一种面向RDSM系统的基于遗传算法优化色散矩阵的方法

doi:10.11959/j.issn.1000-0801.2022291

Abstract

Abstract:

Rectangular differential spatial modulation (RDSM) is a multi-antenna incoherent modulation technology with high spectral efficiency, low power consumption, and zero-overhead for channel estimation.RDSM is especially suitable for 6G communication systems, such as fast-moving Internet of vehicles, Internet of things, cellular networks, etc.However, the construction of the sparse rectangular unitary space-time dispersion matrix (DM) at transmitter is a problem.The proposed Genetic algorithm (GA) will result in less computational complexity than the currently used random research.The fitness of GA was calculated by the rank and determinant criterion (RDC) method to avoid discussions in differential system.Due to characteristics of constellation symbols of RDSM, the proposed method reduced the computational complexity during each single iteration in GA.The simulation results show that the optimized DMS can significantly improve the bit error rate (BER) performance of the RDSM system.Compared with random search, the low-complexity GA effectively improves the DMS optimization efficiency of RDSM.The computational complexity required for optimizing DMS is about 0.1% of random search optimization method.

Key words: differential spatial modulation, index modulation, genetic algorithm, dispersion matrix, constellation rotation

CLC Number:

TN929.5

Peng ZHANG, Xiaoping JIN, Dongxiao CHEN. A genetic algorithm based method of optimizing dispersion matrix for RDSM system[J]. Telecommunications Science, 2022, 38(12): 46-55.

Figures/Tables 6

References 16

[1]	卞李娜, 冯会真, 金小萍 ,等. 广义正交码索引调制系统的性能分析[J]. 电信科学, 2022,38(8): 120-128.
	BIAN L N , FENG H Z , JIN X P ,et al. Performance analysis of generalized orthogonal code index modulation system[J]. Telecommunications Science, 2022,38(8): 120-128.
[2]	MESLEH R Y , HAAS H , SINANOVIC S ,et al. Spatial modulation[J]. IEEE Transactions on Vehicular Technology, 2008,57(4): 2228-2241.
[3]	LI Q , WEN M W , DI RENZO M . Single-RF MIMO:from spatial modulation to metasurface-based modulation[J]. IEEE Wireless Communications, 2021,28(4): 88-95.
[4]	VASAVADA Y , JOHN B B . Low complexity optimal and suboptimal detection at spatial modulation MIMO receivers[J]. IEEE Transactions on Vehicular Technology, 2022,71(4): 3837-3850.
[5]	BIAN Y , CHENG X , WEN M ,et al. Differential spatial modulation[J]. IEEE Transactions on Vehicular Technology, 2014,64(7): 3262-3268.
[6]	ISHIKAWA N , SUGIURA S . Rectangular differential spatial modulation for open-loop noncoherent massive-MIMO downlink[J]. IEEE Transactions on Wireless Communications, 2017,16(3): 1908-1920.
[7]	LIU Y , HUANG W R , OUYANG Y G . Amplitude phase shift keying aided rectangular differential spatial modulation[C]// Proceedings of 2019 International Applied Computational Electromagnetics Society Symposium - China (ACES). Piscataway:IEEE Press, 2019: 1-2.
[8]	WU C W , XIAO Y , XIAO L X ,et al. Space-time block coded rectangular differential spatial modulation:system design and performance analysis[J]. IEEE Transactions on Communications, 2019,67(9): 6586-6597.
[9]	金小萍, 刘家瑜, 蒋晨 ,等. 基于改进遗传算法的 STSK 系统色散矩阵和 3D 星座的联合优化[J]. 电信科学, 2021,37(9): 86-94.
	JIN X P , LIU J Y , JIANG C ,et al. Joint optimization of dispersion matrix and 3D constellation for STSK system based on improved genetic algorithm[J]. Telecommunications Science, 2021,37(9): 86-94.
[10]	SUGIURA S . Dispersion matrix optimization for space-time shift keying[J]. IEEE Communications Letters, 2011,15(11): 1152-1155.
[11]	HANZO L , ALAMRI O , EL-HAJJAR M ,et al. Near-capacity multi-functional MIMO systems:sphere-packing,iterative detection and cooperation[M]. Hoboken: John Wiley ＆ Sons, 2009.
[12]	RAJASHEKAR R , ISHIKAWA N , SUGIURA S ,et al. Full-diversity dispersion matrices from algebraic field extensions for differential spatial modulation[J]. IEEE Transactions on Vehicular Technology, 2016,66(1): 385-394.
[13]	HOLLAND J H . Adaptation in natural and artificial systems:an introductory analysis with applications to biology,control,and artificial intelligence[M]. Cambridge: MIT press, 1992.
[14]	ASHKZARI A , AZIZI A . Introducing genetic algorithm as an intelligent optimization technique[J]. Applied Mechanics and Materials, 2014,568/569/570: 793-797.
[15]	XIAO L X , DA CHEN , HEMADEH I A ,et al. Graph theory assisted bit-to-index-combination gray coding for generalized index modulation[J]. IEEE Transactions on Wireless Communications, 2020,19(12): 8232-8245.
[16]	JOSE D , SAMEER S M . Differential transmission schemes for generalized spatial modulation[J]. IEEE Transactions on Vehicular Technology, 2021,70(12): 12640-12650.

