基于深度学习的抗窄带干扰MSK非相干接收机

窄带干扰(Narrowband Interference,NBI),作为一种敌意的频域干扰,会严重地恶化最小频移键控(Min-imum Shift Keying,MSK)非相干检测的误码率(Bit Error Rate,BER)性能.为降低窄带干扰对BER性能的影响,MSK非相干接收机一般首先对接收信号进行干扰抑制.然...

Deep Learning-Based Anti-Narrowband Interference MSK Noncoherent Receiver

Narrowband interference (NBI), as kind of hostile frequency-domain interference, can seriously impair the bit error rate (BER) performance of minimum shift keying (MSK) noncoherent detection. To reduce the impact of narrowband interference on BER performance, MSK noncoherent receivers generally first perform interference suppression on received signals. However, existing MSK noncoherent detection algorithms do not consider the distortion of MSK signals caused by interference suppression, which limits the BER performance of the MSK communication system with noncoherent detection under narrowband interference. In order to solve this problem, a deep learning-based MSK noncoherent receiver (DL-MSKNCR) is proposed in this paper. This receiver contains an interference suppression subnetwork and an MSK noncoherent detection subnetwork. Through jointly training and optimizing, the MSK noncoherent detection subnetwork can effectively cope with the distortion of MSK signals caused by the interference suppression subnetwork. The simulation results show that DL-MSKNCD significantly improves the BER performance of the MSK communication system with noncoherent detection under narrowband interference.