Multi-UAV Trajectory Control, Resource Allocation, and NOMA User Pairing for Uplink Energy Minimization
Bibliographic record
Abstract
In this work, we study the joint optimization of multiple unmanned aerial vehicles (UAVs)’ trajectories, power allocation, user-UAV association, and user pairing for UAV-assisted wireless networks employing the nonorthogonal multiple access (NOMA) for uplink communications. The design aims to minimize the total energy consumption of ground users while guaranteeing to successfully transmit their required amount of data to the UAV-mounted base stations. The underlying problem is a mixed-integer nonlinear program (MINLP), which is difficult to solve optimally. To tackle this problem, we derive the optimal power allocation as a function of other variables, which is used to transform the optimization problem into an equivalent form. We then propose an iterative algorithm to solve the resulting optimization problem by using the block coordinate descent (BCD) method where three subproblems are solved in each iteration and this process is repeated until convergence. Specifically, given the UAVs’ trajectories and data rates, we solve the NOMA user pairing, and user-UAV association subproblem optimally by exploiting its special structure. Then, we describe how to optimize the users’ data rates and tackle the UAV trajectory optimization in the second and third subproblems, respectively, by using the successive convex approximation (SCA) method. Numerical results show that our proposed algorithm can provide efficient active-inactive schedules (by setting user’s transmit powers to zero), and lower energy consumption compared to an existing baseline, and an OMA-based resource allocation and UAV-trajectory optimization strategy.
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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".