Metrics

Recommended 0

No Suggested Reading articles found!

A genetic algorithm based method of optimizing dispersion matrix for RDSM system

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 16

Related Articles 15

Metrics

Recommended 0

[1]	Lu LIU, Dan YANG, Ruijie CHEN, Jia LI, Xi ZHOU. Research on POI quality prediction based on KPCA-GA-BP neural network [J]. Telecommunications Science, 2023, 39(1): 108-116.
[2]	Lina BIAN, Huizhen FENG, Xiaoping JIN, Weizhong LOU, Chongxiao QU. Performance analysis of generalized orthogonal code index modulation system [J]. Telecommunications Science, 2022, 38(8): 120-128.
[3]	Kun MA, Lingyu XU, Xiaoping SHEN, Zhicheng GONG, Jianping LAN, Shuangxi CHEN, Jun QIAN. Virtual machine scheduling model based on Shapley value modified genetic algorithm in cloud computing [J]. Telecommunications Science, 2022, 38(12): 1-10.
[4]	Xiaoping JIN, Jiayu LIU, Chen JIANG, Qiang GUO. Joint optimization of dispersion matrix and 3D constellation for STSK system based on improved genetic algorithm [J]. Telecommunications Science, 2021, 37(9): 86-94.
[5]	Xiaoping JIN, Qing WU, Wenchao ZHAI, Jianrong BAO. Non-orthogonal multiple access wireless energy harvesting cooperative system [J]. Telecommunications Science, 2021, 37(4): 46-53.
[6]	Xiaohui YAO,Qing LI,Kunkun SUN. A domain adaptive hybrid genetic algorithm and its verification in security service function chain orchestration [J]. Telecommunications Science, 2020, 36(5): 16-24.
[7]	Wenyu HU,Wenju WANG. Empirical research on the diffusion effect of ICT innovation diffusion and prediction in China [J]. Telecommunications Science, 2020, 36(1): 66-76.
[8]	Yaguan QIAN,Xiaohui GUAN,Shuhui WU,Bensheng YUN,Dongxiao REN. An approach of Bagging ensemble based on feature set and application for traffic classification [J]. Telecommunications Science, 2018, 34(4): 41-48.
[9]	Kai ZHOU. Nodes deployment strategy for underwater wireless sensors networks based on grids [J]. Telecommunications Science, 2018, 34(11): 48-58.
[10]	Chang CAI,Yafang WANG,Bingmei MIAO,Hui JIANG. Dynamic spectrum allocation for cognitive radio sensor networks based on improved genetic algorithm [J]. Telecommunications Science, 2017, 33(8): 85-93.
[11]	Heng ZHOU,Zhixian CHANG,Wujun YANG,Juan GUO. An orchestration algorithm of 5G network slicing [J]. Telecommunications Science, 2017, 33(8): 130-137.
[12]	Guoling ZHANG. An emotional neural network based approach for wind power prediction [J]. Telecommunications Science, 2017, 33(3): 168-172.
[13]	Xiaoan BAO,Xue WEI,Lei CHEN,Guoheng HU,Na ZHANG. Load balancing method of service cluster based on mean-variance [J]. Telecommunications Science, 2017, 33(1): 1-8.
[14]	Qing SHEN,Yunliang JIANG,Zhangguo SHEN,Jungang LOU. Test case minimizing based on combination chaos genetic algorithm [J]. Telecommunications Science, 2016, 32(6): 93-102.
[15]	Panxing LI,Jing WANG. PCI planning method based on genetic algorithm in LTE network [J]. Telecommunications Science, 2016, 32(3): 147-152.