A Benchmark for Joint Channel Allocation and User Scheduling in Flexible Heterogeneous Networks
Bibliographic record
Abstract
Flexible duplexing is a promising technique to improve the spectral efficiency of future cellular networks, which has been proposed mainly to provision asymmetric uplink (UL) and downlink (DL) traffic scenarios, through flexible channel allocations. However, this flexibility in the channel allocation process, which is responsible for allocating the underlying channel to different base stations in a heterogeneous network (HetNet), has brought new technical challenges due to the introduction of complex UL-to-DL and DL-to-UL interference scenarios. This paper analyzes the joint channel allocation (CA) and user scheduling (US) process for orthogonal frequency-division multiple access-based flexible HetNets, while considering exact inter-cell/intra-cell interferences. The resulting joint problem is a large-scale mixed-integer nonlinear programming problem that is computationally intractable, therefore, it has been re-formulated into a convex upper bound problem to find benchmark solutions for CA in flexible HetNets. Since, no new CA scheme has been proposed yet for the HetNets employing flexible duplexing techniques, we discuss the efficacy of existing schemes under various UL and DL traffic scenarios.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".