Maximizing the Energy Efficiency in Integrated Sub-6 GHz, mmWave and THz Wireless Networks
Bibliographic record
Abstract
To cope with the spectrum scarcity of emerging mobile applications, using higher frequency bands (such as THz) becomes a necessity. Combining simultaneously higher and lower bands is then an important step for future cellular networks. Meanwhile, resource management plays a dominant effect on the system performance especially when different quality of service requirements are considered. In this paper, we formulate and investigate a joint optimization problem of resource allocation in integrated sub-6 GHz, mmWave and THz networks to maximize the system energy efficiency (EE). Therefore, we propose efficient centralized and distributed low-complexity greedy solutions. Also, we propose more efficient multi-agent reinforcement learning (MARL) solutions where users, modeled as agents, collaborate to learn and converge to the optimal user association that maximizes the EE. Simulation results show the EE provided by the proposed solutions and illustrate the superiority of the MARL-based solutions.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".