用于正交频分复用系统中幅度失真估计与补偿的DCT神经元
The DCT Neuron for Estimation and Compensation of Amplitude Distortions in OFDM Systems
AI总结：
本文提出了一种接收端框架，利用DCT神经元高效估计频率选择性OFDM信道的非线性响应，实现低复杂度的实时补偿。
摘要：
我们提出了一种接收端框架，用于识别频率选择性OFDM信道中的幅度失真。核心创新是使用基于离散余弦变换（DCT）的DCT神经元，作为一种紧凑的自适应处理器，利用其特性实现高效估计。该方法直接在时域操作，构建准确的信号模型并自适应跟踪信道变化，仅需两个OFDM符号即可实现可靠识别。所学的非线性响应可用于预失真和迭代解码，从而在多载波系统中实现低复杂度、实时的复杂响应补偿。
英文摘要：
We present a receiver-side framework for identifying amplitude distortions in frequency-selective OFDM channels. The core novelty is the use of the DCT Neuron, a compact adaptive processor based on the discrete cosine transform (DCT), to characterize the channel's nonlinear response, leveraging its properties for highly efficient estimation. Operating directly in the time domain, the method builds an accurate signal model and tracks channel variations adaptively, achieving reliable identification with as few as two OFDM symbols. The learned nonlinear response can then be exploited for predistortion and iterative decoding, enabling low-complexity, real-time adaptive compensation of complex responses in multicarrier systems.
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