极化码的K-best译码算法

基于K-best算法和球形译码算法,提出了一种占用极少内存的K-best译码算法,该算法解决了短码情况下SC译码算法性能不佳和SCL译码算法硬件实现占用内存过多的问题,改进了SD时间复杂度不固定和译码性能过于依赖初始半径选择的问题,可以通过调整K大小在纠错性能和复杂性之间提供良好的折衷,具有固定时间复杂度,并且不涉及半径。仿真结果表明,目标算法与SC算法和SCL算法的译码性能几乎相同,但仅占SCL算法48%的内存。

Novel K-best detection algorithms for MIMO system

Aiming at the optimum path excluding characteristics and the full constellation searching characteristics of the K-best detection algorithm, an improved-performance K-best detection algorithm and several reduced-complexity K-best detection algorithms are proposed. The improved-performance K-best detection algorithm deploys minimum mean square error （MMSE） filtering of a channel matrix before QR decomposition. This algorithm can decrease the probability of excluding the optimum path and achieve better performance. The reducedcomplexity K-best detection algorithms utilize a sphere decoding method to reduce searching constellation points. Simulation results show that the improved performance K-best detection algorithm obtains a 1 dB performance gain compared to the K- best detection algorithm based on sorted QR decomposition （SQRD）. Performance loss occurs when K = 4 in reduced complexity K-best detection algorithms. When K = 8, the reduced complexity K-best detection algorithms require less computational effort compared with traditional K-best detection algorithms and achieve the same performance.

一种新型基于格基约减的MIMO检测算法

MIMO技术是LTE的关键技术,MIMO信号检测算法的选用需要在复杂度和检测性能之间取得合理的折中。将格基理论应用于MIMO检测中,能够大幅降低由于天线数增多所带来的检测复杂度过高问题,典型的格约减算法有LLL算法和Seysen算法。以上述格基约减算法为基础,引进排序串行干扰抵消和广度优先球译码K-best算法的思想,提出了一种新型的基于格基约减的MIMO检测算法———KLR_OSIC。与原有基于格基约减的检测算法相比,该算法能够在较低的计算复杂度下显著提高MIMO的检测性能。通过仿真可看出,新算法比原基于格基约减的算法性能更佳,且更接近ML算法。