OFDM系统中基于压缩传感理论的信道估计算法

信道估计是OFDM系统中的一项关键技术，信道估计质量的好坏对整个系统的性能有重要的影响。传统的最小均方算法对稀疏信道进行估计时存在精确性差的缺陷。本文利用信道冲激响应的稀疏性，提出了一种基于近似l₀范数的信道估计算法。该算法用三种函数逼近l₀范数，应用梯度下降法和梯度投影算法获得代价函数的最优解，从而得到信道的最稀疏解。仿真实验结果表明:在相同条件下，与基于l₁范数的信道估计算法比较，本文算法的收敛速度快，估计值信噪比高，且均方误差小。 

Channel estimation algorithm based on compressed sensing for OFDM systems

Channel estimation for orthogonal frequency division multiplexing (OFDM) is a crucial technology.The quality of channel estimation has vital impact on the overall system performance.The traditional channel based on linear minimum mean-squared error suffers low accuracy.Exploiting the sparsity of channel impulse response,a channel estimation algorithm based on approximately?l₀ norm is proposed.We use three functions to approximate l₀ norm,and get the optimal solution of the cost function by applying gradient descend and gradient projection algorithm,thus obtains the sparsest channel response.Simulated experiment results show that under the same conditions,compared with the channel estimation algorithm based on l₁ norm,the proposed algorithm’s convergence speed is faster,and the signal-to-noise ratio is higher.