Energy-Efficient Resource Allocation and Subchannel Assignment for NOMA-Enabled Multiaccess Edge Computing
Bibliographic record
Abstract
In this article, we study an energy-efficient nonorthogonal multiple access (NOMA) enabled multiaccess edge computing (MEC) system with strict latency requirements. We aim to minimize the energy consumption of all users by optimizing the resource allocation (including power and computation resources) and subchannel assignment, subject to the given latency constraint. The formulated problem, however, is a nonconvex combinatorial optimization problem. Nevertheless, we decompose the problem into a resource allocation subproblem and a subchannel assignment subproblem, and then solve the two subproblems iteratively. On one hand, we investigate the hidden convexity of the resource allocation subproblem under the optimal conditions, and propose an efficient algorithm to optimally allocate the resources by dual decomposition methods. On the other hand, we formulate the subchannel assignment subproblem into an integer linear programming problem and strictly prove that the problem is nondeterministic polynomial-time hard. We then solve it optimally by branch-and-bound methods, which is shown to be efficient in extensive simulations. Moreover, through considerable simulation results, we show that our proposed algorithm helps greatly reduce users’ energy consumption when communication resources (e.g., bandwidth) are limited. Additionally, it is verified that NOMA outperforms orthogonal multiple access in multiuser latency-sensitive MEC systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".