Fuzzy-Based Joint User Association and Resource Allocation in HetNets
Bibliographic record
Abstract
In this paper, a user association and bandwidth allocation approach is proposed for heterogeneous networks (HetNets) using fuzzy logic controllers. Due to the heterogeneous nature of user demands, we categorize the incoming mobile users into low, medium, or high based on their data rate requirements. Similarly, the bandwidth utilization in a given small cell base station (SBS) is quantified and evaluated. Based on the per user demand and bandwidth availability, the controller decides whether a particular user should be associated with that SBS or offloaded to the macro base station (MBS). Moreover, the controller adjusts the fraction of bandwidth allocated to each user based on users data rate requirement and availability of resources towards maximizing the total data rate in the network. The proposed scheme, which is performed by SBSs in a distributed manner, is investigated and compared with two other approaches; namely, the best signal-to-interference-plus-noise ratio (SINR) which is considered as the baseline approach in the literature, and a greedy-based approach where priority in association is given to users demanding higher data rates. Our approach shows promising results regarding the improvement of data rate, bandwidth utilization, and blocking ratio, with an increased number of offloaded users.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".