Optimum Resource Allocation in MU-MIMO OFDMA Wireless Systems
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Bibliographic record
Abstract
With the introduction of Advanced Antenna Systems (AAS) in cellular communication technologies, such as LTE and NR, the same resource can be allocated simultaneously to multiple users via spatial multiplexing. However, this raises new challenges to resource allocation strategy to decide opportunistic co-scheduling on a given resource to increase the system capacity without adversely impacting the user fairness. In addition, the effects of transmit power sharing and inter-user interference on co-scheduling need to be considered in the allocation decision. In this paper, a generic framework for resource allocation considering all these aspects of Multi-User Multi-Input Multi Output (MU-MIMO) in cellular Orthogonal Frequency Division Multiple Access (OFDMA) systems is presented and a scheduling algorithm for optimum resource allocation is provided. The performance of the proposed algorithm is evaluated for a two dimensional AAS using SCM-5G channel model.
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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 | 0.000 | 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 | 0.000 | 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