Hybrid NOMA-FDMA Assisted Dual Computation Offloading: A Latency Minimization Approach
Bibliographic record
Abstract
Edge computing has been considered as a promising solution for enabling computation-intensive yet latency-sensitive applications at resource-constrained wireless devices (WDs). In this paper, exploiting the advanced small-cell dual connectivity (DC), we investigate a paradigm of dual computation offloading in which a WD can simultaneously offload partial workloads to a cloudlet-server co-located at the macro base station (MBS) and an edge-server (ES) co-located at a small-cell based station (SBS). To facilitate the multi-user dual computation offloading, we exploit a hybrid model of non-orthogonal multiple access (NOMA) and frequency division multiple access (FDMA). Specifically, due to the SBSs’ limited channel resources, we consider that the WDs form different NOMA-groups for offloading their respective workloads to different SBSs, which improves the spectrum efficiency. Meanwhile, all WDs use FDMA for offloading their workloads to the MBS, which avoids the WDs’ co-channel interference. We formulate a joint optimization of the WDs’ partial offloading decisions, their FDMA transmission to the MBS, different NOMA-groups’ transmission to the SBSs, as well as the computing-rate allocation of the ESs and the cloudlet-server, with the objective of minimizing the overall latency for completing all WDs’ workloads. Despite the strict non-convexity of the joint optimization problem, we propose a layered yet cell-based distributed algorithm for obtaining the optimal dual offloading solution. Based on the optimal dual offloading solution, we further investigate how to properly group WDs into different NOMA-groups for offloading workloads to the corresponding SBSs, and propose a cross-entropy based learning algorithm for finding the optimal NOMA grouping scheme. Numerical results are finally provided to validate the effectiveness and efficiency of our proposed algorithms.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".