A Quantization Bit Allocation Method Based on Semantic Importance in Digital Semantic Communication
Digital semantic communication not only retains the advantages of semantic communication,but also is compatible with existing communication systems,with quantization being a key step in implementing digital semantic communication.Quantization in digital semantic communication requires multiple quantizers to quantify multi-dimensional semantic features.Due to limited hardware and constrained number of quantization bits,an efficient bit allocation scheme for quantizers is necessary.To solve this problem,a bit allocation algorithm based on semantic importance is proposed.Firstly,a quantized bit allocation problem based on semantic importance is constructed.Under the limitation of the maximum number of bits,the importance of different semantic information is considered to minimize the distortion caused by quantization and transmission.Then,a quantization bit allocation algorithm based on reinforcement learning is proposed with the bit allocation range as the action space and the semantic feature as the state space.Finally,the proposed algorithm is trained and the optimal bit allocation strategy is obtained.The simulation results show that the proposed algorithm converges quickly.In the image classification task scenario,the cross entropy of the proposed algorithm decreases by up to 48.16％ and the classification accuracy increases by up to 12.65％ ,compared with the benchmark algorithm.
digital semantic communicationsemantic importancequantization bit allocationreinforcement learning
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