Precoding and Trajectory Design for UAV-Assisted Integrated Communication and Sensing Systems
Bibliographic record
Abstract
Benefited from the characteristics of high mobility, low cost and convenient deployment, unmanned aerial vehicles (UAVs) can be deployed in wireless communication systems as mobile base stations (BSs) to improve the communication performance of users. In addition, by deploying communication and sensing equipment and supporting efficient resource sharing of communication and sensing technologies, UAVs are expected to act as high-performance aerial platforms which integrate communication and sensing technologies. In this paper, a multiantenna UAV-enabled joint communication and sensing scenario is examined by jointly considering the flight energy of the UAV, multi-antenna transmissions, and the user service requirements. Two optimization problems are respectively formulated for various UAV states. In particular, the problem of communication precoding and UAV flight trajectory optimization is formulated as a minimum user rate maximization problem and the joint optimization problem of UAV sensing position, communication and sensing precoding is formulated as a minimum target detection probability maximization problem. Since the minimumrate maximization problem is a non-convex optimization problem, which is difficult to solve directly, we decompose the original optimization problem into a communication precoding design subproblem and a UAV trajectory design subproblem, and solve the two subproblems successively by applying an alternate iteration method. Specifically, a zero forcing (ZF) algorithm is put forward for solving the communication precoding design subproblem. A successive convex approximation (SCA) algorithm is applied to determine the optimal trajectory of the UAV. Based on the optimal trajectory of the UAV, the sensing position optimization problem is modeled as a weighted distance minimization problem, and then a heuristic algorithm is proposed to obtain the optimal positions. Finally, a ZF algorithm-based joint communication and sensing precoding is presented. The effectiveness of the proposed algorithm is verified by simulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".