OFDM系统中深度神经网络指导的IQ不平衡补偿算法

正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)是现代移动通信中一项重要的物理层通信技术,并且OFDM系统要求子载波间严格正交。然而,在实际系统中,振荡器和滤波器等器件的非理想特性会导致同相正交(In-phase and Quadrature-phase,IQ)不平衡,从而破坏子载波的正交性,严重影响OFDM系统的性能。通过研究IQ不平衡对OFDM系统的影响,提出了一种并联深度神经网络架构下的IQ不平衡补偿算法。该算法利用了深度神经网络不依赖于模型的特点,直接从接收到的频域信号恢复原输入信号的二进制序列,并利用干扰信号来自镜像子载波的先验知识来初始化模型驱动的神经网络,加快其网络优化的收敛速度。仿真结果表明,该算法能有效地补偿IQ不平衡失真,并且在幅度和相位失真的补偿上,其性能都优于传统的基于导频的最小二乘补偿算法,证明了深度学习方法解决物理层问题的优越性。

IQ imbalance compensation algorithm with deep neural network in OFDM systems

OFDM (orthogonal frequency division multiplexing) is an essential technique in the physical layer of wireless communications, and OFDM system requires rigid orthogonality between subcarriers. However, in practical systems, the imperfection of components like the oscillator and filter would introduce IQ(in-phase and quadrature-phase) imbalance into the system. The IQ imbalance would infect the orthogonality between subcarriers and decrease the system performance. The effect of IQ imbalance was discussed and an IQ imbalance compensation algorithm with the guidance of parallel DNN (deep neural network) was proposed. The deep neural network relies rarely on mathematic models, and the proposed algorithm utilizes this feature to recover the original signal from the received signal in the frequency domain to its original binary sequence of transmitted signal directly. Meanwhile, the prior knowledge that the interference comes from the image aliasing effect was utilized to initialize the model-driven neural network. Simulation results proves that the proposed algorithm can effectively compensate IQ imbalance distortion, and it outperforms traditional LS algorithm based on pilots in both amplitude and phase compensation and proves the superiority of deep learning solutions for issues in the physical layer.