In learning-based semantic communications, neural networks have replaced different building blocks in traditional communication systems. However, the digital modulation still remains a challenge for neural networks. The intrinsic mechanism of neural network based digital modulation is mapping continuous output of the neural network encoder into discrete constellation symbols, which is a non-differentiable function that cannot be trained with existing gradient descend algorithms. To overcome this challenge, in this paper we develop a joint coding-modulation scheme for digital semantic communications with BPSK modulation. In our method, the neural network outputs the likelihood of each constellation point, instead of having a concrete mapping. A random code rather than a deterministic code is hence used, which preserves more information for the symbols with a close likelihood on each constellation point. The joint coding-modulation design can match the modulation process with channel states, and hence improve the performance of digital semantic communications. Experiment results show that our method outperforms existing digital modulation methods in semantic communications over a wide range of SNR, and outperforms neural network based analog modulation method in low SNR regime.
翻译:在基于学习的语义通信中,神经网络取代了传统通信系统中的不同构件。然而,数字调制仍然是神经网络的一个挑战。基于神经网络的数字调制的内在机制是将神经网络编码器的连续输出绘制成离散星座符号,这是一个无法用现有梯度下游算法培训的不可区分的功能。为了克服这一挑战,我们在本文件中开发了一个与BPSK调制的数码语义通信联合编码-调制方案。在我们的方法中,神经网络输出每个星座点的可能性,而不是进行混凝土绘图的可能性。因此使用了随机代码而不是确定性代码,为每个星座点的符号保留了更接近可能性的更多信息。联合编码-调制设计可以使调制进程与频道状态相匹配,从而改进数字语义通信的性能。实验结果表明,我们的方法超越了SNR广泛范围内的语管通信中现有的数字调制方法,并且超越了以低调制系统为基础的神经系统。