OTFS-ISAC system channel estimation based on GAN-LSTM network in urban rail train-infrastructure scenario - YANG Qian; SU Hongsheng; TAO Wanglin; LIU Dawei (Tongxin xuebao)
In order to solve the problem of integrated sensing and communication (ISAC) signal transmission channel estimation in commercial B5G/6G urban rail train-infrastructure scenario, a channel estimation method based on deep learning was proposed. An ISAC signal transmission system model based on orthogonal time frequency space (OTFS) modulation was established, the OTFS pilot was introduced, with OTFS pilot introduced to aid, CGAN-LSTM combining conditional generative adversarial network (CGAN) and long short-term memory (LSTM) network was designed. Chaos game optimization (CGO) algorithm was combined with classical Adam optimizer to optimize the network parameters, and the optimized network was used to complete the channel estimation. Simulation results show that the proposed method is superior to traditional channel estimation methods in normalized mean square error and bit error rate, and provides necessary data basis for ISAC signal detection and recovery.