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

2026-02-16 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Wireless Communications · Depth: Expert, extended

Summary

A new multi-user wireless Token Communications (TokenCom) framework, called Wireless TokenCom, has been developed to enhance digital semantic- and goal-oriented communications in future wireless networks. This framework addresses the challenge of Tokenizer Agreement (TA) in a multi-user downlink scenario where a base station transmits video token streams to multiple users. The authors formulate this as a mixed-integer non-convex problem and propose a hybrid reinforcement learning (RL) solution. This solution integrates a Deep Q-Network (DQN) for joint tokenizer agreement and sub-channel assignment with a Deep Deterministic Policy Gradient (DDPG) for beamforming. Simulation results demonstrate that the proposed framework significantly outperforms baseline methods in semantic quality and resource efficiency, reducing video freezing events by 68% compared to conventional H.265-based schemes for 1080p video with 16 users, and achieving approximately 10dB higher PSNR for 4 users and 64 antennas.

Key takeaway

For AI Scientists and Research Scientists developing next-generation wireless communication systems, this hybrid DQN-DDPG framework offers a robust approach to optimize multi-user video TokenCom. You should consider integrating such adaptive RL techniques to manage tokenizer agreement, resource allocation, and beamforming, especially when aiming for superior semantic quality and reduced video freezing rates in dynamic wireless environments. This can significantly improve performance over traditional codecs like H.265.

Key insights

Hybrid RL optimizes tokenizer agreement, resource allocation, and beamforming for efficient multi-user wireless video TokenCom.

Principles

• Tokens unify multimodal communication and computation.
• Tokenizer agreement is crucial for shared semantic latent space.
• RL adapts to dynamic wireless channels and semantic needs.

Method

A hybrid RL framework combines DQN for discrete tokenizer agreement and sub-channel assignment, and DDPG for continuous beamforming, to solve a mixed-integer non-convex optimization problem for multi-user wireless TokenCom.

In practice

• Reduce video freezing by 68% in high-resolution streams.
• Achieve 10dB PSNR improvement over H.265 baselines.
• Scale effectively with increasing user numbers.

Topics

• Token Communications
• Hybrid Reinforcement Learning
• Tokenizer Agreement
• Multi-user Wireless Networks
• Video Semantic Communications

Best for: AI Scientist, Research Scientist, AI Researcher, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.