隐含训练序列信道估计中的功率分配

Power Allocation of Superimposed Training

与传统时分发送训练序列的信道估计算法相比,隐含训练序列信道估计算法将训练序列与信息序列直接相加后通过天线发送,从而节约了信道带宽。然而,在天线发送总功率一定时,训练序列的功率越大,信息序列的功率便越小,从而导致信道均衡器的信噪比减小。本文研究了基于MIMO系统的隐含训练序列信道估计算法,分析了信道均衡器信噪比与训练序列功率的关系,并根据均衡器信噪比最大原则推导出训练序列与信息序列的最佳功率分配。分析和仿真结果表明:在训练序列的最佳功率点上,信道均衡器的信噪比最高;随着接收天线信噪比的增加,训练序列的最佳功率增大。

Compared with the conventional Time-Division Multiplexed （TDM） channel estimation scheme based on training sequences, the use of implicit training saves valuable bandwidth, where the training sequences are added to information sequences before antenna transmission. However, for a fixed total transmission power, the information power decrease with the increase of the training sequence power, which causes decrease in the Signal to Noise Ratio （SNR） at channel equalizer. In this paper, the relationship between the SNR of the channel equalizer and the training sequences power is analyzed for MIMO system. The optimal power allocation of the training sequence is derived based on the criterion of maximizing SNR of the equalizer. Analysis and simulation results show that the SNR of the channel equalizer is maximized at the optimal training sequence power, and the optimal power of the training sequences is increased with increase of the signal to noise ratio at the received antennas.