Low complexity implementations of sphere decoding for MIMO detection
Bibliographic record
Abstract
A new low complexity sphere decoding method for multiple-input multiple-output (MIMO) maximum-likelihood (ML) detection is proposed. One method that reduces the complexity of sphere decoding is the decoding order of MIMO sphere decoder using the soft-output signal of a suboptimum receiver as a reference. We refer to this method as ordered sphere decoder and we try to reduce its complexity. In order to do this, we use the reliability information of the transmitted vector to do channel ordering. This means that we make decisions on the elements of the transmitted vector starting from its most reliable element. To this end, we arrange the reliabilities in an increasing order. This ordering will define a permutation. The elements of the reference signal and also the columns of the channel matrix will be arranged according to this permutation. Then, we detect the permuted transmitted vector using ordered sphere decoder with the new permuted channel matrix and reference signal. In our proposed method, we start detecting the transmitted vector from its most reliable element and for each element, we start from the most probable transmitted symbol based on the information from the reference signal. This kind of ordering will help finding the candidate transmitted vectors quickly. Our method results in reducing the complexity of sphere decoder specially in low signal to noise ratios without compromising the performance of ML detection.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 0.003 |
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".