PABSO-DRL: Power and Beam Self-Optimization Scheme for Multiple Slices in MU-MISO Systems
Bibliographic record
Abstract
Recently, the concept of zero-touch networks (ZTNs) relying heavily on pervasive artificial intelligence (AI) algorithms for the full automation of future networks have emerged. ZTNs, empowered by AI, will play a significant role in reducing the management complexity of beyond fifth-generation (B5G) networks. One enabling technology of ZTNs is network slicing (NS), which is the cornerstone of B5G networks. However, NS faces challenges, particularly in terms of radio resource allocation management. Therefore, this paper presents an efficient self-optimization scheme for a multi-user multiple-input single-output (MU-MISO) system across different slices; named PABSO-DRL. This scheme dynamically and jointly manages power allocation and beam direction based on a deep reinforcement learning (DRL) framework, considering imperfect channel state information (CSI) and the time-varying dynamics of the NS environment. PABSO-DRL ensures high data rates for enhanced mobile broadband (eMBB) while guaranteeing high reliability for ultra-reliable low latency communications (uRLLC). The problem is formulated as a multi-objective optimization problem and solved by designing multiple deep Q-learning (DQL) agents. The proposed scheme is extensively evaluated under two scenarios: perfect and imperfect CSI, comparing its performance with four traditional benchmark algorithms, and state-of-the-art schemes. Results demonstrate the superiority of the proposed DRL scheme, even under imperfect CSI, highlighting its adaptability to various network slicing conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".