基于DNN的无人机数据OFDM传输技术

doi:10.12305/j.issn.1001-506X.2022.02.41

摘要/Abstract

摘要：

针对无人机信道含多径、多普勒频移, 还易受到外来干扰和高功率放大带来的非线性失真影响的问题, 提出一种基于深度神经网络(deep neural network, DNN)的无人机正交频分复用(orthogonal frequency division multiplexing, OFDM)数据传输技术。下行链路采用OFDM系统, 在接收端进行解调之后利用最小二乘(least square, LS)信道估计和迫零(zero forcing, ZF)算法做初步信号检测, 然后输入到由BiLSTM和全连接层组成的DNN中进行信道估计和信号检测优化, 并恢复数据流。仿真结果表明, 与传统插值方法相比, 所提方法在无人机起降、飞行和盘旋等3种状态下，信道估计性能具有明显优势, 比特误差率性能提升至少一个数量级; 在非线性失真和外来干扰的影响下, 所提方法仍具有显著性能优势, 不仅简化了系统对非线性失真和干扰的处理模块, 而且提高了系统的可靠性。

关键词: 无人机, 时变多径信道, 正交频分复用, 深度神经网络, 干扰, 信道估计, 信号检测

Abstract:

The unmmand aerial vehicle (UAV) channel contains multipath, Doppler frequency shift. It is easily affected by external interference and nonlinear distortion caused by high power amplification. To solve those problems a data transmission technology of UAV orthogonal frequency division multiplexing (OFDM) based on deep neural network (DNN) is proposed. In the downlink, OFDM system is used. After demodulation and borrowing at the receiver, the least square (LS) channel estimation and zero forcing (ZF) algorithm are used for preliminary signal detection, and then input into the DNN composed of BiLSTM and full connected layer for channel estimation and signal detection optimization, and data stream recovery. Simulation results show that compared with the traditional interpolation method, the proposed method has obvious advantages in channel estimation performance under three states of UAV takeoff and landing, flight and hovering, and the bit error rate performance is improved by at least one order of magnitude; under the influence of nonlinear distortion and external interference, the proposed method still has significant performance advantages, which not only simplifies the processing module of nonlinear distortion and interference, but also improves the stability of the system qualitative.

Key words: unmanned aerial vehicle (UAV), time varying multipath channel, orthogonal frequency division multiplexing (OFDM), deep neural network (DNN), interference, channel estimation, signal detection

中图分类号:

TN914.34

刘步花, 丁丹, 杨柳, 薛乃阳, 刘仲谦. 基于DNN的无人机数据OFDM传输技术[J]. 系统工程与电子技术, 2022, 44(2): 696-702.

Buhua LIU, Dan DING, Liu YANG, Naiyang XUE, Zhongqian LIU. OFDM data transmission technology of UAV based on deep neural network[J]. Systems Engineering and Electronics, 2022, 44(2): 696-702.

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	飞行速度/(m/s)	飞行高度/m	多普勒频移/Hz	莱斯因子/dB	时延/μs	能量损失/dB
起飞降落	40	100	320	20	[0, 0.33, 0.66]	[0, -7, -20]
途中飞行	134	750	1 072	15	[0, 2.5, 5]	[0, -7, -15.2]
任务盘旋	40	1 050	320	10	[0, 3.5, 6]	[0, -0.97, -10]

信道状态	SNR
信道状态	0 dB	5 dB	10 dB	15 dB	20 dB	25 dB
起飞降落状态	0.185	0.066	0.013	0.003	0.001	5.0e-4
途中飞行状态	0.190	0.074	0.018	0.005	0.002	0.001
任务盘旋状态	0.218	0.072	0.018	0.004	0.001	6.02e-4

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