A canonical wireless communication system consists of a transmitter and a receiver. The information bit stream is transmitted after coding, modulation, and pulse shaping. Due to the effects of radio frequency (RF) impairments, channel fading, noise and interference, the signal arriving at the receiver will be distorted. The receiver needs to recover the original information from the distorted signal. In this paper, we propose a new receiver model, namely DeepReceiver, that uses a deep neural network to replace the traditional receiver's entire information recovery process. We design a one-dimensional convolution DenseNet (1D-Conv-DenseNet) structure, in which global pooling is used to improve the adaptability of the network to different input signal lengths. Multiple binary classifiers are used at the final classification layer to achieve multi-bit information stream recovery. We also exploit the DeepReceiver for unified blind reception of multiple modulation and coding schemes (MCSs) by including signal samples of corresponding MCSs in the training set. Simulation results show that the proposed DeepReceiver performs better than traditional step-by-step serial hard decision receiver in terms of bit error rate under the influence of various factors such as noise, RF impairments, multipath fading, cochannel interference, dynamic environment, and unified reception of multiple MCSs.

中文翻译：

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DeepReceiver：基于深度学习的物理层无线通信智能接收器

规范的无线通信系统由发送器和接收器组成。信息比特流经过编码、调制和脉冲整形后传输。由于射频 (RF) 减损、信道衰落、噪声和干扰的影响，到达接收器的信号会失真。接收器需要从失真的信号中恢复原始信息。在本文中，我们提出了一种新的接收器模型，即 DeepReceiver，它使用深度神经网络来代替传统接收器的整个信息恢复过程。我们设计了一个一维卷积 DenseNet（1D-Conv-DenseNet）结构，其中使用全局池化来提高网络对不同输入信号长度的适应性。最终分类层采用多个二元分类器，实现多位信息流恢复。我们还通过在训练集中包含相应 MCS 的信号样本，利用 DeepReceiver 对多个调制和编码方案 (MCS) 进行统一盲接收。仿真结果表明，在噪声、射频损伤、多径衰落、同信道干扰、动态环境和统一等多种因素的影响下，所提出的 DeepReceiver 在误码率方面优于传统的逐步串行硬判决接收器。接收多个 MCS。