This paper explores the integration of deep learning techniques for joint sensing and communications, with an extension to semantic communications. The integrated system comprises a transmitter and receiver operating over a wireless channel, subject to noise and fading effects. The transmitter employs a deep neural network, namely an encoder, for joint operations of source coding, channel coding, and modulation, while the receiver utilizes another deep neural network, namely a decoder, for joint operations of demodulation, channel decoding, and source decoding to reconstruct the data samples. The transmitted signal serves a dual purpose, supporting communication with the receiver and enabling sensing. When a target is present, the reflected signal is received, and another deep neural network decoder is utilized for sensing. This decoder is responsible for detecting the target's presence and determining its range. All these deep neural networks, including one encoder and two decoders, undergo joint training through multi-task learning, considering data and channel characteristics. This paper extends to incorporate semantic communications by introducing an additional deep neural network, another decoder at the receiver, operating as a task classifier. This decoder evaluates the fidelity of label classification for received signals, enhancing the integration of semantics within the communication process. The study presents results based on using the CIFAR-10 as the input data and accounting for channel effects like Additive White Gaussian Noise (AWGN) and Rayleigh fading. The results underscore the effectiveness of multi-task deep learning in achieving high-fidelity joint sensing and semantic communications.

翻译：本文探索了深度学习技术在联合感知与通信中的集成应用，并将其扩展至语义通信领域。该集成系统包含一个通过无线信道工作的发射机与接收机，需应对噪声与衰落效应的影响。发射机采用深度神经网络（编码器）实现信源编码、信道编码与调制的联合操作；接收端则利用另一深度神经网络（解码器）联合执行解调、信道解码与信源解码以重构数据样本。发射信号具有双重功能：既支持与接收机的通信，又实现感知功能。当存在目标时，反射信号被接收，并通过另一深度神经网络解码器进行感知处理，该解码器负责检测目标存在性并测定其距离。所有深度神经网络（包含一个编码器与两个解码器）均通过多任务学习进行联合训练，充分考虑了数据与信道特性。本文进一步将语义通信纳入框架，通过在接收端引入另一深度神经网络（任务分类器）实现语义集成。该分类器对接收信号的标签分类保真度进行评估，从而增强通信过程中的语义整合能力。本研究以CIFAR-10作为输入数据，并考虑加性高斯白噪声（AWGN）与瑞利衰落等信道效应。实验结果表明，多任务深度学习在实现高保真联合感知与语义通信方面具有显著有效性。