Deep Reinforcement Learning for Resource Allocation in Multi-Band and Hybrid OMA-NOMA Wireless Networks
Bibliographic record
Abstract
Exploiting the advantages of both non-orthogonal multiple access technique and millimeter-wave communications requires joint efficient resource allocation techniques toward satisfying the stringent requirements of future mobile communication systems. This paper focuses on a multi-band (i.e., millimeter-wave band and sub-6 GHz band) wireless network where both orthogonal and non-orthogonal multiple access techniques coexist. A joint optimization of user association, transmit power allocation, sub-channel assignment, and multiple access technique selection is investigated to maximize the down-link sum-rate under a minimum rate requirement per user and power constraints. The problem is formulated as a non-convex mixed-integer optimization problem; then, it is proved to be$\mathcal {NP}$-hard. First, simple greedy and meta-heuristic solutions are proposed. Then, since model-based approaches have generally a high computational complexity, model-free centralized and distributed approaches based on deep reinforcement learning technique are proposed. The latter are based on multiple parallel deep neural networks to generate resource allocation solutions. The proposed approaches are evaluated and compared. Simulation results corroborate the high performance offered by the proposed solutions for stationary and mobile users. They also highlight the benefits of employing hybrid orthogonal and non-orthogonal multiple access scheme in multi-band systems in terms of down-link sum-rate and user fairness.
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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 | 0.000 |
| 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) | 0.001 | 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".