Planning Modeling and Optimization Algorithm for 5G Indoor Distribution System

doi:10.16182/j.issn1004731x.joss.23-0241

Abstract

Abstract:

Most of the new services in 5G mobile communication technologies, including smart homes, smart factories, and virtual reality, take place in indoor scenes. Therefore, how to quickly plan and build a 5G indoor distribution system with low construction cost and low power loss is of great significance for telecom operators. This paper establishes a mathematical planning model of a 5G indoor distribution system, which is closer to the actual scenario. The model aims to minimize the deployment cost and the maximum output signal power deviation between antennas, and the constraint is to meet the expected output signal power of each antenna, which is a constrained multi-objective optimization problem of mixed variables. Based on the Huffman coding idea, this paper proposes a coding strategy suitable for the structure of an indoor distribution system and uses the coding strategy to design an effective algorithm to solve the model under the framework of the MOEA/D-CM2M algorithm. The strategy can provide multiple planning schemes through a single operation. Computer simulation shows that the established model and the proposed algorithm are effective, and the cost is reduced by 8.90% and 20.09% compared with the original design of two real cases.

Key words: 5G, indoor distribution system, multi-objective optimization, antenna power, deployment cost

CLC Number:

TP391.9

Zeng Shaoda, Liu Hailin. Planning Modeling and Optimization Algorithm for 5G Indoor Distribution System[J]. Journal of System Simulation, 2024, 36(3): 659-672.

Figures/Tables 11

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器件类型	器件索引号	损耗/dB	价格/元
5 dB 耦合器	1.1	5/1.8	18.88
6 dB 耦合器	1.2	6/1.5	18.88
7 dB 耦合器	1.3	7/1.2	18.88
10 dB 耦合器	1.4	10/0.5	18.88
15 dB 耦合器	1.5	15/0.3	18.88
20 dB 耦合器	1.6	20/0.2	18.88
25 dB 耦合器	1.7	25/0.2	18.88
30 dB 耦合器	1.8	30/0.2	18.88
二功分器	2	3.5	20
三功分器	3	3.5	30
四功分器	4	3.5	40
六功分器	6	3.5	60
1/2 电缆/m	1	0.1	7.11
7/8 电缆/m	2	0.062 5	14.53

指标	AWC	ASC	RWC	RSC
mean cost	8 088.09	7 639.41	7 836.57	7 440.95
mean p.d.	12.29	8.34	14.33	9.07
mean con.	2.65	0.47	3.55	0.63
min cost	7 284.26	6 710.34	6 755.47	6 691.44
min p.d.	3.59	1.63	4.36	1.90
min con.	0	0	0	0
median cost	7 823.54	7 321.63	7 602.01	7 041.63
median p.d.	10.41	8.10	12.55	8.66
median con.	1.11	0	2.10	0
mean c.d.s.	1.34	10.92	0.30	4.22
median c.d.s.	0	8.00	0	2.50

指标	AWC	ASC	RWC	RSC
mean cost	7 098.15	7 182.63	9 235.29	8 057.39
mean p.d.	8.18	7.61	12.01	14.66
mean con.	0.92	0.51	2.07	3.67
min cost	6 470.30	5 915.88	6 912.85	5 410.64
min p.d.	2.93	2.29	3.60	2.23
min con.	0	0	0	0
median cost	6 876.39	6 834.73	8 894.91	7 456.02
median p.d.	7.20	7.00	11.60	12.38
median con.	0	0	1.50	2.14
mean c.d.s.	13.88	13.22	0	0.70
median c.d.s.	10.00	11.00	0	0

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