Reinforcement Learning-powered Semantic Communication via Semantic Similarity (2108.12121v2)

Abstract: We introduce a new semantic communication mechanism - SemanticRL, whose key idea is to preserve the semantic information instead of strictly securing the bit-level precision. Unlike previous methods that mainly concentrate on the network or structure design, we revisit the learning process and point out the semantic blindness of commonly used objective functions. To address this semantic gap, we introduce a schematic shift that learns from semantic similarity, instead of relying on conventional paired bit-level supervisions like cross entropy and bit error rate. However, developing such a semantic communication system is indeed a nontrivial task considering the non-differentiability of most semantic metrics as well as the instability from noisy channels. To further resolve these issues, we put forward a self-critic reinforcement learning (RL) solution which allows an efficient and stable learning on any user-defined semantic measurement, and take a step further to simultaneously tackle the non-differentiable semantic channel optimization problem via self-critic stochastic iterative updating (SCSIU) training on the decoupled semantic transceiver. We have firstly tested the proposed method in the challenging European-parliament dataset, which confirms the superiority of our method in revealing the semantic meanings, and better handling the semantic noise. Apart from the experimental results, we further provide an in-depth look at how the semantic model behaves, along with its superb generalization ability in real-life examples. An RL-based image transmission extension is also exemplified, so as to prove the generalization ability and motivate future discussion.

Overview of Reinforcement Learning-Powered Semantic Communication via Semantic Similarity

The paper introduces SemanticRL, an innovative approach to semantic communication that leverages reinforcement learning (RL) to enhance the transmission of semantic information, rather than focusing solely on bit-level precision. The primary goal of SemanticRL is to address the limitations of current semantic communication systems which inadequately handle the semantic gap due to their reliance on traditional bit-level objective functions, such as cross-entropy and bit error rate.

Key Contributions

1. Semantic Similarity Over Bit-Level Accuracy: SemanticRL shifts the focus from ensuring bit-level accuracy to maximizing semantic similarity between transmitted and received messages. It employs self-critic RL to directly optimize non-differentiable semantic metrics, such as BLEU and CIDEr scores, distinguishing it from conventional methods that use differentiable objectives.
2. Self-Critic Reinforcement Learning: The paper introduces a self-critic RL mechanism, which stabilizes the optimization by providing low-variance policy gradient updates. This strategy allows the system to optimize for semantic similarity without requiring additional parameters, thus maintaining an efficient training process.
3. Semantic Transceiver Design: SemanticRL also tackles the challenge of optimizing the non-differentiable stochastic channel by employing self-critic stochastic iterative updating (SCSIU). This method decouples the transceiver components and applies RL to both the encoding and decoding processes, demonstrating comparable performance to traditional joint source-channel coding (JSCC).
4. Robustness and Generalization: Experimental results using the European Parliament dataset show that SemanticRL outperforms baseline models (those based on cross-entropy) in high-order semantic similarity metrics. It also exhibits robustness across different signal-to-noise ratio (SNR) levels and generalizability to other contexts, such as varying channel conditions.

Implications and Future Directions

The proposed SemanticRL framework presents significant implications for both the theoretical understanding and practical application of semantic communication systems:

• Theoretical Implications: By demonstrating how semantic similarity can be integrated into the learning process, SemanticRL offers a new perspective on how machine learning models can be tuned to address the semantic aspect of communication. This approach challenges existing paradigms in information theory by foregrounding semantic accuracy over traditional measures of communication efficacy.
• Practical Applications: The capability of SemanticRL to function under non-differentiable and unpredictable conditions makes it highly applicable in real-world wireless communication settings, where the channel state might not conform to ideal assumptions. This approach can lead to more resilient communication systems that maintain semantic integrity even in noisy environments.
• Expansion to Broader Contexts: While the paper primarily explores text communication, the underlying principles of SemanticRL could be extended to other media types such as images and video. This opens avenues for future research into task-specific implementations where semantic communication can enhance overall system performance.

The introduction of SemanticRL represents a noteworthy advancement in the field of semantic communications, which is poised to play a critical role in the evolution of next-generation communication systems. By emphasizing semantic understanding, this research not only improves the robustness of communication systems but also sets the stage for novel techniques that align with the nuanced demands of human-centered technologies. Future work may explore more sophisticated models and reward functions to further capitalize on the potential that SemanticRL has introduced.