Spatial pulse position modulation multi-classification detector based on deep learning
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摘要:
为有效避免最大似然（ML）检测复杂的计算过程，根据空间脉冲位置调制（SPPM）信号的特点，将深度神经网络（DNN）与分步检测相结合，提出了一种基于深度学习的SPPM多分类检测器。在该检测器中，利用DNN建立接收信号与PPM符号间的非线性关系，并以此为准则完成在线接收PPM符号的检测，从而有效避免了对PPM符号的穷搜索检测过程。结果表明，采用本文检测器后，SPPM系统在大幅降低检测复杂度的前提下，取得了近似最优的误比特性能，同时还克服了K均值聚类（KMC）分步分类检测所出现的错误平台效应。当PPM阶数为64时，本文方法较ML检测和线性均衡DNN检测器的计算复杂度分别降低了约95.45%、33.54%。
Abstract:In order to effectively avoid high computational complexity when using Maximum Likelihood (ML) detection, a deep learning-based Spatial Pulse Position Modulation (SPPM) multi-classification detector is proposed by combining a Deep Neural Network (DNN) and step detection. In the detector, the DNN is used to establish a non-linear relationship between the received signal and the PPM symbols. Thereafter, the subsequent received PPM symbols are detected according to this relationship, so as to avoid the exhaustive search process of PPM symbol detection. The simulation results show that with the proposed detector, the SPPM system approximately achieves optimal bit error performance on the premise of greatly reducing detection complexity. Meanwhile, it overcomes the error platform effect caused by K-Means Clustering (KMC) step classification detection. When the PPM order is 64, the computational complexity of the proposal is about 95.45% and 33.54% lower than that of ML detectors and linear equalization DNN detectors, respectively.
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图 1 基于深度学习的SPPM多分类检测器
Figure 1. Deep learning-based SPPM multi-classification detector
图 2 不同检测方法时(2,3,4)-SPPM系统的误比特性能
Figure 2. Bit error performance of a (2,3,4)-SPPM system with different detection methods
图 3 不同检测方法时两种SPPM系统的误比特性能
Figure 3. Bit error performances of two SPPM systems with different detection methods
图 4 湍流对系统误比特性能的影响
Figure 4. Effect of turbulence on the bit error performance of the system
图 5 PPM阶数对系统误比特性能的影响
Figure 5. Influence of PPM order on the system’s bit error performance
图 6 算法复杂度与PPM阶数L的关系
Figure 6. Relationship between algorithm complexity and PPM order L
表 2 多分类检测器的超参数
Table 2. Hyperparameters of the multi-classification detector
超参数 值 各隐藏层神经元数目 F1=64,F2=98,F3=48 Batch 1.25×104 Batch_size 24 轮次Epoch 50 激活函数 Relu+Sigmoid 损失函数 Cross Entropy Loss 优化器 SGD 学习率 0.001 
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表 3 各算法计算复杂度
Table 3. Computational complexity of each algorithm
检测算法 计算复杂度/Flops ML 检测 $ {N_t}L\left( {2{N_t}{N_r}L + 2{N_r}L - 1} \right) $ KMC分步分类检测[23] $ {N_t}\left( {2{N_t}{N_r}L + {\text{2}}{N_r}L - 1} \right) + L\left( {3{N_r}L - 1} \right) $ 线性均衡DNN检测器[17] $ 2\left( {{{\left( {{N_r}L} \right)}^2} + {N_r}L{F_1} + {F_1}{F_2} + {F_2}{F_3} + {F_3}{{\log }_2}\left( {{N_t}L} \right)} \right) + {\log _2}\left( {{N_t}L} \right) $ DNN多分类检测器 $ 2\left( {{N_r}L{F_1} + {F_1}{F_2} + {F_2}{F_3} + {F_3}L + N_t^2{N_r}L + {N_t}{N_r}L} \right) + 3L - {N_t} - 1 $
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