面向感知误差的鲁棒两阶段MIMO-OFDM信道估计方法

doi:10.16182/j.issn1004731x.joss.25-0405

摘要/Abstract

摘要：

针对ISAC (integrated sensing and communication)辅助MIMO-OFDM系统中雷达感知信息存在误差时传统信道估计方法性能恶化、导频开销大、计算复杂度高等问题，提出一种面向感知误差的鲁棒两阶段稀疏信道估计框架。第一阶段设计残差能量加权的同步正交匹配追踪算法REW-SOMP(residual energy weighted simultaneous orthogonal matching pursuit)，通过局部字典自适应扩展与残差加权路径筛选机制，在感知误差下准确提取通信关联路径；第二阶段提出自适应惩罚因子交替方向乘子算法AP-ADMM (adaptive penalty factor alternating direction method of multipliers)，动态平衡原始残差与对偶残差以优化信道增益估计，有效解决固定惩罚因子导致的收敛速度与精度矛盾问题。仿真结果表明：在3GPP标准信道下，导频密度仅为6.25%时，所提方法相较于传统宽带算法不仅可获得更好的NMSE性能，且在感知误差大时仍保持鲁棒性，同时显著降低了计算复杂度。

关键词: 信道估计, 通感一体化, 多输入多输出, 正交频分复用, 感知误差, 同步正交匹配追踪算法, 交替方向乘子算法

Abstract:

To address the challenges of performance degradation, high pilot overhead, and high computational complexity in traditional channel estimation methods for integrated sensing and communication (ISAC) assisted MIMO-OFDM systems when radar sensing information contains errors, this paper proposes a robust two-stage sparse channel estimation framework designed to be tolerant of sensing errors. In the first stage, a residual energy weighted simultaneous orthogonal matching pursuit (REW-SOMP) algorithm is designed. Leveraging locally adaptive dictionary expansion and a residual-weighted path selection mechanism, it accurately captures communication-associated paths even under sensing errors. The second stage introduces an adaptive penalty factor alternating direction method of multipliers (AP-ADMM) algorithm. It dynamically balances the primal and dual residuals to refine channel gain estimation, effectively resolving the trade-off between convergence speed and estimation accuracy inherent to fixed penalty factors. Simulation results under 3GPP standard channels demonstrate that with only 6.25% pilot density, the proposed method achieves superior NMSE performance compared to conventional broadband algorithms, maintains robustness under large sensing errors, and significantly reduces computational complexity.

Key words: channel estimation, integrated sensing and communication, MIMO, OFDM, sensing errors, simultaneous orthogonal matching pursuit algorithm, alternating direction method of multipliers

中图分类号:

TN928

彭艺,王俊,杨青青等 . 面向感知误差的鲁棒两阶段MIMO-OFDM信道估计方法[J]. 系统仿真学报, 2026, 38(5): 1205-1223.

Peng Yi,Wang Jun,Yang Qingqing,et al . Robust Two-stage MIMO-OFDM Channel Estimation Method Against Sensing Errors[J]. Journal of System Simulation, 2026, 38(5): 1205-1223.

图/表 18

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图2

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表1

复杂度对比

算法名称	复杂度
WB+ADMM	$Ο (d 3 + T m a x d N f f t)$
WB+OMP	$Ο (N f f t M i t e r N r d)$
WB+REW-SOMP	$Ο (Λ N f f t (N r d + N r Λ 2))$
Propose Method	$Ο (Λ P d θ N r)$ + $Ο (N r D 2 + D 3 + T (N r D P + D 2 P))$

表1

表2

仿真参数设置

仿真参数	取值
子载波间隔 $Δ f$ /kHz	120
中心频率 $f c$ /GHz	28
采样率 $f s$ /MHz	30.72
子载波数 $K$	256
基带带宽 $B$ /MHz	30.72
采样周期 $T s$ /ns	32.552
FFT长度 $N f f t$	256
循环前缀长度 $N c p$	34
延迟抽头 $N c$	34
发射天线数量 $N t$	1
接收天线数量 $N r$	32
蒙特卡罗迭代次数 $N$	500
角度信息误差的标准差 $σ θ$	3
时延信息误差的标准差 $σ τ$	$T s / 2$
角度信息字典大小 $d θ$	100
时延信息字典误差 $d τ$	20
ADMM算法最大迭代次数 $T m a x$	2 000
惩罚因子 $ρ$	100
收敛容差 $t o l$	1×10^–6
残差比阈值 $μ$	10
幅度调整因子 $ω$	4
正则化稀疏系数 $λ$	0.1 $σ 2$

表2

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