Robust Two-stage MIMO-OFDM Channel Estimation Method Against Sensing Errors

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

Abstract

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

CLC Number:

TN928

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

Figures/Tables 18

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Table 1

Complexity comparison

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	$Ο (Λ P d θ N r)$ + $Ο (N r D 2 + D 3 + T (N r D P + D 2 P))$

Table 1

Table 2

Simulation parameter setting

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 $σ 2$

Table 2

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