Enhancement of 5G's Aerial Coverage with Unmanned Aerial Vehicles (UAVs): Apply AI-Based Path Planning and Interference Mitigation
Bibliographic record
Abstract
5G networks have undergone fast development, and provide high-throughput, ultra-reliable, and low-latency communication on urban and rural scenarios. Yet providing seamless coverage is a challenge—particularly in areas with terrain or infrastructure obstacles, as well as when facing temporary surges in demand. Unmanned Aerial Vehicles (UAVs) have been considered as a promising approach to expanding the coverage of 5G aerial networks with flexibility of deployment, mobility, and low-cost properties. This paper introduces an AI-enabled framework for smart path planning and inference management to enhance the performance of a UAV-based 5G BS. We present a hybrid system design, where UAVs are mobility targets during the maneuver and AI algorithms, namely reinforcement learning (RL) and swarm optimization are employed for the dynamic placement, and considering the user distribution, instantaneous network load and environmental changes. The UAVs serve as in-air small cells and can be relocated to improve coverage and signal quality. Real-time path optimization is conducted online via a DQN-driven controller, which actively reduces the interference between UAVs and between UAVs and ground infrastructure based on a game-theoretical frequency allocation and beamforming algorithms. Simulation results over urban and suburban topologies reveal that the proposed approach can achieve up to 35% and 28% enhancement in SINR and user coverage, respectively, compared with homogeneous configurations with static or heuristic UAV deployment. The AI algorithms have the added benefit of lowering energy use by optimizing UAV itineraries and hover times. This work provides a new integration solution for UAV mobility, AI-driven decision making and radio resource management design in the next-generation 5G contexts. The proposed solution is envisaged being quickly deployed into disaster zones, for rural connectivity, in high densified events, as well as for military communications systems. In addition, the framework paves the way for the integration of aerial networks into the 6G ecosystem focusing on autonomy, intelligence and collaborative coverage strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".