On the Benefits and Implementation Costs of Multi-Cell Selection in Heterogeneous Networks
Bibliographic record
Abstract
We consider a heterogeneous cellular network (HetNet) on the downlink and focus on multi-cell selection (MCS). MCS allows each user to associate with and receive data from multiple base stations (BTSs) at once, potentially boosting the network performance. We formulate a fully coordinated realistic MCS problem that provides an upper-bound on the network performance which is 20% above that of the state-of-the-art single-cell selection (SCS). However, this improvement comes at the cost of inter-BTS coordination which is not so easy to perform in practice. Hence, we focus on how to obtain this performance gain in two practical scenarios; 1) a conventional HetNet with no inter-BTS coordination, and 2) a Centralized Radio Access Network (C-RAN) HetNet. In Scenario 1, we show that SCS with periodic individual opportunities for re-associations along with local Round Robin scheduling (which requires no inter-BTS coordination) can outperform the state-of-the-art SCS by 17% without incurring a huge cost in signaling. In Scenario 2, we show that we can almost reach the upper-bound without paying for the complexity in coordinated scheduling using a simple two-step heuristic.
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 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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".