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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".