, therefore, has its limitation. In addition, the provision of a sufficiently long training sequence to estimate the complete ICI matrix is not feasible, since it will result in severe throughput reduction. In this paper, we derive a lower bound for the mean-square estimation error among the least-square ICI matrix estimators using different training sequences and prove that the minimum mean-square error (MMSE) optimality is attained when the training sequences in different OFDM blocks are orthogonal to each other, regardless of the sequence length. We also prove that the asymptotical mean-square estimation error using the maximal-length shift-register sequences (m-sequences) as in the existing communication standards is 3 dB larger than that using the perfectly orthogonal sequences for ICI matrix estimation. Thus, we propose to employ the training sequences based on the Hadamard matrix to achieve a highly efficient and optimal ICI matrix estimator with minimum mean-square estimation error among all least-square ICI matrix estimators. Meanwhile, our new scheme involves only square computational complexity, while other existing least-square methods require the complexity proportional to the cube of the ICI matrix size. Analytical and experimental comparisons between our new scheme using Hadamard sequences and the existing method using m-sequences (pseudo-random sequences) show the significant advantages of our new ICI matrix estimator. The proposed method is most suitable for OFDM systems with large amount of subcarriers, using high order of subcarrier modulation, and designed for high-end of RF frequency band, where accurate ICI estimation is crucial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".