摘要
传统基于范数天线选择的可见光广义空间调制-多输入多输出(OGSM-MIMO)系统不能最大化系统信道容量,且发射端等功率分配未均衡信道质量,使系统信道容量和可靠性受到制约。针对信道范数和相似系数对信道容量的影响,推导了范数联合相似系数对信道容量影响的数学模型,设计了范数联合相似系数的天线组合算法来最大化系统信道容量,利用粒子群优化算法设计了基于信道容量的适应度函数,对选定的天线进行最优功率配置,提升系统传输质量,并采用分段式界理论与蒙特卡罗方法对系统性能进行理论推导与实验验证。结果表明:当误码率为10^(-3),采用BPSK调制方式,发射天线数量为6时,范数联合相似系数的天线选择算法的误码性能相较于随机选择算法、RSS选择算法和范数选择算法分别提升了7.4 dB、5.8 dB和3.2 dB,且采用粒子群优化功率分配算法后的OGSM-MIMO系统所需信噪比改善了4.5 dB。
Objective Optical spatial modulation is a novel multiple input multiple output(MIMO)technology that activates a single transmitting antenna at each moment to avoid co-channel interference between channels.However,it has low spectrum utilization and significant limitations.Optical generalized spatial modulation(OGSM)extends this approach by enabling multiple antennas to transmit data simultaneously,improving antenna utilization and overall data transmission rate.In OGSM systems,bit error performance can be enhanced by refining detection algorithms.However,these improvements have often been limited.Therefore,researchers have turned to antenna selection and power allocation algorithms to optimize bit error performance more effectively.Methods In this paper,we propose a norm joint similarity coefficient-based antenna selection algorithm and a particle swarm optimization-based power allocation algorithm.To maximize channel capacity,we derive a mathematical model that reflects the influence of channel norm and similarity coefficient on capacity.The norm joint similarity coefficient is used to create an antenna selection strategy that optimally combines antennas for improved performance.A channel capacity-based fitness function is designed using the particle swarm optimization algorithm to allocate optimal power to selected antennas,thus enhancing system transmission quality.Results and Discussions In the simulated environment,we apply BPSK modulation with two active antennas.The following key results emerge from the analysis:1)at low signal-to-noise ratios(SNRs),theoretical BER of the OGSMMIMO system is initially higher than simulated bit error rate(BER);however,as SNR increases,this gap narrows,aligning closely at higher SNRs.2)As the number of receiving antennas increases,bit error performance improves notably.For instance,when BER reaches 10^(-3),the four-antenna setup outperforms the three-antenna setup by 4.1 dB(Fig.2).The proposed norm joint similarity coefficient antenna selection algorithm significantly enhances bit error performance compared to random,RSS,and norm-based selection algorithms.When BER reaches 10^(-4)with four transmitting antennas,bit error performance improves by 7.9 dB,5.2 dB,and 2.2 dB,respectively.With six antennas,improvements are 7.4 dB,5.8 dB,and 3.2 dB,respectively(Fig.3).In addition,particle swarm optimization-based power allocation algorithm considerably enhances bit error performance over traditional equal-power and water-filling methods,improving by 7.5 dB and 4.2 dB,respectively,at BER of 10^(-3)(Fig.4).In the antenna selection algorithm system utilizing the norm joint similarity coefficient,when BER reaches 10^(-4),the bit error performance of the OGSM_(6×4-4)system is enhanced by 4.5 dB following particle swarm optimization-based power allocation,while the OGSM_(4×4-2)system achieves a 2.6 dB improvement under the same optimization.Compared to the OGSM_(4×4-2)system,the OGSM_(6×4-4)system exhibits a 3 bit/s increase in each control unit,though its bit error performance slightly declines(Fig.5).Conclusions In this paper,we examine the norm joint similarity coefficient antenna selection and particle swarm optimization power allocation algorithms for the visible OGSM-MIMO system,providing a simulation-based analysis of system bit error performance.The findings indicate that the norm joint similarity coefficient algorithm plays a critical role in the system’s operation,enabling intelligent antenna activation that mitigates co-channel interference and improves system capacity and stability.The simulation results confirm that,across different SNR conditions,BER is significantly enhanced with this algorithm over traditional methods.In addition,the particle swarm optimization-based power allocation strategy optimizes transmission power,allowing the system to adapt to varying communication environments and channel conditions,thus improving transmission efficiency and performance.Overall,the system employing the particle swarm optimization algorithm achieves a lower BER across diverse channel conditions compared to conventional methods.
作者
赵黎
范琳
陈俊霖
Zhao Li;Fan Lin;Chen Junlin(School of Electronic Information Engineering,Xi’an Technological University,Xi’an 710021,Shaanxi,China)
出处
《光学学报》
北大核心
2025年第2期96-104,共9页
Acta Optica Sinica
基金
国家自然科学基金(12004292)
陕西省科技计划项目-重点研发计划一般项目(2024GX-YBXM-105)
西安市科技局高校院所科技人员服务企业项目(24GXFW0026)
自主无人系统智能化测试与协同控制创新团队。
关键词
光广义空间调制
范数联合相似系数
粒子群优化
误码率
optical generalized spatial modulation
norm joint similarity coefficient
particle swarm optimization
bit error rate
