AI 中文总结
本文提出基于DCT的最大似然信道估计框架，用于OFDM系统非线性信道估计，结合低复杂度紧凑参数化，在有限训练开销下实现接近理想的BER性能，适用于多种非线性补偿方法。
AI 中文摘要
本文提出一种用于估计正交频分复用（OFDM）通信系统中非线性频率选择性信道的最大似然（ML）框架。非线性失真采用基于离散余弦变换（DCT）的表示建模，这会产生适定的估计问题，具有良好的收敛特性。所提方法结合了紧凑参数化与低计算复杂度，支持快速自适应和高效实时实现。数值结果表明，该信道估计可用于多种非线性补偿方法，在训练开销非常有限的情况下可实现接近理想的误码率（BER）性能；当存在幅度与相位非线性时，该性能可维持至信噪比（SNR）约15 dB，仅存在幅度失真时可维持至-10 dB。
英文摘要
This paper proposes a maximum-likelihood (ML) framework for estimating nonlinear frequency-selective channels in orthogonal frequency-division multiplexing (OFDM) communication systems. The nonlinear distortions are modeled using a Discrete Cosine Transform (DCT)-based representation, which results in a well-conditioned estimation problem with favorable convergence properties. The proposed method combines a compact parameterization with low computational complexity, enabling fast adaptation and efficient real-time implementation. Numerical results show that the proposed channel estimation can be used for multiple nonlinear compensation methods and achieve near-ideal BER performance with very limited training overhead. This performance is maintained down to approximately 15 dB SNR in the presence of both amplitude and phase nonlinearities, and down to -10 dB when only amplitude distortions are present.
CommentsPaper submitted for publication in IEEE Transactions on Signal Processing
