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首页> 外文期刊>IEEE Transactions on Cognitive Communications and Networking >DeepReceiver: A Deep Learning-Based Intelligent Receiver for Wireless Communications in the Physical Layer
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DeepReceiver: A Deep Learning-Based Intelligent Receiver for Wireless Communications in the Physical Layer

机译：Deepreceiver：用于物理层中无线通信的基于深度学习的智能接收器

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摘要

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 article, 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.
机译：规范无线通信系统由发射器和接收器组成。在编码，调制和脉冲整形之后发送信息比特流。由于射频（RF）损伤的影响，通道衰落，噪声和干扰，到达接收器的信号将被扭曲。接收器需要从失真的信号中恢复原始信息。在本文中，我们提出了一个新的接收器模型，即使用深度神经网络来取代传统的接收器的整个信息恢复过程的Deepreceiver。我们设计了一维卷积Densenet（1D-CONC-DENSENET）结构，其中全局汇总用于提高网络对不同输入信号长度的适应性。在最终分类层中使用多个二进制分类器以实现多位信息流恢复。我们还利用DeepreCeiver来通过包括训练集中的相应MCS的信号样本来利用多种调制和编码方案（MCS）的统一盲目接收。仿真结果表明，在噪声，RF损伤，多径衰落，Cochannel干扰，动态环境和统一的各种因素的影响下，所提出的Deepreceiver在误码率下表现优于传统的逐步串行硬度决策接收器。接收多个MCS。

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