부산대학교 도서관

Unmanned aerial vehicle (UAV) ultraviolet (UV) communication has garnered significant attention due to its broad range of applications. Currently, there are few decoding algorithms specifically tailored for UAV UV multiple-input-single-output (MISO) communication scenarios. We propose an enhanced maximum a posteriori (MAP) and log-likelihood ratio belief propagation (LLRBP) algorithm for MISO UV communication systems. This algorithm strategically ranks interference signals based on the signal-to-interference plus noise ratio (SINR), effectively eliminates errors in transmitter information recovery, and is named the SINR-MAP-LLRBP Algorithm. Building upon this algorithm, we establish the optimal quantitative relationship for the signal-to-noise ratio (SNR) in the MISO UVC system. Additionally, we introduce an enhanced decoding algorithm for MISO UV communication systems, leveraging the residual-based loop iteration methodology, named as the Res-SINR-MAP-LLRBP Algorithm. This algorithm provides an additional decoding gain compared to the SINR-MAP-LLRBP Algorithm.