Reinforcement Learning-powered Semantic Communication via Semantic Similarity

• URL: http://arxiv.org/abs/2108.12121v1
• Date: Fri, 27 Aug 2021 05:21:05 GMT
• Title: Reinforcement Learning-powered Semantic Communication via Semantic Similarity
• Authors: Kun Lu, Rongpeng Li, Xianfu Chen, Zhifeng Zhao, Honggang Zhang
• Abstract summary: We introduce a new semantic communication mechanism, whose key idea is to preserve the semantic information instead of strictly securing the bit-level precision. We show that the commonly used bit-level metrics are vulnerable of catching important semantic meaning and structures. We put forward a reinforcement learning (RL)-based solution which allows us to simultaneously optimize any user-defined semantic measurement.
• Score: 13.569045590522316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a new semantic communication mechanism, whose key idea is to preserve the semantic information instead of strictly securing the bit-level precision. Starting by analyzing the defects of existing joint source channel coding (JSCC) methods, we show that the commonly used bit-level metrics are vulnerable of catching important semantic meaning and structures. To address this problem, we take advantage of learning from semantic similarity, instead of relying on conventional paired bit-level supervisions like cross entropy and bit error rate. However, to develop such a semantic communication system is indeed a nontrivial task, considering the nondifferentiability of most semantic metrics as well as the instability from noisy channels. To further resolve these issues, we put forward a reinforcement learning (RL)-based solution which allows us to simultaneously optimize any user-defined semantic measurement by using the policy gradient technique, and to interact with the surrounding noisy environment in a natural way. We have testified the proposed method in the challenging European-parliament dataset. Experiments on both AWGN and phase-invariant fading channel have confirmed the superiority of our method in revealing the semantic meanings, and better handling the channel noise especially in low-SNR situations. Apart from the experimental results, we further provide an indepth look at how the semantics model behaves, along with its superb generalization ability in real-life examples. As a brand new method in learning-based JSCC tasks, we also exemplify an RL-based image transmission paradigm, both to prove the generalization ability, and to leave this new topic for future discussion.

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