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Joint semiblind channel estimation and data detection for MIMO-OFDM systems

机译:MIMO-OFDM系统的联合半盲信道估计和数据检测

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In MIMO systems, where multiple antennas are used at both transmitter and receiver to achieve high spectral efficiency, channel impulse responses are often assumed to be constant over a block or packet. This assumption of block stationarity on channels is valid for most fixed wireless scenarios. However, for communications in a high mobility environment, the assumption will result in considerable performance degradation. In this paper, we focus on channel estimation for a MIMO system with OFDM transmission technique. In our system, pilots are placed on N/sub P/ subcarriers for a novel channel estimation at the receiver with Kalman filters. With the channels estimated by a Kalman filter, we apply the QRD-M algorithm for suboptimal data detection with reasonable computational cost. The outputs of data detection are fed back to another Kalman filter for an improved channel estimation. By alternatively and iteratively using these two Kalman filters, a better channel estimation can be obtained. A simulation result of the bit error rate shows the improvement of 10 dB in SNR with four iterations over noniteration.
机译:在MIMO系统中,在发射器和接收器上都使用多个天线来实现高频谱效率,通常假设信道脉冲响应在一个块或一个数据包中是恒定的。信道上的块平稳性的这种假设对于大多数固定无线场景都是有效的。但是,对于高移动性环境中的通信,此假设将导致相当大的性能下降。在本文中,我们专注于采用OFDM传输技术的MIMO系统的信道估计。在我们的系统中,将导频放置在N / sub P /个子载波上,以便在带有Kalman滤波器的接收器处进行新颖的信道估计。利用卡尔曼滤波器估计的信道,我们将QRD-M算法用于次优数据检测,并具有合理的计算成本。数据检测的输出反馈到另一个卡尔曼滤波器,以改善信道估计。通过交替和迭代地使用这两个卡尔曼滤波器,可以获得更好的信道估计。误码率的仿真结果显示,与非迭代相比,经过四次迭代,SNR改善了10 dB。

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