This paper introduces a new way to use UAVs in the 5G wireless network, based on Fuzzy C-Means (FCM) clustering and Transformer architecture for improved coverage, less energy consumption, and better Quality of Service (QoS). The new method improves user grouping by incorporating fuzzy distance functions into the FCM algorithm and it further trains Transformer models to interpret the spatial and temporal complexities of the network. This enables UAV replacement, such that now they can be placed more intelligently, especially in congested or unserved regions. Tests with the C2TM dataset demonstrated that the approach outperformed traditional ones by K-Medoid and plain FCM. Energy consumption by the UAV was reduced by 20.100% to 3,597 joules against 4,500 joules using K-Medoid, and the network performed better in terms of Mbps, reaching an average throughput of 180 and with latency at 40 milliseconds and packet loss at 0.8%. This shows a great change in how reliable the network is and what the user experiences. The plan also made the UAV use better by getting more close grouping within and farther apart grouping between, making sure resources are used well. This work points to how much can be done when smart computer models are joined with grouping methods to solve hard issues in the wireless networks of the future. The way suggested here gives a strong answer for changing places, making sure there are scale-able, useful, and trusted communication services. These results make a way for more study in UAV-based communication systems, looking at going deeper with the use of smart learning methods and quick math finding.
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