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山西大学 物理电子工程学院, 山西 太原 030006
王晓雯(1979-), 女, 副教授, 硕士, 主要从事网络通信方面的研究。E⁃mail: 381984245@qq.com。
收稿:2026-01-16,
网络首发:2026-07-31,
纸质出版:2026-08-31
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王晓雯, 任浩浩. OFDM中基于DFT的自适应信道估计算法[J]. 中北大学学报(自然科学版), 2026, 47(4): 569-576.
Wang Xiaowen, Ren Haohao. Adaptive DFT channel estimation algorithm based on joint delay and SNR estimation in OFDM system[J]. Journal of North University of China(Natural Science Edition), 2026, 47(4): 569-576.
王晓雯, 任浩浩. OFDM中基于DFT的自适应信道估计算法[J]. 中北大学学报(自然科学版), 2026, 47(4): 569-576. DOI: 10.62756/jnuc.issn.1673-3193.2026.01.0012.
Wang Xiaowen, Ren Haohao. Adaptive DFT channel estimation algorithm based on joint delay and SNR estimation in OFDM system[J]. Journal of North University of China(Natural Science Edition), 2026, 47(4): 569-576. DOI: 10.62756/jnuc.issn.1673-3193.2026.01.0012.
针对正交频分复用(Orthogonal Frequency Division Multiplexing, OFDM)系统中传统离散傅里叶变换(Discrete Fourier Transform, DFT)信道估计算法因固定使用循环前缀(Cyclic Prefix, CP)长度、 非整数倍时延信道下能量泄漏严重、 噪声抑制不充分等问题, 提出一种基于联合时延与信噪比(Signal-to-Noise Ratio, SNR)估计的自适应阈值DFT改进算法。该算法以最小二乘(Least Square, LS)初始估计为基础, 引入多重信号分类(Multiple Signal Classification, MUSIC)超分辨率技术实现信道多径时延的精准估计, 根据实际最大时延动态确定最优时域降噪窗口, 摆脱对固定CP长度的依赖。基于动态窗口重新划分纯净信号区间与噪声区间, 精确完成信号功率与噪声功率计算, 实现可靠SNR估计, 并将该SNR估计值作为核心依据构造自适应降噪阈值, 通过两级时域滤波完成噪声抑制与信号保护。最后经频域变换与窗函数补偿得到最终信道响应。仿真结果表明, 在瑞利多径衰落信道下, 与传统DFT算法、 固定阈值算法相比, 本文算法的误码率(Bit Error Rate, BER)与均方误差(Mean Square Error, MSE)显著降低, 在中高信噪比及非整数倍时延场景下性能提升尤为明显。该算法实现了时延估计、 窗口长度与降噪阈值的联合自适应优化, 有效提升了信道估计性能。但受MUSIC算法特性影响, 整体计算复杂度高于传统方法, 在时延敏感场景下存在一定限制, 后续将围绕复杂度优化开展进一步研究。
Aiming at the problems of traditional Discrete Fourier Transform channel estimation algorithms in orthogonal frequency division multiplexing systems, such as fixed use of cyclic prefix length, severe energy leakage under non-integer delay channels, and insufficient noise suppression, an improved adaptive threshold DFT algorithm based on joint delay and signal-to-noise ratio estimation was proposed. Based on the initial least square estimation, the algorithm introduced the multiple signal classification super-resolution technology to achieve accurate estimation of channel multipath delays, and dynamically determined the optimal time-domain denoising window according to the actual maximum delay, thus getting rid of the dependence on fixed CP length. Based on the dynamic window, the pure signal interval and noise interval were re-divided, the signal power and noise power were accurately calculated to realize reliable SNR estimation, and the estimated SNR value was used as the core basis to construct an adaptive denoising threshold, and noise suppression and signal protection were completed through two-stage time-domain filtering. Finally, the final channel response was obtained through frequency-domain transformation and window function compensation. Simulation results show that under Rayleigh multipath fading channels, compared with the traditional DFT algorithm and fixed threshold algorithm, the mean square error and bit error rate are significantly reduced, and the performance improvement is particularly obvious in medium and high SNR and non-integer delay scenarios. The algorithm realizes the joint adaptive optimization of delay estimation, window length and denoising threshold, which effectively improves the channel estimation performance. However, affected by the characteristics of the MUSIC algorithm, the overall computational complexity is higher than that of traditional methods, which has certain limitations in delay-sensitive scenarios, and further research will focus on complexity optimization in the future.
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