Multi-Layer Linear Processing for Uplink Massive MIMO Systems in the Presence of Unequal-Power Co-Channel Interferers
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Bibliographic record
Abstract
We propose a novel multi-layer linear receiver for massive MIMO systems that can provide a range of complexity/ performance trade-offs. The proposed method consists of splitting the antenna array into a number of subsets of size greater than all users, which is further divided into smaller subsets. Then, optimum combining (OC) is applied on each subset in a first layer. The outputs are combined using OC again at a second layer. Finally, the resulting outputs are combined using maximal-ratio combining (MRC). This design is inspired by our previous work which proposes to implement two layer processors to achieve a good trade-off between performance and complexity. Simulation results show that our method approaches the performance of a conventional OC combiner, albeit with significantly reduced complexity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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