The Orthogonal-Time-Frequency-Space (OTFS) signaling is known to be resilient
to doubly-dispersive channels, which impacts high mobility scenarios. On the
other hand, the Orthogonal-Frequency-Division-Multiplexing (OFDM) waveforms
enjoy the benefits of the reuse of legacy architectures, simplicity of receiver
design, and low-complexity detection. Several studies that compare the
performance of OFDM and OTFS have indicated mixed outcomes due to the plethora
of system parameters at play beyond high-mobility conditions. In this work, we
exemplify this observation using simulations and propose a deep neural network
(DNN)-based adaptation scheme to switch between using either an OTFS or OFDM
signal processing chain at the transmitter and receiver for optimal
mean-squared-error (MSE) performance. The DNN classifier is trained to switch
between the two schemes by observing the channel condition, received SNR, and
modulation format. We compare the performance of the OTFS, OFDM, and the
proposed switched-waveform scheme. The simulations indicate superior
performance with the proposed scheme with a well-trained DNN, thus improving
the MSE performance of the communication significantly.

在本研究中，我们使用模拟结果验证了 Orthogonal-Time-Frequency-Space（OTFS）和 Orthogonal-Frequency-Division-Multiplexing（OFDM）之间的性能差异，并提出了基于深度神经网络（DNN）的自适应方案来优化均方误差（MSE）性能。通过观察信道条件、接收信噪比和调制格式，训练 DNN 分类器在发送器和接收器之间切换使用 OTFS 或 OFDM 信号处理链，仿真结果表明使用该方案可以显著提高通信的 MSE 性能。