基于粒子群优化算法的5G波束权值寻优方法

doi:10.11959/j.issn.1000-0801.2023146

Abstract

Abstract:

In order to solve the problems of insufficient indoor 5G signal coverage and difficulty in optimization of high-rise buildings, the 5G beamforming weight optimization method based on particle swarm optimization (PSO) algorithm was proposed.Decoupling and reducing the network dimension into multiple subnet slices by collecting the data reported by the terminal, and the single cell weight database was planned and simulated to create the multi-cells weights group and fitness function.Finally, the PSO algorithm was adopted to obtain the local optimal weight group of the multi-cells.The test results show that the reference signal receiving power (RSRP) is improved by 8.7%, the signal to interference plus noise ratio (SINR) is improved by 17.5%, the downlink throughput is promoted by 27.3% together.The proposed method has the advantages of low cost, high efficiency and intelligence, and can significantly improve the user perception in high-rise buildings.

Key words: high-rise building, network decoupling, particle swarm optimization algorithm, weight optimization

CLC Number:

TN929.5

Jianbin WANG, Hengjun WANG, Song WU, Zhenkai WANG. 5G beamforming weight optimization method based on particle swarm optimization algorithm[J]. Telecommunications Science, 2023, 39(7): 23-34.

Figures/Tables 14

参数	描述
m	粒子群个数，1.5倍权值库数
c₁	粒子个体学习因子（加速因子），通常为2
c₂	(0,1)的随机数
r₁	粒子的社会学习因子（加速因子），通常为2
r₂	(0,1)的随机数
U	速度的惯性权重，一般取0.9（取0.9～1.2比较合适）
l₁	RSRP的学习权重，l₁取值范围为0.1～0.9，等梯度0.1遍历（l₁+ l₂=1）
l₂	SINR的学习权重，l₂取值范围为0.1～0.9，等梯度0.1遍历（l₁+l₂=1）
$V_{p}^{d}$	第d次迭代时粒子p的移动速度
${[{Wg}_{j}]}_{p}^{d}$	第d次迭代时粒子p所在的权值组位置
$f ({[{Wg}_{j}]}_{p}^{d})$	第d次迭代时粒子p的适应度函数值
${[{Wg}_{pbest}]}_{p}^{d}$	第d次迭代时粒子p经过的最好权值组位置
${[{Wg}_{pbest}]}^{d}$	第d次迭代时所有粒子经过的最好权值组位置

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样本点	CellSrv	RSRP_Srv/dB	服务小区/邻区DOA_{[HDOA, VDOA]}	Cell_Nbr	RSRP_Nbr/dB
1	Srv_A	-84.3	(-10,8)、(-3,4)、(20.13)	Nbr_B,C	-87.6、-89.4
2	Srv_A	-86.4	(9,-24)、(14,-6)、(4,-2)	Nbr_C,D	-88.7、-91.4
3	Srv_B	-91.1	(6,-23)、(3,-13)、(5,-9)	Nbr_D,E	-95.8、-96.5
…	…	…	…	…	…
T	Srv_T	-104.8	(33,12)、(53,6)、(21,3)	Nbr_V,S	-108.1、-109.3

对比项	方位角	下倾角	水平波宽	垂直波宽
子波束0/1/2/3	[-50o,50o]	[-9o,15o]	[10o, 30o, 45o]	6o
子波束4/5/6	[-50o,50o]	[-9o,15o]	30o	6o
调整步长	5o	1o	10o、30o、45o	6o
权值库数S	64 260	210	3	1

SSB权值库	方位角、下倾角、水平波宽、垂直波宽
W_Init	(-30,-20,-10,0,10,20,30)、(3,3,3,3,3,3,3)、(16,10,10,10,10,10,16)、(6,6,6,6,6,6,6)
W₁	(0,10,20,30,0,0,0)、(-3,3,9,15)、(10,10,10,10,30,30,30)、(6,6,6,6,6,6,6)
W₂	(0,10,20,35,0,0,0)、(-3,3,9,15)、(10,10,10,10,30,30,30)、(6,6,6,6,6,6,6)
W₃	(0,10,20,40,0,0,0)、(-3,3,9,15)、(10,10,10,10,30,30,30)、(6,6,6,6,6,6,6)
…	…
W_S	(-50,-5,5,50,50,50,50)、(-9,-3,3,9)、(45,45,45,45,30,30,30)、(6,6,6,6,6,6,6)

	RSRP权值增益/dB
	水平-60o	水平-59o	水平-58o	…	水平58o	水平59o	水平60o
垂直-30o	-13.35	-11.23	-8.82	…	-2.51	-2.73	-2.95
垂直-29o	-13.29	-10.17	-14.87	…	-2.52	-2.74	-2.95
垂直-28o	-8.24	-4.29	-13.19	…	-2.79	-3.01	-3.23
垂直-27o	-6.95	-5.04	-3.47	…	-3.35	-3.57	-3.79
…	…	…	…	…	…	…	…
垂直27o	-62.83	-56.04	-55.59	…	-55.51	-55.73	-55.94
垂直28o	-41.13	-39.78	-45.41	…	-37.99	-38.21	-38.43
垂直29o	-42.33	-40.08	-40.82	…	-34.14	-34.36	-34.58
垂直30o	-43.02	-42.16	-43.85	…	-34.52	-34.74	-34.96

SSB权值库	CSI波束0	CSI波束1	CSI波束2	CSI波束3
SSB权值库	(方位角,下倾角,水平波宽,垂直波宽)	(方位角,下倾角,水平波宽,垂直波宽)	(方位角,下倾角,水平波宽,垂直波宽)	(方位角,下倾角,水平波宽,垂直波宽)
W_Init	(-25,0,50,6)	(25,0,50,6)	(-25,6,50,6)	(25,6,50,6)
W₁	(0,-9,50,6)	(0,-5,50,6)	(0,-1,50,6)	(10,6,50,6)
W₂	(0,-9,50,6)	(0,-5,50,6)	(0,-1,50,6)	(15,6,50,6)
W₃	(0,-9,50,6)	(0,-5,50,6)	(0,-1,50,6)	(20,6,50,6)
…	…	…	…	…
W_S	(-30,-5,50,6)	(-10,-2,50,6)	(10,0,50,6)	(30,3,50,6)

5G beamforming weight optimization method based on particle swarm optimization algorithm

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Figures/Tables 14

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方案	样本点数/个	平均RSRP/dBm	平均SINR/dB	下行感知速率/(Mbit·s^-1)	下行MCS
对照组	118 630	-98.1	10.3	237.1	12.6
寻优后	146 720	-89.6	12.1	301.8	15.9
增益	28 090	8.5	1.8	64.7	3.3

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