人工智能在5G系统中的应用综述

doi:10.11959/j.issn.1000-0801.2021109

Abstract

Abstract:

With the continuous development of 5G, the era of the internet of everything is coming.Problems such as massive device connections, massive application requests, ultra-high network load and complex dynamic network environment pose great challenges to the optimization of 5G systems in the context of the internet of everything.Facing these challenges, artificial intelligence (AI) shows its unique advantages.Firstly, the advantages of deep learning driven AI algorithms in 5G system compared with conventional algorithms were briefly introduced.Then, the application of AI algorithms in multi-access edge computing (MEC) and mmWave massive multiple-input multiple-output (MIMO) system were described in detail, with advantages and disadvantages of each method being compared and analyzed.Finally, according to the existing research, the shortcomings of AI algorithms in 5G application scenarios were summarized and the future research directions were forecasted.

Key words: 5G, artificial intelligence, reinforcement learning, edge computing, mmWave massive MIMO

CLC Number:

TN929

Jianwu ZHANG, Luxin WANG, Lingfen SUN, Qianye ZHANG, Hangguan SHAN. An survey on application of artificial intelligence in 5G system[J]. Telecommunications Science, 2021, 37(5): 14-31.

Figures/Tables 10

	压缩率（CR）	CsiNet^[67]	CsiNet+^[68]	SM-CsiNet+^[68]	PM-CsiNet+^[68]	LSTM-CsiNet^[68]
室内	4	-17.36	-27.37	$- 27 . 90$	-27.60	—
	8	-12.70	-18.29	$- 18 . 49$	-17.70	—
	16	-8.64	-14.14	-13.45	-12.25	$- 23 . 06$
	32	-6.24	-10.43	-9.89	-8.24	$- 22 . 33$
	64	-5.84	—	—	—	$- 21 . 24$
室外	4	-8.75	$- 12 . 40$	-11.91	-12.02	—
	8	-7.61	$- 8 . 72$	-8.25	-8.10	—
	16	-4.51	-5.73	-5.31	-5.07	$- 9 . 86$
	32	-2.81	-3.4	-3.22	-3.00	$- 9 . 18$
	64	-1.93	—	—	—	$- 8 . 83$

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人工智能算法	优化目标	卸载方式	参考文献
MADDPG	时延、信道利用率、信道成功接入率	全卸载	参考文献[14]
DNN	兼顾时延与能耗	全卸载	参考文献[15]
深度强化学习	兼顾时延与能耗	部分卸载	参考文献[18]
DNN、SNN	时延、任务错误率	部分卸载	参考文献[16-17]
improved DNN	计算速率	全卸载	参考文献[20]
DDPG	兼顾时延与能耗	全卸载	参考文献[19]
CNN	时延	部分卸载	参考文献[21]
DQN	兼顾时延与能耗	全卸载	参考文献[22]
AHP-DQN	兼顾时延与能耗	全卸载	参考文献[23]

网络	4	8	16	32	64
CsiNet^[67]	2 103 904	1 055 072	530 656	268 448	137 344
CsiNet+^[68]	2 122 340	1 073 508	549 092	286 884	—
LSTM-CsiNet^[69]	10 247 148	—	7 484 688	7 024 272	6 794 064
LSTM-Attention^[70]	2 924 912	—	582 704	281 936	141 152
AnciNet^[75]	2 289 334	—	716 086	453 878	322 774

压缩率	CNR（dB）	CsiNet^[67]	AnciNet^[75]
16	0	-2.76	-8.70
	5	-6.24	-11.64
	10	-8.71	-13.31
	15	-9.56	-14.42
	20	-10.03	-14.84
	25	-10.19	-14.99
32	0	-1.94	-7.47
	5	-4.94	-9.26
	10	-7.23	-9.88
	15	-7.83	-10.42
	20	-8.09	-10.55
	25	-8.17	-10.60
64	0	-0.75	-6.49
	5	-3.45	-7.65
	10	-4.80	-7.92
	15	-5.28	-8.30
	20	-5.45	-8.37
	25	-5.52	-8.40

An survey on application of artificial intelligence in 5G system

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