An ML-assisted OTFS vs. OFDM adaptable modem
This work addresses the challenge of waveform selection in communication systems, particularly for high-mobility scenarios, but it is incremental as it builds on existing OTFS and OFDM methods with a machine learning adaptation.
The paper tackled the problem of choosing between OTFS and OFDM waveforms for optimal communication performance by proposing a DNN-based adaptation scheme that switches between them based on channel conditions, SNR, and modulation format, resulting in significantly improved MSE performance as shown in simulations.
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.