Symbol detection method for low-complexity OTFS systems for highly dynamic unmanned cluster intelligent data link

Data Link network is the foundation and key to support the efficient aggregation and sharing of various resources on the battlefield. Unmanned cluster intelligent data chain under strong confrontation environment will become the key to support unmanned system to form combat capability. At present, the existing unmanned system communication is susceptible to the Doppler effect due to the orthogonal frequency division multiplexing (OFDM) modulation technique, while the orthogonal time frequency space (OTFS) has better performance than OFDM in high mobility wireless communication scenarios. In this paper, we propose a deep neural network-based symbol detection method for OTFS system to address the problems of high complexity and poor detection performance of the symbol reception detection algorithm of OTFS for unmanned system intelligent data chain. The method uses a training set to train the constructed deep neural network, detects the received symbols in the time-delayed Doppler domain, and obtains the estimated value of the transmitted symbols. The method is able to reduce the overall symbol detection complexity of the OTFS system, increase the speed of detecting received symbols, and significantly improve the reception performance of OTFS.