Delay-Optimal Distributed Scheduling in Multi-User Multi-Relay Cellular Wireless Networks
Bibliographic record
Abstract
We propose a novel scheme for delay-optimal scheduling in multi-user multi-relay cellular wireless networks. The cell area is divided into several sectors, each serviced by an individual relay station (RS). In order to have simultaneous transmissions by the users in neighbouring sectors, we assume that users of each individual sector use separate set of orthogonal channels to communicate with the RS and the base station (BS). Moreover, a separate orthogonal channel is shared among relays for transmission to the BS. For uplink communication, users are allowed to choose between two modes of transmission, namely, direct transmission mode and relayed transmission mode through a simple transmission mode selection algorithm. Users are allocated fractions of the time-slot for the first phase of transmission (from the users to the BS and the RSs) in a time-division multiple access (TDMA) fashion. For the second phase of transmission (from the RSs to the BS), each RS is allocated a fraction of the time-slot. We model the problem of end-to-end (e2e) delay-optimal scheduling as an infinite-horizon average reward Markov decision process (MDP) for users and relays in two separate stages. An online learning approach is then employed to solve the problem in a distributed manner for both users and relays in each phase of transmission. The proposed online stochastic learning solution converges to the optimal solution almost surely (with probability 1) under some realistic conditions. Simulation results show that the proposed approach outperforms the conventional scheduling schemes.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 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".