标题： 数字深度联合信源信道编码与盲训练用于自适应调制和功率控制 显示英文标题

标题： Digital Deep Joint Source-Channel Coding with Blind Training for Adaptive Modulation and Power Control

摘要： 本文提出了一种新颖的数字深度联合信源信道编码（DeepJSCC）框架，在无需大量重新训练和通信环境先验知识的情况下，实现了在各种通信环境中的鲁棒性能。 传统的数字DeepJSCC技术在适应各种通信环境时常常面临挑战，因为它们需要大量的训练开销和大量的通信数据来开发多个专用模型或一个通用模型，在预定义的通信环境中。 为了解决这一挑战，在我们的框架中，设计了一种误差自适应的盲训练策略，消除了对通信环境先验知识的需求。 这是通过使用二元对称信道对编码器输出和解码器输入之间的关系进行建模，并将比特翻转概率视为可训练参数进行优化来实现的。 在我们的框架中，还提出了一种训练感知的通信策略，该策略根据当前信道条件动态选择最优的编码器-解码器对和传输参数。 特别是，在该策略中，开发了一种自适应功率和调制控制方法，以最小化总传输功率，同时保持高性能的任务表现。 仿真结果表明，我们的框架优于现有的DeepJSCC方法，实现了更高的峰值信噪比、更低的功耗，并且在适应过程中需要显著更少的编码器-解码器对。 显示英文摘要

摘要： This paper proposes a novel digital deep joint source-channel coding (DeepJSCC) framework that achieves robust performance across diverse communication environments without requiring extensive retraining and prior knowledge of communication environments. Traditional digital DeepJSCC techniques often face challenges in adapting to various communication environments, as they require significant training overhead and large amounts of communication data to develop either multiple specialized models or a single generalized model, in pre-defined communication environments. To address this challenge, in our framework, an error-adaptive blind training strategy is devised, which eliminates the need for prior knowledge of communication environments. This is achieved by modeling the relationship between the encoder's output and the decoder's input using binary symmetric channels, and optimizing bit-flip probabilities by treating them as trainable parameters. In our framework, a training-aware communication strategy is also presented, which dynamically selects the optimal encoder-decoder pair and transmission parameters based on current channel conditions. In particular, in this strategy, an adaptive power and modulation control method is developed to minimize the total transmission power, while maintaining high task performance. Simulation results demonstrate that our framework outperforms existing DeepJSCC methods, achieving higher peak signal-to-noise ratio, lower power consumption, and requiring significantly fewer encoder-decoder pairs for adaptation.