Deep-Learning Based Auction-Driven Beamforming for Wireless Information\n and Power Transfer
Bibliographic record
Abstract
In this paper, we design a deep learning based resource allocation framework,\nin the form of an auction, for simultaneous information and power transfer from\na hybrid access point (AP) to information devices and energy harvesting\ndevices, respectively. Using Myerson's lemma and the concept of virtual welfare\nmaximization, we develop an optimal dominant-strategy incentive-compatible\nmechanism for the AP to maximize its expected revenue, based on the devices'\nbid profiles, valuation distributions, demand profiles, and channel state\ninformation. In so doing, we formulate the revenue maximization problem, which\nis a mixed-integer non-linear program, and propose an efficient\nBranch-and-Bound (BnB) algorithm to solve the problem using semidefinite\nrelaxation technique in each branch. Since the problem has exponential time\ncomplexity, using BnB algorithms can be impractical for real-time applications.\nTo circumvent this, a deep neural network (DNN) is proposed, and trained to\npredict the optimal mechanism for beamforming the data and the energy towards\nthe information and energy devices, respectively. We use the BnB algorithm to\nsolve the problem offline and populate the training dataset. The proposed DNN\narchitecture is indeed a multi-layer perceptron, which is trained well to map\nthe heterogeneous input to the desired output with high accuracy. Furthermore,\nwe propose a heuristic iterative solution whose accuracy performance is\ncomparable to that of the DNN-based solution. The heuristic solution has\npolynomial time complexity whereas the DNN-based solution has linear time\ncomplexity.\n
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".