Joint Clustering and Resource Allocation Optimization in Ultra-Dense Networks with Multiple Drones as Small Cells...

Area of Science:

• Wireless communication networks
• Game theory applications
• Resource management

Background:

• Ultra-dense networks (UDNs) face significant intercell interference.
• Drones as small-cell base stations offer flexible deployment but require efficient management.
• Massive multiple-input multiple-output (MIMO) enhances spectral efficiency.

Purpose of the Study:

• To propose a game-theoretic framework for clustering and resource allocation in UDNs with drone base stations.
• To mitigate intercell interference and maximize network energy efficiency (EE).
• To compare the performance of the proposed game-based methods against traditional approaches.

Main Methods:

• A coalition game is formulated for clustering small cells based on signal-to-interference ratio.
• Resource allocation is decomposed into subchannel and power allocation subproblems.
• The Hungarian method is used for subchannel allocation; Stackelberg game and centralized algorithms are used for power allocation.

Main Results:

• The proposed game-based clustering effectively mitigates intercell interference.
• The distributed Stackelberg game algorithm achieves high network energy efficiency.
• The game-based approach demonstrates superior performance in execution time and EE compared to centralized and traditional methods.

Conclusions:

• Game theory provides an effective framework for optimizing resource allocation in drone-based UDNs.
• The proposed coalition and Stackelberg game models enhance network performance and energy efficiency.
• The developed methods offer a practical solution for managing interference and optimizing resources in future wireless networks.