Joint Resource Block Allocation and Beamforming with Mixed-Numerology for eMBB and URLLC Use Cases
Bibliographic record
Abstract
Mixed-numerology has been proposed in the Third Generation Partnership Project (3GPP) standard for the fifth generation (5G) wireless networks, where flexible subcarrier spacing (SCS) can be applied to support uses cases with different quality-of-service (QoS) requirements. In this paper, we study the joint design of resource block allocation and beamforming with mixed-numerology for enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) use cases. We consider multiple multi-antenna base stations (BSs) cooperatively provide services to the users. By using beamforming, inter-user interference can be mitigated and a resource block can be utilized by more than one user. Short packet transmission is considered for URLLC users to satisfy their low-latency requirements. We formulate a mixed-integer nonlinear programming problem to maximize the aggregate throughput of eMBB users while guaranteeing the throughput, reliability, and latency requirements of URLLC users. We propose a low-complexity algorithm, which leverages fractional programming and successive convex approximation (SCA), to obtain the solutions. Simulation results show that our proposed algorithm can improve the aggregate eMBB throughput by 30% compared with the fixed-numerology based approach.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".