Sphere Decoding for Millimeter Wave Massive MIMO
Bibliographic record
Abstract
Massive MIMO can significantly increase the data rate of a wireless communications system. The requirement for even greater data transmission rates in 5G systems has prompted the consideration of the largely unused millimeter wave (mmWave) band. To mitigate the high path loss at mmW frequencies and the poor scattering nature of the mmW channel (fewer paths exist), this paper proposes the use of a uniform planar array (UPA) hybrid beamforming technique with large antenna arrays and the sphere decoding algorithm to improve the performance of mmWave massive MIMO systems. When perfect Channel State Information (CSI) is available at the base Station (BS) and mobile Station (MS), computer simulations have shown that a gain of 10 dB or more can be achieved by using the Sphere Decoding algorithm compared to a system with Zero-forcing (ZF) or minimum mean- square error (MMSE) hybrid beamforming. Moreover, the complexity of the SD algorithm can be reduced by efficiently selecting the radius.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".