Essai Ali Mohamed Hassan, Taha Ibrahim B M
Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Qena, Qena, Egypt.
Department of Electrical Engineering, College of Engineering, Taif University, Taif, Saudi Arabia.
PeerJ Comput Sci. 2021 Aug 26;7:e682. doi: 10.7717/peerj-cs.682. eCollection 2021.
In this study, a deep learning bidirectional long short-term memory (BiLSTM) recurrent neural network-based channel state information estimator is proposed for 5G orthogonal frequency-division multiplexing systems. The proposed estimator is a pilot-dependent estimator and follows the online learning approach in the training phase and the offline approach in the practical implementation phase. The estimator does not deal with complete certainty for channels' statistics and attains superior performance in the presence of a limited number of pilots. A comparative study is conducted using three classification layers that use loss functions: mean absolute error, cross entropy function for kth mutually exclusive classes and sum of squared of the errors. The Adam, RMSProp, SGdm, and Adadelat optimisation algorithms are used to evaluate the performance of the proposed estimator using each classification layer. In terms of symbol error rate and accuracy metrics, the proposed estimator outperforms long short-term memory (LSTM) neural network-based channel state information, least squares and minimum mean square error estimators under different simulation conditions. The computational and training time complexities for deep learning BiLSTM- and LSTM-based estimators are provided. Given that the proposed estimator relies on the deep learning neural network approach, where it can analyse massive data, recognise statistical dependencies and characteristics, develop relationships between features and generalise the accrued knowledge for new datasets that it has not seen before, the approach is promising for any 5G and beyond communication system.
在本研究中，针对5G正交频分复用系统，提出了一种基于深度学习双向长短期记忆（BiLSTM）递归神经网络的信道状态信息估计器。所提出的估计器是一种依赖导频的估计器，在训练阶段采用在线学习方法，在实际实现阶段采用离线方法。该估计器不处理信道统计的完全确定性，并且在导频数量有限的情况下具有卓越的性能。使用三个使用损失函数的分类层进行了对比研究：平均绝对误差、第k个互斥类的交叉熵函数以及误差平方和。采用Adam、RMSProp、SGdm和Adadelat优化算法，使用每个分类层来评估所提出估计器的性能。在符号错误率和准确率指标方面，所提出的估计器在不同的仿真条件下优于基于长短期记忆（LSTM）神经网络的信道状态信息、最小二乘和最小均方误差估计器。给出了基于深度学习BiLSTM和LSTM的估计器的计算和训练时间复杂度。鉴于所提出的估计器依赖深度学习神经网络方法，它能够分析海量数据、识别统计依赖性和特征、建立特征之间的关系并将积累的知识推广到其之前未见过的新数据集，该方法对于任何5G及以后的通信系统都很有前景。
