An adaptive deep learning-based UAV receiver design for coded MIMO with correlated noise
An adaptive deep learning-based UAV receiver design for coded MIMO with correlated noise
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DOI：
10.1016/j.phycom.2021.101365
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Zizhi Wang;Wenqi Zhou;Lunyuan Chen;Fasheng Zhou;Fusheng Zhu;Liseng Fan
中科院分区：
文献类型：
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作者：
Zizhi Wang;Wenqi Zhou;Lunyuan Chen;Fasheng Zhou;Fusheng Zhu;Liseng Fan
针对编码多输入多输出（MIMO）系统中噪声在时域间存在一定相关性，严重影响系统传输性能的问题，提出了一种基于深度学习的无人机接收机设计方法。为了提高系统性能，我们在发射机处采用线性卷积码，然后提出了一种基于自适应深度学习的迭代无人机接收机。迭代式无人机接收机由三部分组成：零强迫（zero-forcing， ZF）或最小均方误差（minimum mean square error， MMSE）检测器、通过捕获噪声之间的相关特征来抑制噪声的深度卷积神经网络（deep convolutional neural network， DCNN）和Viterbi译码器。特别是在码后附加的循环冗余校验（CRC）可以帮助控制检测、DCNN和解码的迭代，从而实现接收机的自适应实现。仿真结果表明，与传统接收机相比，该接收机在降低计算复杂度的同时具有更好的误码率性能。
In this paper, we propose an adaptive deep learning-based unmanned aerial vehicle (UAV) receiver design for coded multiple-input multiple-output (MIMO) systems, where the noise in the systems presents some correlation among time domain, which deteriorates the system transmission performance severely. To improve the system performance, we employ the linear convolutional code at the transmitter, and then propose an adaptive deep learning based iterative UAV receiver. The iterative UAV receiver contains three parts: the detector such as zero-forcing (ZF) or minimum mean square error (MMSE) detector, the deep convolutional neural network (DCNN) which can help suppress the noise by capturing the correlation characteristics among noise, and the decoder such as Viterbi decoding. In particular, the cyclic redundancy check (CRC) appended to the code can help control the iteration of the detection, DCNN and decoding, which leads to an adaptive implementation of receiver. Simulation results demonstrate that the proposed UAV receiver can achieve a much better bit error rate (BER) performance over conventional receivers with a reduced computational complexity.