Secure Batch-Based Resource Allocation for Green Cognitive MIMO Indoor Flying Networks

The growing demand for advanced indoor communication capabilities in sixth-generation (6G) networks has led to extensive research into integrating Cognitive Radio (CR) and multiple-input multiple-output (MIMO) technologies with Unmanned Aerial Vehicles (UAVs). The integration of CR and MIMO with UAVs contributes to the green Open Radio Access Network (O-RAN) paradigm by leveraging CR’s advantages in interoperability, adaptability, and software-defined nature, along with UAVs’ flexible 3D movement and MIMO’s energy-efficient attributes. However, securing communication in CR-enabled MIMO-equipped UAVs in O-RAN networks against jamming attacks presents significant challenges, particularly in designing resource allocation algorithms that are both secure and energy-efficient in the presence of jamming attacks. This paper presents a secure and jamming-resistant green channel-assignment algorithm designed for indoor uplink communication in MIMO- and CR-enabled O-RAN-supported UAV networks. The proposed algorithm aims to maximize served transmissions with minimal total transmission power, exploiting MIMO, CR adaptability, and jamming awareness. Leveraging the Lagrangian technique, a closed-form formula for per-antenna power allocation is derived to solve the power minimization problem for each UAV over the available channels. Using the obtained per-UAV powers on idle channels, a power-efficient batch-based channel-assignment problem is formulated, presented as unimodular binary-linear programming solvable through polynomial-time linear programming. Compared to CR MIMO-based algorithms, the proposed algorithm significantly improves overall network performance under jamming attacks by employing user-batching with jamming awareness.