Intelligent electromagnetic resource allocation for UAV swarms using union equalization algorithms

This paper proposes Union Least Mean Square (U-LMS) and Union Recursive Least Square (U-RLS) algorithms for optimizing electromagnetic resource allocation in unmanned aerial vehicle (UAV) swarm communication. These algorithms jointly address link delay and clock synchronization issues in one-to-many scenarios, reducing bit error rates (BER) across multiple targets. U-LMS minimizes the mean square error iteratively, while U-RLS ensures faster convergence. Simulation results show that U-RLS exhibits the fastest convergence speed and smallest error in signal equalization, and ultimately achieves communication performance far superior to conventional methods. Compared with U-LMS, U-RLS can achieve a 3.4 dB signal-to-noise ratio (SNR) gain and a bandwidth factor gain of 16 under the 7% hard-decision forward error correction (HD-FEC) threshold of 3.8×10^-3. In addition, U-LMS and U-RLS are both able to continuously extract diversity gain from multi-link signals and achieve an average gain of 2.15 dB per UAV, enhancing overall system performance and making the proposed approach effective for UAV swarm operations.