广义无线光空间调制中的低复杂度OB-MMSE检测算法

Low-Complexity OB-MMSE Detection Algorithm for Generalized Wireless-Optical Spatial Modulation

针对传统光空间调制传输速率和激光器利用率较低,且要求激光器数目必须是2的整数次幂的问题,通过同时激活多个激光器并结合脉冲位置调制构建了一种适用于无线光通信的广义光空间调制(GOSM)方案。针对最大似然译码(ML)算法复杂度较高的问题,引入排序块最小均方误差(OB-MMSE)信号检测方法,利用平衡因子在综合考虑误码性能和复杂度的情况下,对OB-MMSE算法进行改进,并依据GOSM信号的特点对其权值进行修正;此外还推导了对数正态湍流信道中阈值的选取公式。最后,采用蒙特卡罗仿真验证了算法的性能,并将所提算法与经典ML、MMSE算法的误码性能和计算复杂度进行了对比。结果表明:与ML算法相比,所提算法在牺牲较小误码性能的情况下,能有效降低算法的复杂度;与MMSE算法相比,虽然所提算法的复杂度略有增大,但它可有效地降低误码率,而且还适用于接收机数目少于发射机数目的系统。

Traditional optical spatial modulation has low transmission rate and laser utilization and requires the number of lasers to be an integer power of two. To address these problems, we propose a generalized optical spatial modulation(GOSM) scheme suitable for wireless optical communication, which is combined with pulse position modulation and can simultaneously activate multiple lasers. Aiming at the high complexity of the maximum likehood decoding(ML) algorithm, we propose a signal detection algorithm based on ordered-block minimum mean square error(OB-MMSE). A balance factor is utilized to comprehensively consider the error performance and computational complexity. The proposed OB-MMSE algorithm is improved,and the weight is modified according to the characteristics of the GOSM signal. Moreover, an expression for the threshold selection in the lognormal turbulence channel is derived. Finally, the Monte Carlo simulation is utilized to verify the performance of the proposed solution, and the bit error rate and computational complexity of the proposed OB-MMSE algorithm are compared with those of classical ML and MMSE algorithms. Simulation results demonstrate that the proposed OB-MMSE algorithm can effectively reduce the computational complexity at the expense of a slight bit error rate reduction compared to ML algorithm. Furthermore, the proposed OB-MMSE algorithm can reduce the bit error rate compared to the MMSE algorithm. The proposed solution is applicable to systems with less receivers than transmitters at the cost of a slight increase in the computational complexity.