Channel-Aware Waveform Selection Criteria Across Different Waveform Domains

TL;DR

This paper introduces a comprehensive channel model for 6G waveform evaluation, revealing how different waveforms perform under realistic, complex propagation conditions and proposing a channel-aware prioritization framework.

Contribution

It develops a scalable, generalized channel model incorporating realistic dynamics and derives effective input-output relationships for main 6G waveforms, exposing their interference structures.

Findings

AFDM and OTFS maintain spectral efficiency under sparse, stationary conditions.

OFDM and DFT-s-OFDM can be tuned for better reliability in complex channels.

Waveform performance varies significantly with channel conditions and stationarity.

Abstract

Waveform evaluation for sixth generation (6G) networks has largely relied on sparse and quasi-stationary channel models that enabled mathematical tractability, diversity gains, and Doppler robustness. However, such models obscure the propagation complexity of dense urban environments, high mobility scenarios, and heterogeneous network deployments. This paper sheds light on a generalized and scalable channel model that incorporates cluster birth-death dynamics, Doppler spectral spreading, time-varying delays, and piecewise local stationarity. Based on this model, the effective input-output relationships of the main 6G waveforms are derived, exposing waveform dependent interference structures that remain hidden under conventional sparse assumptions. Building on these effective channels, a channel-aware waveform prioritization framework is developed based on delay-Doppler resolvability, stationarity conditions, effective signal-to-interference-plus-noise ratio (SINR), and user equipment (UE) cell distribution. Simulation results under the proposed channel model using 3GPP CDL parameters confirm that affine frequency division multiplexing (AFDM) and orthogonal time frequency space (OTFS) retain their spectral efficiency advantage and path combining gains only under sparse, resolvable, stationarity conditions, whereas orthogonal frequency division multiplexing (OFDM) and discrete Fourier transform spread (DFT-s)-OFDM can be both tuned to achieve superior reliability and more stable performance under the proposed channel model.