LTE-A下行信道估计算法研究

LTE-A downlink channel estimation algorithm

LTE-A(long term evolution advanced)通信系统采用正交频分复用(OFDM)为其物理层核心技术以获得抗频率选择性衰落和高频谱利用率等优点。接收端需要对多径信道动态估计和跟踪以满足相干解调,准确而简单的信道估计算法可以提高数据传输速率和降低用户设备的功耗。对LTE-A中基于导频的下行信道估计算法进行研究,提出了一种使用神经网络的信道估计算法。该算法考虑了下行传输链路的信道频率选择性,利用接收到的导频信号动态跟踪信道的变化特征,然后根据得到的信道信息对接收数据进行相干解调。仿真结果表明,与最小二乘(LS)、最小均方误差(MMSE)等传统方法相比较,该信道估计算法在均方误差(MSE)、误符号率(SER)和计算复杂度方面具有更好的性能,并且能保证LTE-A系统吞吐量需求。

LTE-A （long term evolution advanced）communication system uses orthogonal frequency division multiplexing （OFDM） as the core technology for the physical layer to achieve robustness against frequency selective fading and high spectral efficiency.Receiver requires dynamic multipath channel estimation and tracking for coherent demodulation,so an accurate and simple channel estimation algorithm can improve the data transmit rate and reduce the power consumption of user equipment.This paper describes the LTE-A downlink channel estimation by using pilot and proposes a channel estimator using neural network.This algorithm considers the downlink transmissions with channel frequency selectively and uses the received pilot signal to dynamically track the channel characteristic changes,then preforms coherent demodulation for the received data according to the obtained channel information.Simulation results show that compared to the conventional methods least square （LS） and MMSE,the proposed method has better performance in terms of mean square error （MSE）,symbol error rate （SER） and complexity,and can effectively improve the system throughput.