基于压缩感知的协同OFDM稀疏信道估计方法

Sparse channel estimation method based on compressed sensing for OFDM cooperation system

针对频率选择性衰落信道下的放大转发协同正交频率复用(OFDM)通信系统,提出一种基于压缩感知理论的稀疏信道估计方法。首先,构造协同OFDM系统模型,利用循环矩阵理论,将该系统模型变换成类似于传统的点对点系统模型,该模型由一个协同卷积信道矢量和等效的观测矩阵组成;然后,通过压缩感知理论证明,该等效矩阵以很高的概率满足严格等距特性(RIP);最后,利用压缩感知算法重构卷积信道脉冲响应。与传统的线性信道估计方法相比较,所提方法能够利用较少的训练序列达到稳健的信道估计,有效地提高频谱资源利用率,且具备计算复杂度低的特点。仿真结果验证了该方法的有效性。

A compressed channel sensing method was proposed for Orthogonal Frequency Division Muhiplexing （OFDM） based Amplify-and-Forward （AF） cooperative communication network over frequency-selective fading channels. First, by using cyclic matrix theory, the system model was established similar to the traditional point-to-point system model, which consisted of a cascaded channel vector and a measurement matrix. And then, using the theory of compressed sensing, the measurement matrix was proven to satisfy Restricted Isometry Property （RIP） with high probability. Finally, convolution channel impulse response was reconstructed with compressed sensing algorithm. According to the figures example, the cooperative channel exhibited an inherent sparse or sparse clustering structure. Hence, the proposed method can fully exploit the inherent sparse structure in cooperative channel. The simulation results confirm that the proposed method provides significant improvement in Mean Square Error （MSE） performance or spectral efficiency compared with the traditional linear channel estimation methods.