MIMO-OFDM系統(tǒng)中基于RAMP及其改進的稀疏信道估計算法
發(fā)布時間:2019-01-19 16:39
【摘要】:研究了在MIMO-OFDM系統(tǒng)中的稀疏信道估計問題.將正則化稀疏度自適應匹配追蹤算法(RAMP)運用到MIMO-OFDM稀疏信道估計中,并對該算法的迭代結(jié)束條件加以改進,取殘差的能量之差小于設(shè)定的閾值來終止迭代過程,更加準確地估計出信道稀疏度,進而提高了稀疏信道的估計精度.仿真結(jié)果表明,在MIMO-OFDM系統(tǒng)中,相比RAMP算法與稀疏度自適應匹配追蹤算法(SAMP),改進算法能夠獲得更好的MSE性能,在不需要稀疏度的前提下達到了與正交匹配追蹤算法(OMP)算法相似的MSE性能.
[Abstract]:The problem of sparse channel estimation in MIMO-OFDM systems is studied. The regularized sparsity adaptive matching tracking algorithm (RAMP) is applied to the MIMO-OFDM sparse channel estimation, and the iterative end condition of the algorithm is improved. The difference of the residual energy is less than the set threshold to terminate the iterative process. The channel sparsity is estimated more accurately, and the estimation accuracy of sparse channel is improved. The simulation results show that compared with the RAMP algorithm and the sparse adaptive matching tracking algorithm (SAMP), the improved MSE algorithm can achieve better MSE performance in MIMO-OFDM system. The performance of MSE is similar to that of the orthogonal matching tracking algorithm (OMP) without the need of sparsity.
【作者單位】: 北京理工大學信息與電子學院;
【分類號】:TN919.3
[Abstract]:The problem of sparse channel estimation in MIMO-OFDM systems is studied. The regularized sparsity adaptive matching tracking algorithm (RAMP) is applied to the MIMO-OFDM sparse channel estimation, and the iterative end condition of the algorithm is improved. The difference of the residual energy is less than the set threshold to terminate the iterative process. The channel sparsity is estimated more accurately, and the estimation accuracy of sparse channel is improved. The simulation results show that compared with the RAMP algorithm and the sparse adaptive matching tracking algorithm (SAMP), the improved MSE algorithm can achieve better MSE performance in MIMO-OFDM system. The performance of MSE is similar to that of the orthogonal matching tracking algorithm (OMP) without the need of sparsity.
【作者單位】: 北京理工大學信息與電子學院;
【分類號】:TN919.3
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