Jointly optimal base station assignment for up- and downlink CDMA through space division duplex
Bibliographic record
Abstract
Nonuniform traffic can severely degrade the performance of CDMA cellular systems. To alleviate this, load balancing schemes have been proposed which let the neighboring cells accommodate the traffic of a heavily loaded cell. Most of the previous works have addressed load balancing for the uplink since it was considered to be the bottleneck of the system performance. In fact it is very likely for the uplink to become congested before the downlink in the presence of conventional symmetric services (e.g. voice). However emergence of the new highly asymmetric services has put much more demand on the downlink and there may be many cases where the downlink of a cell is heavily loaded without significant uplink-loading. This motivates us to perform downlink load balancing by assigning an appropriate base station to each user. In this paper we propose an algorithm based on space division duplex which assigns optimal base stations for the up- and downlink independently.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".