Coexistence design that enables the emerging candidate waveforms to interoperate with legacy orthogonal frequency division multiplexing (OFDM) is essential for sixth-generation standardization. This paper focuses on orthogonal time frequency space (OTFS)—a representative waveform for high-mobility scenarios—and proposes a novel coexistence paradigm between OTFS and OFDM, termed cross-domain division multiplexing (XDM), utilizing the different signal representations across multiple domains. XDM is formulated through sparse mapping in the delay-Doppler domain and periodicity-based partial superposition in the time-frequency domain. We prove that XDM holds several favorable invariant properties, which enable tractable interference characterization and suppression even under dynamic channels. We also show that XDM supports flexible numerologies and provide some standardization-oriented practice examples. For practical implementation, XDM transceivers are designed for near interference-free coexistence leveraging the invariant properties. We first develop a cross-domain waveform-level interference cancellation algorithm with ultra-low complexity for single OTFS user coexisting with OFDM. More generally, a domain-transform embedded factor-graph is constructed for multiple OTFS users, over which a cross-domain expectation propagation algorithm is proposed to achieve almost lossless recovery of the coexisting signals. Through a novel variance transfer technique, we prove that the detection errors of coexisted OTFS and OFDM respectively converge to their counterparts under interference-free transmission. Simulations show that XDM exhibits BER loss about 0.5 dB compared to interference-free transmission under various channel conditions and system configurations.

Precise unmanned aerial vehicle (UAV) localization is crucial for the successful operation of UAV swarms, particularly in environments where global navigation satellite system (GNSS) signals are denied or unavailable. This paper introduces a cooperative positioning framework for such scenarios. Considering the resource and coverage constraints of UAVs, a multi-objective cooperative positioning algorithm using a projection factor graph (FG) is proposed. This algorithm achieves dimensionality reduction and interaction of the positioning message among UAVs, employing low-complexity techniques for parameter estimation. Simulation results demonstrate that the proposed algorithm achieves linear complexity, significantly lower than cooperative benchmarks like SPAWN with cubic. Furthermore, it provides an 82.5% increase in positioning accuracy over non-cooperative methods, yielding accuracy approaching that of the high-complexity SPAWN algorithm.