预编码增益优化的预编码叠加训练设计

Precoding superimposed training design with precoding gain optimization

为获取更优的预编码增益,针对满秩预编码叠加训练模型,通过推导成对错误概率近似表达式,分析该模型中预编码矩阵带来的分集和编码增益,导出相应的优化策略,并据此提出基于线性调频序列的满秩预编码叠加训练方案.该方案采用线性调频序列构建预编码矩阵与训练序列,实现数据与训练的正交传输.与现有方案相比,该方案在相同信道估计性能下可获取更优的编码增益性能.仿真结果表明,该方案在高信噪比环境下可有效提高符号检测性能.采用线性调频序列比多相序列构建预编码矩阵与训练序列能获取更优的编码增益.

To gain a higher precoding gain of the precoder for the full rank precoding superimposed training model, the diversity and coding gain of the precoder are derived bypair wise error probability analysis, with optimization strategies provided. Furthermore, chirp sequence based full rank precoding superimposed training is proposed. The proposed scheme adopts chirp sequences to construct the precoder and training sequences, so as to realize orthogonal transmission for data and training sequences and achieve a greater coding gain than the existing scheme with the same performance of channel estimation. Simulation results show that the proposed scheme remarkably improves the performance of symbol detection in high signal noise ratio environment. The precoding superimposed training model constructed by chirp sequences achieves a greater coding gain than that constructed by polyphase sequences.