大规模MIMO室外无线光通信系统中基于分段高斯近似的最大似然盲检测算法

在室外可见光通信场景下,现有的盲检测算法在近似信道模型时,往往未能与真实信道模型的概率密度函数在截尾处充分拟合,导致在寻找最佳判决门限时存在误差,从而影响系统的平均误符号率性能。因此,针对大规模MIMO(Multiple-Input Multiple-Output)室外无线光通信系统,提出了一种基于分段高斯近似的最大似然盲检测算法。该算法在强大气湍流情况下,得到各个子信道叠加后的等效信道模型服从伽马分布,依据等效信道概率密度函数的唯一极值点确定左右两个分段区间,得到各个子信道在两个分段区间的一阶和二阶统计信息,然后利用中心极限定理和大数定理得到等效信道在两个分段区间都近似服从高斯分布,弥补了等效信道模型与真实信道模型的概率密度函数在截尾处拟合较差的缺点,获得了精确的最佳判决门限,从而改善了系统的平均误符号率性能。为了验证该算法的优越性,通过MATLAB仿真实验将其与现有的盲检测算法进行平均误符号率性能对比。实验数据表明,在收发天线数为4和小信噪比的情况下,所提算法的平均误符号率性能相比现有盲检测算法性能提高近10倍。同时,在接收天线数为8时,所提算法的平均误符号率性能与现有盲检测算法在接收天线数为16时的性能接近,接收天线数是原来的50%。实验数据充分说明,相比于现有的盲检测算法,所提算法在仅利用信道的数学模型和统计信息的情况下,随着收发天线数的增加能够明显提高系统的平均误符号率性能。

Maximum Likelihood Blind Detection Algorithm Based on Piecewise Gaussian Approximation

For outdoor visible light communication scenarios,existing blind detection algorithms often fail to fit well with the probability density function of the real channel model at the truncation when approximating the channel model,resulting in errors in finding the optimal decision threshold,thus affecting the system’s average symbol error rate performance.Therefore,aiming at the large-scale MIMO outdoor wireless optical communication system,a maximum likelihood blind detection algorithm based on piecewise Gaussian approximation was proposed.In the case of powerful gas turbulence,the algorithm obtains the equivalent channel model superposed by each sub-channel and obeys the gamma distribution.According to the unique extreme point of the equivalent channel probability density function,the left and right segmentation intervals are determined,and the first and second order statistical information of each sub-channel in two segmentation intervals is obtained.Then,by using the central limit theorem and the large number theorem,the equivalent channel is approximated to a Gaussian distribution in both segmentation intervals.The algorithm compensates for the poor fitting of the probability density function of the equivalent channel model and the real channel model at the truncation,and obtains the optimal decision threshold,thus improving the average symbol error rate performance of the system.In order to verify the superiority of the algorithm,the MATLAB simulation experiment was used to compare the average symbol error rate performance between the proposed algorithm and the existing blind detection algorithm.The experimental results show that the average symbol error rate performance of the proposed algorithm is nearly 10 times higher than that of the existing blind detection algorithm when the number of transmitting and receiving antennas is 4 and the signal to noise ratio is small.At the same time,when the number of receiving antennas is 8,the average symbol error rate performance of the proposed algorithm is close to that of the existing blind detection algorithm when the number of receiving antennas is 16,which 50%the number of receiving antennas.The experimental datas fully demonstrate that compared with the existing blind detection algorithm,the proposed algorithm can significantly improve the average symbol error rate performance of the system with the increase of the number of transmitting and receiving antennas when only the mathematical model and statistical information of the channel are utilized.