标题： 深度学习在无线通信接收器中的应用：综述 显示英文标题

标题： Deep Learning in Wireless Communication Receiver: A Survey

摘要： 无线通信接收器的设计正在通过利用深度神经网络（DNNs）在复杂和动态环境中增强信号处理能力方面经历变革。 传统的无线接收器依赖于数学模型和算法，这些模型和算法无法从数据中适应或学习。 相比之下，基于深度学习的接收器更适合现代无线通信系统，因为它们能够从数据中学习并相应地适应。 本综述探讨了多种深度学习架构，如多层感知机（MLPs）、卷积神经网络（CNNs）、循环神经网络（RNNs）、生成对抗网络（GANs）和自编码器，并重点介绍了它们在无线接收器设计中的应用。 接收器的关键模块，如同步、信道估计、均衡、空时解码、解调、解码、干扰消除和调制分类，在正交频分复用（OFDM）、多输入多输出（MIMO）、语义通信、任务导向通信和下一代（Next-G）网络等先进无线技术的背景下进行了讨论。 本综述不仅强调了基于深度学习的接收器在未来无线通信中的潜力，还研究了基于深度学习的接收器的不同挑战，如数据可用性、安全和隐私问题、模型可解释性、计算复杂性和与传统系统的集成。 显示英文摘要

摘要： The design of wireless communication receivers to enhance signal processing in complex and dynamic environments is going through a transformation by leveraging deep neural networks (DNNs). Traditional wireless receivers depend on mathematical models and algorithms, which do not have the ability to adapt or learn from data. In contrast, deep learning-based receivers are more suitable for modern wireless communication systems because they can learn from data and adapt accordingly. This survey explores various deep learning architectures such as multilayer perceptrons (MLPs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and autoencoders, focusing on their application in the design of wireless receivers. Key modules of a receiver such as synchronization, channel estimation, equalization, space-time decoding, demodulation, decoding, interference cancellation, and modulation classification are discussed in the context of advanced wireless technologies like orthogonal frequency division multiplexing (OFDM), multiple input multiple output (MIMO), semantic communication, task-oriented communication, and next-generation (Next-G) networks. The survey not only emphasizes the potential of deep learning-based receivers in future wireless communication but also investigates different challenges of deep learning-based receivers, such as data availability, security and privacy concerns, model interpretability, computational complexity, and integration with legacy systems.