A Markov State-Transition-Aware Event-Driven Compensation Method for All-Digital Transmitters

All-digital transmitters (ADTx) synthesize radio-frequency (RF) waveforms through coordinated binary switching, where delay mismatch, parasitic effects, and dynamic response differences among switching paths introduce transient distortions with strong state- and transition-path dependence. Conventional waveform-based digital predistortion (DPD) treats the ADTx as an equivalent nonlinear system and lacks awareness of internal state transitions, limiting targeted compensation of high-risk events. This paper proposes an adaptive state-transition compensation method based on four-dimensional binary state-vector Markov modeling and event-driven local predistortion. The four switching states of a five-level ADTx are encoded as a binary vector, and a first-order Markov model with 16 states characterizes the state evolution. By jointly considering transition probability, state-change magnitude, and steady-state probability, a transition-importance matrix quantifies all 256 transition paths and identifies the effective ones, for which a path-specific finite impulse response (FIR) compensation kernel library is developed. An event-driven mechanism activates the corresponding kernel only at transition events, enabling localized compensation of transient distortions. Simulations show an adjacent channel power ratio (ACPR) improvement of 11.5 dB and an error vector magnitude (EVM) reduction of 4.8% over the uncompensated system, while only approximately 19% of transition events require online compensation, substantially reducing real-time computational overhead.