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RL framework enhances wireless token communications for video transmission

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel framework for Wireless Token Communications (TokenCom) that utilizes reinforcement learning to improve efficiency and semantic quality in multi-user video transmission. The system integrates a Deep Q-Network (DQN) for tokenizer agreement and sub-channel assignment with a Deep Deterministic Policy Gradient (DDPG) for beamforming. This approach aims to establish a shared semantic latent space by enabling transmitters and receivers to agree on identical tokenizer models and codebooks. Simulation results indicate a significant reduction in video transmission freezing events, outperforming conventional methods. AI

IMPACT This research could lead to more efficient and higher-quality video streaming over wireless networks by improving how devices agree on data representations.

RANK_REASON Academic paper detailing a new framework for wireless communications. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

RL framework enhances wireless token communications for video transmission

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Academic paper detailing a new framework for wireless communications. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Farshad Zeinali, Mahdi Boloursaz Mashhadi, Rahim Tafazolli ·

    Wireless TokenCom: RL-Based Tokenizer Agreement for Multi-User Wireless Token Communications

    arXiv:2602.12338v2 Announce Type: replace Abstract: Token Communications (TokenCom) has recently emerged as an effective new paradigm, where tokens are the unified units of multimodal communications and computations, enabling efficient digital semantic- and goal-oriented communic…

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