PAPR-Aware Multimodal Token Transmission in MLLM-Based Multiuser Networks

RESUMO

Task-oriented semantic communication (SemCom) empowered by multimodal large language models (MLLMs) has recently emerged as a promising paradigm for efficiently transmitting multimodal information, yet transmitting token embeddings over OFDM channels faces critical challenges due to hardware impairments, particularly the high peak-to-average power ratio (PAPR) that severely degrades energy efficiency under nonlinear power amplifiers. In this paper, we propose a novel PAPR-aware task-oriented multimodal token transmission framework. Specifically, we propose MAPS (Multimodal AI-driven PAPR-aware Token Transmission) scheme for energy-efficient multiuser wireless communications. The key challenge is to jointly ensure inter-modal consistency, task-relevant token transmission, and PAPR reduction. To address this, we adopt a two-stage training strategy that integrates joint cross-modal alignment and PAPR reduction, followed by task-oriented fine-tuning. MAPS transmits multimodal token embeddings over an OFDM channel under a realistic Modified Rapp power amplifier. Specifically, we incorporate a trainable linear projection in the text branch for effective gradient propagation, design a balanced multimodal PAPR loss with variance-based equalization across users, and anchor the reconstruction loss to a fixed semantic target to preserve cross-modal alignment. Simulation results show that MAPS achieves balanced PAPR reduction across all modalities while maintaining strong inter-modal consistency. Under power amplifier nonlinearity, MAPS outperforms baseline schemes in both audio-visual question answering (AVQA) accuracy (64.5% gain) and task-oriented energy efficiency (64.4% gain) at an input back-off (IBO) of 3 dB, highlighting its effectiveness for energy-efficient multimodal semantic communication.