首页|Neural Network-Aided Multiple-Symbol Noncoherent Detection Scheme of LDPC Coded MPSK Receiver for Unmanned Aerial Vehicle Communications
Neural Network-Aided Multiple-Symbol Noncoherent Detection Scheme of LDPC Coded MPSK Receiver for Unmanned Aerial Vehicle Communications
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NETL
NSTL
World Scientific
Multiple-symbol noncoherent detection (MSND) with the aid of Neural Networks (NNs) for low-density parity-check (LDPC) coded multiple phase shift keying (MPSK) signals is studied for Unmanned aerial vehicle (UAV) communications. In the traditional MSND scheme, the number of the candidate sequences grows exponentially with respect to the length of the symbol observation period. Implementing the optimal bit log-likelihood ratio (LLR) for decoding is challenging, even when the observed symbol period is two. In this paper, we first proposed an improved scheme to reduce the number of the candidate sequences by phase combination, the phase is uniformly quantized into L discrete values. We find that the performance requirements can be well met when the phase quantization order is only 4. Then we utilize Back Propagation neural networks (BPN) to compute the bit LLR. To enhance the training efficiency of our NNs and achieve better performance, we also uniformly quantize the carrier phase offset (CPO) into discrete states. The decoding convergence is accelerated significantly compared to the improved traditional scheme. The complexity is reduced to a certain extent within the acceptable range of performance loss.
Di Wu、Gege Wei、Gaolei Song、Yongen Li、Gaoyuan Zhang
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Maintenance and Engineering Department, Civil Aviation Flight University of China, Luoyang College, Luoyang 471001,P. R. China||School of Information Engineering, Henan University of Science and Technology, Luoyang 471023,P. R. China
School of Information Engineering, Henan University of Science and Technology, Luoyang 471023,P. R. China
Science and Technology Innovation Center of Intelligent system, Longmen Laboratory, Luoyang 471000,P. R. China
NETL
NSTL
World Scientific
