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arXiv 2202.03244cs.ITeess.SPmath.IT
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用于无线通信优化的在线深度神经网络

Online Deep Neural Network for Optimization in Wireless Communications

Jiabao Gao, Caijun Zhong, Geoffrey Ye Li, Zhaoyang Zhang

摘要:

近年来,深度神经网络(DNN)以其较强的学习能力和较低的测试复杂度在智能通信系统的设计中得到了广泛的应用。然而,当前大多数基于DNN的离线方法仍然存在性能不佳、泛化能力有限和可解释性差的问题。在本文中,我们提出了一种基于DNN的在线方法来解决无线通信中的一般优化问题,其中为每个数据样本训练专用DNN。通过将优化变量和目标函数分别作为网络参数和损失函数,优化问题可以通过网络训练等价求解。由于在线优化的性质和有意义的网络参数,所提出的方法具有很强的泛化能力和可解释性,而其优越的性能是通过一个实际的例子,联合波束形成的智能反射面(IRS)辅助多用户多输入多输出(MIMO)系统。仿真结果表明,该算法的性能优于传统的离线DNN和迭代优化算法,但复杂度较低。

英文摘要:

Recently, deep neural network (DNN) has been widely adopted in the design of intelligent communication systems thanks to its strong learning ability and low testing complexity. However, most current offline DNN-based methods still suffer from unsatisfactory performance, limited generalization ability, and poor interpretability. In this article, we propose an online DNN-based approach to solve general optimization problems in wireless communications, where a dedicated DNN is trained for each data sample. By treating the optimization variables and the objective function as network parameters and loss function, respectively, the optimization problem can be solved equivalently through network training. Thanks to the online optimization nature and meaningful network parameters, the proposed approach owns strong generalization ability and interpretability, while its superior performance is demonstrated through a practical example of joint beamforming in intelligent reflecting surface (IRS)-aided multi-user multiple-input multiple-output (MIMO) systems. Simulation results show that the proposed online DNN outperforms conventional offline DNN and state-of-the-art iterative optimization algorithm, but with low complexity.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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