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

TL;DR

This paper introduces a reinforcement learning framework for multi-user wireless Token Communications, enabling efficient tokenizer agreement and resource allocation, significantly improving semantic quality and reducing freezing events in video transmission.

Contribution

It presents a novel hybrid RL approach combining DQN and DDPG for joint tokenizer agreement, sub-channel assignment, and beamforming in multi-user wireless TokenCom scenarios.

Findings

Outperforms baseline methods in semantic quality.

Reduces freezing events in video transmission by 68%.

Enhances resource efficiency in multi-user wireless TokenCom.

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 communications in future wireless networks. To establish a shared semantic latent space, the transmitters/receivers in TokenCom need to agree on an identical tokenizer model and codebook. To this end, an initial Tokenizer Agreement (TA) process is carried out in each communication episode, where the transmitter/receiver cooperate to choose from a set of pre-trained tokenizer models/ codebooks available to them both for efficient TokenCom. In this correspondence, we investigate TA in a multi-user downlink wireless TokenCom scenario, where the base station equipped with multiple antennas transmits video token streams to multiple users. We formulate the corresponding mixed-integer non-convex problem, and propose a hybrid reinforcement learning (RL) framework that 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 show that the proposed framework outperforms baseline methods in terms of semantic quality and resource efficiency, while reducing the freezing events in video transmission by 68% compared to the conventional H.265-based scheme.