Multi-users space-time modulation with QAM division for massive uplink communications
Bibliographic record
Abstract
In this paper, we consider the design of multi-users space-time modulation (MUSTM) for an uplink MIMO system with one base station equipped with the massive number of antennas and N single-antenna users, where it is assumed that only large scale channel coefficients are available at both the transmitter and the receiver. For such a system, a novel concept called uniquely factorable (UF) MUSTM is introduced. Then, using our recently developed framework on uniquely decomposable constellation group with energy-efficient quadrature amplitude modulation (QAM), and properly and timely assigning each sub-constellation to each user at each time slot, we develop a machinery method for systematically designing a family of invertible UF-MUSTM with flexible data rates in order to assure the reliable estimation of the transmitted signal as well as of the channel for the massive MIMO system. In addition, a simple cross-correlation receiver is proposed to efficiently and effectively detect such UF-MUSTM. Its pair-wise error probability (PEP) is derived, showing that our proposed invertible UF-MUSRM enables full receiver diversity. Furthermore, the optimal closed-form power allocation and the optimal user constellation assignment are found to maximize the worst-case coding gain under a peak power constraint on each user and each time slot.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".