利用张量结构--多模式储备池计算及其在大规模MIMO中的应用
Harnessing Tensor Structures -- Multi-Mode Reservoir Computing and Its Application in Massive MIMO
摘要：
本文介绍了一种新的神经网络结构--多模式储备池计算（Multi-ModeRC）。它继承了RC的动态机制，并使用张量作为底层数据格式来处理神经网络的前向路径和损耗优化。与传统的RC结构（例如单模RC）相比，多模RC具有更低的复杂度，具有相当的泛化性能。在此基础上，提出了一种基于交替最小二乘的多模式RC学习算法，并进行了相关的理论分析。该结果可用于指导NN参数的配置，以充分规避过拟合问题。作为一个关键的应用程序，我们认为符号检测任务的多输入多输出（MIMO）正交频分复用（OFDM）系统与大规模MIMO在基站（BS）。由于大规模MIMO-OFDM信号的张量结构，我们的在线学习为基础的符号检测方法推广以及比特出错率，即使使用有限的在线训练集。评估结果表明，基于多模RC的学习框架可以有效地克服无线系统的实际约束（即信道状态信息（CSI）错误和硬件非线性），以实现健壮和自适应的基于学习的空中通信。
英文摘要：
In this paper, we introduce a new neural network (NN) structure, multi-mode reservoir computing (Multi-Mode RC). It inherits the dynamic mechanism of RC and processes the forward path and loss optimization of the NN using tensor as the underlying data format. Multi-Mode RC exhibits less complexity compared with conventional RC structures (e.g. single-mode RC) with comparable generalization performance. Furthermore, we introduce an alternating least square-based learning algorithm for Multi-Mode RC as well as conduct the associated theoretical analysis. The result can be utilized to guide the configuration of NN parameters to sufficiently circumvent over-fitting issues. As a key application, we consider the symbol detection task in multiple-input-multiple-output (MIMO) orthogonal-frequency-division-multiplexing (OFDM) systems with massive MIMO employed at the base stations (BSs). Thanks to the tensor structure of massive MIMO-OFDM signals, our online learning-based symbol detection method generalizes well in terms of bit error rate even using a limited online training set. Evaluation results suggest that the Multi-Mode RC-based learning framework can efficiently and effectively combat practical constraints of wireless systems (i.e. channel state information (CSI) errors and hardware non-linearity) to enable robust and adaptive learning-based communications over the air.
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。
