Radio Resource and Interference Management in Uplink Multi-Cell MU-MIMO Systems with ZF Post-Processing
Bibliographic record
Abstract
This paper presents our initial investigations of noise enhancement control when decoding uplink transmissions in multi-cell multiuser multiple- input multiple-output (MU-MIMO) systems with zero- forcing (ZF) post-processing. This problem has been partially addressed within simplified cellular system topologies and with single antenna terminals. However, applying existing solutions directly to a multi cell system is not viable due to the noise enhancement that would heavily affect the inter-cell interference (ICI). Therefore, this paper proposes a cross-layer resource allocation to improve the performance by utilizing a low complexity scheduling (user and antenna selection) and a power allocation method. The proposed scheduling algorithm extends our previous work to a multi cell system where the base and the mobile stations are equipped with multiple antennas. When allocating the power levels to different users in order to manage the multiple access interference (MAI) in MU-MIMO environment, we utilize Newton's method for resource optimization due to its efficiency in finding the combination of spatial streams and power levels to improve the average bit error rates (BERs) and their statistics for different users. This improvement in BERs is documented in terms of median and average BER performance in the system under study with and without centralized ICI cancelation.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| 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 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".