Load balancing in cellular networks with user-in-the-loop: A spatial traffic shaping approach
Bibliographic record
Abstract
Long term user rate in cellular networks is the product of spectral efficiency achieved and the resources (time/frequency slots) allocated. The former is related to the received SINR, while the latter is limited by the load of the associated cell. The max-SINR cell association strategy has been used in cellular networks from GSM to LTE. This strategy maximizes the possible achieved spectral efficiency but fails to account for the load imbalance. Recently, there have been several investigations on load-aware cell association as an approach to match the traffic demand with the traffic supply, in which a user may associate to a less loaded cell, even though it does not necessarily provide the maximum SINR. In other words, a user is associated with a cell to get more share of resources at the cost of lower spectral efficiency. This paper goes beyond that by proposing a new load balancing approach that can simultaneously increase the user received SINR and the share of allocated resources. This is achieved by the user-in-the-loop (UIL) paradigm, which encourages the user to move to a new location that maximizes the utility function considering the received SINR, cell load and the probability of moving. Numerical results show that the UIL can increase the mean user rate substantially in comparison to the max-SINR or the load-aware cell association strategy, and also results in a more balanced load across the network.
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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 | 0.000 |
| 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 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".