Closer Twins Model: Consistent Design of Modem Scheme and Channel Estimation Under High-Mobility Scenarios

Communication objectives with high mobility bring severe Doppler shifts, causing the inter-carrier interference of orthogonal frequency division multiplexing (OFDM) system, which raises the requirements of novel modem schemes. However, existing modem schemes for high-mobility communications face challenges in adapting to diverse channel environments, involving complex channel estimation and etc. Fortunately, the potential of deep learning (DL) has been exploited in various communication applications. In order to design the consistent and robust modem scheme for different channel environments, we propose the DL-based architecture termed the closer twins (CTs) model, which borrows the idea from the Siamese structure in contrastive learning. In specific, two identical network backbones like twins can simultaneously process different channel inputs and make outputs consistent. We design a convlutional neural network called modem network (ModNet) as the backbone for the design of consistent and robust modem scheme. Moreover, to make traditional channel estimation and interpolation methods applicable to the designed modem scheme, a training-aided strategy called random-pilot (R-P) is proposed. In R-P strategy, we simulate the process of conventional channel estimation to modify the objective function of the modem scheme design. Furthermore, the performance of traditional channel estimation can be further improved by DL-based methods. We utilize the CTs model and design the backbone called estimation matrix network (EMNet) to optimize a linear channel estimation method, who outperforms the traditional methods with a similar complexity. Simulation results demonstrate that the proposed modem scheme outperforms OFDM, especially with high Doppler spread. The channel estimation strategy, supported by the R-P strategy and EMNet, achieves lower normalized mean square error compared with traditional methods, contributing to more reliable transmission.