Radio Resource and Beam Management in 5G mmWave Using Clustering and Deep Reinforcement Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To optimally cover users in millimeter-Wave (mmWave) networks, clustering is needed to identify the number and direction of beams. The mobility of users motivates the need for an online clustering scheme to maintain up-to-date beams towards those clusters. Furthermore, mobility of users leads to varying patterns of clusters (i.e., users move from the coverage of one beam to another), causing dynamic traffic load per beam. As such, efficient radio resource allocation and beam management is needed to address the dynamicity that arises from mobility of users and their traffic. In this paper, we consider the coexistence of Ultra-Reliable Low-Latency Communication (URLLC) and enhanced Mobile BroadBand (eMBB) users in 5G mmWave networks and propose a Quality-of-Service (QoS) aware clustering and resource allocation scheme. Specifically, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used for online clustering of users and the selection of the number of beams. In addition, Long Short Term Memory (LSTM)-based Deep Reinforcement Learning (DRL) scheme is used for resource block allocation. The performance of the proposed scheme is compared to a baseline that uses K-means and priority-based proportional fairness for clustering and resource allocation, respectively. Our simulation results show that the proposed scheme outperforms the baseline algorithm in terms of latency, reliability, and rate of URLLC users as well as rate of eMBB users.
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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 it