双向中继网络中基于压缩感知的稀疏信道估计

为提高双向中继网络中稀疏信道估计的精度并减少训练序列的长度, 利用双向中继信道(Two-way Relay Channel, TWRC)的潜在稀疏特性, 研究了基于压缩感知的稀疏TWRC估计问题, 提出了一种改进的正交匹配追踪(Improved Orthogonal Matching Pursuit, IOMP)算法. 新算法运用迭代重加权最小二乘估计代替了正交匹配追踪(Orthogonal Matching Pursuit, OMP)算法中的最小二乘估计过程, 通过对样本进行迭代重加权, 逐步减小了异常样本的影响, 不断地修正了估计值, 在使用相同长度的训练序列时, 提高了估计的精度. 与传统的最小二乘估计算法相比, 新算法能够在获得相同估计效果的情况下, 显著减少所需训练序列的长度. 仿真结果验证了基于新算法的稀疏TWRC估计的有效性.

Sparse Channel Estimation for Two-way Relay Networks Based on Compressive Sensing

In order to improve the precision of sparse channel estimation and reduce the length of the training sequences required by the sparse channel estimation in two-way relay networks, the compressive sensing based sparse two-way channel (TWRC) estimation is studied by exploiting the potential sparsity of the TWRC. An improved orthogonal matching pursuit (IOMP) algorithm is proposed in this paper. The new algorithm uses an iteratively reweighted least squares method to replace the least squares estimation process of the orthogonal matching pursuit (OMP) algorithm. By applying the iteratively update weight to the samples, the new algorithm can weaken the effect of abnormal samples gradually, thus revising the estimation values during each iteration and improving the precision of the estimation when using the same length of training sequences. Furthermore, the IOMP based sparse TWRC estimation algorithm can significantly reduce the length of the training sequences under the same estimation precision when comparing to the conventional least squares estimation algorithm. Simulation results demonstrate the effectiveness of the proposed TWRC estimation algorithm.