Semidefinite further relaxation on likelihood ascent search detection algorithm for high‐order modulation in massive MIMO system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent studies have shown that existing detection algorithms are not suitable for high‐order quadrature amplitude modulation (QAM) in massive MIMO system. In this paper, with an equivalent objective function by QR decomposition and further relaxation on the constrains, the authors develop an improved semidefinite further relaxation detector (SFRD), which is proved to be convex and has solutions within polynomial complexity time. Using the detection result from the proposed SFRD as the initial vector, they propose a novel semidefinite further relaxation on the likelihood ascent search (SFRLAS) detection algorithm. It has been shown through their studies that the proposed SFRLAS scheme can effectively approach the optimum bit error rate from the maximum‐likelihood detection algorithm for systems with high‐order QAM and large‐scale antennas, however, with a lower computational complexity. The spectral efficiency converges to the theoretical value at a much lower required average received signal‐to‐noise ratio. It is an effective method for high‐order QAM signal detection in massive MIMO system.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it