Energy-Optimal Multiple Access Computation Offloading: Signalling Structure and Efficient Communication Resource Allocation
Bibliographic record
Abstract
Computation offloading enables energy-limited devices to expand the scope of the computational tasks that they can complete within specified latency requirements. However, when multiple devices seek to offload, effective allocation of resources becomes crucial. In this paper, we develop an energy-optimal signalling structure for a K-user offloading system, and an efficient algorithm for allocating the communication resources provided by that structure. The signalling structure is designed to exploit the differences between the users' latencies and the reduction in interference that arises when a device completes its offloading. That results in a time-slotted signalling structure, which is then optimized for a multiple access scheme that exploits the full capabilities of the channel (FullMA). The optimized signalling structure enables us to substantially reduce the dimension of the resource allocation problem, and to develop efficient algorithms to tackle that problem for both the binary and partial offloading cases. Our numerical experiments illustrate that the proposed time-slotted FullMA signalling structure significantly reduces the energy consumption of the devices compared to some existing methods that employ orthogonal multiple access schemes, such as TDMA, and to those with FullMA, but sub-optimal single-time-slot signalling structures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".