MIMO-OFDM系统的SAGE-IPSO联合估计检测
发布时间：2018-11-03 14:53
【摘要】：针对多输入多输出-正交频分复用系统中最大似然检测算法难以硬件实现以及传统的信道估计性能较差等缺陷,提出了一种联合估计检测算法.该算法使用离散傅里叶变换-最小二乘(DFT-LS)算法进行信道初估计,利用广义空间迭代期望最大化(SAGE)算法对估计的信道信息进行校正,并结合改进的粒子群优化(IPSO)算法完成对信号的迭代检测,使系统性能得到改善.仿真分析结果表明,算法能以较少的迭代次数估计出信道状态信息和检测数据;在相同误比特率的情况下,性能优于经典检测算法,与理想状态下的最大似然检测算法仅相差1 dB左右.
[Abstract]:A joint estimation detection algorithm is proposed to overcome the difficulties of hardware implementation and the poor performance of traditional channel estimation in multi-input multiple-output orthogonal frequency division multiplexing systems. The algorithm uses discrete Fourier transform-least squares (DFT-LS) algorithm for channel initial estimation, and uses generalized space iterative expectation maximization (SAGE) algorithm to correct the estimated channel information. The improved particle swarm optimization (IPSO) algorithm is used to detect the signal iteratively, and the system performance is improved. Simulation results show that the algorithm can estimate channel state information and detection data with less iteration times. In the case of the same bit error rate, the performance is better than the classical detection algorithm, which is only 1 dB different from the maximum likelihood detection algorithm in ideal state.
【作者单位】： 哈尔滨工程大学信息与通信工程学院;
【基金】：国家自然科学基金项目(F010201) 中央高校基本科研业务专项基金项目(HEUCF130802)
【分类号】：TN919.3
，
本文编号：2308118
[Abstract]:A joint estimation detection algorithm is proposed to overcome the difficulties of hardware implementation and the poor performance of traditional channel estimation in multi-input multiple-output orthogonal frequency division multiplexing systems. The algorithm uses discrete Fourier transform-least squares (DFT-LS) algorithm for channel initial estimation, and uses generalized space iterative expectation maximization (SAGE) algorithm to correct the estimated channel information. The improved particle swarm optimization (IPSO) algorithm is used to detect the signal iteratively, and the system performance is improved. Simulation results show that the algorithm can estimate channel state information and detection data with less iteration times. In the case of the same bit error rate, the performance is better than the classical detection algorithm, which is only 1 dB different from the maximum likelihood detection algorithm in ideal state.
【作者单位】： 哈尔滨工程大学信息与通信工程学院;
【基金】：国家自然科学基金项目(F010201) 中央高校基本科研业务专项基金项目(HEUCF130802)
【分类号】：TN919.3
，
本文编号：2308118
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