MIMO-NOMA系统中基于深度学习CCDNN框架的传输速率提高和能量效率优化算法

doi:10.13229/j.cnki.jdxbgxb.20240719

Abstract

Abstract:

Multiple-input-multiple-output non-orthogonal multiple access（MIMO-NOMA） technique involves the NOMA idea into MIMO systems， which can effectively improve the system energy efficiency and throughput. Nevertheless， complex spatial structures and rapidly varying spatial channels of the MIMO-NOMA system decrease the system's throughput and block its application. To address this issue， this paper proposes a deep-learning-based framework for MIMO-NOMA systems that employs an efficient Convolutional Deep Neural Network（CCDNN） tailored to MIMO-NOMA communications. The method comprises multiple convolutional and hidden layers and is able to solve the power-allocation problem through a dedicated algorithm， thereby improving both the transmission rate and energy efficiency of the MIMO-NOMA system. Numerical simulations are conducted for validation， and the results demonstrate that the proposed CCDNN framework outperforms conventional approaches， offering a viable solution for enhancing MIMO-NOMA power-allocation performance and laying a theoretical foundation for future research in this area.

Key words: MIMO-NOMA system, deep learning, energy efficiency, power allocation

CLC Number:

TG115.28

Jin-peng WANG,Shun-yao DOU,Jue WANG,Xin ZHAO. Optimization algorithm for transmission rate improvement and energy efficiency in MIMO-NOMA system based on a deep learning CCDNN framework[J].Journal of Jilin University(Engineering and Technology Edition), 2026, 56(3): 830-842.

Figures/Tables 13

Fig.1

Fig.2

Table 1

MIMO-NOMA system parameter settings"

发射机	空间信息	the angles> $- π ~ + π$
	射频（RF）链路的功耗	$P R F = 300 ? m W$
	误码率阈值	$σ = 10 - 7$
	基带功耗	$P B B = 200 ? m W$
	基站BS 覆盖面积	a 500 m circle area
	移相器的功耗	$P S = 5 ? m W$
	像素距离Pixel Distance	d=λ/2
	信噪比SNR	10 dB
	莱斯Rician信道因子	k=8 dB
信道衰落类型	高斯白噪声AWGN信道
	瑞利Rayleigh信道
	莱斯Rician信道
	纳科拉米Nakagami信道（m=3）

Table 1

Table 2

CCDNN network framework parameters selection"

训练序列长度	L=16 bits， L=8 bits
序列批处理尺寸	1 000 batch sizes， 500 batch sizes
学习速率	0.1、0.02、0.01、0.001
每簇用户数	K=4
用户总数	$M = 128$
初始信噪比 SNR	25 dB
缩放参数 $ζ$ 和 $ρ$	学习训练动态调整

Table 2

Table 3

Description of variables or parameters in experiment"

	变量或参数名称	含义
仿真图3	L=16 bits，L=8 bits	序列训练长度
仿真图3	Sum data rate/ （bit·Hz^-1·s^-1）	系统速率总和/ （bit·Hz^-1·s^-1）
仿真图4	Learning rate	网络学习速率分别为0.1、0.01、0.02及0.001
仿真图4	SNR/dB	信噪比/dB
仿真图5	Data rate per user/ （bit·Hz^-1·s^-1）	每个用户的速率/ （bit·Hz^-1·s^-1）
	Retraining time duration T/s	再训练时间/s
	k=12 dB， $?$ =3.8	分解因子，超参数
仿真图6	Spectrum efficiency	系统频谱效率
仿真图7	Energy efficiency/ （bps·Hz^-1·W^-1）	系统能效/ （bps·Hz^-1·W^-1）
仿真图7	Data Rate/bps	系统速率/bps
仿真数据表4~6	L²	二维范数正则化项
	DNN	深度学习网络
	MAC/Million	乘法累加计数/百万次

Table 3

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Table 4

Table 5

Table 6

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卷积层数	算法	测试精度/%
8	DNN	96.004
8	DNN+L²正则运算	97.179 6
9	DNN	97.460
9	DNN+L²正则运算	98.271
10	DNN	98.332
10	DNN+L²正则运算	98.331
11	DNN	98.334
11	DNN+L²正则运算	98.335

隐藏层数	算法	测试精度/%
8	DNN	95.596
8	DNN+L²正则运算	96.223
9	DNN	97.511
9	DNN+L²正则运算	97.730
10	DNN	98.194
10	DNN+L²正则运算	98.205
11	DNN	98.331
11	DNN+L²正则运算	98.332

算法	MAC性能/百万	运算时间/s
OMA^［24］	0.54	43 921
NOMA^［25］	0.52	43 921
CRS-NOMA^［26］	0.09	9 894
本文算法	0.50	36 997

Optimization algorithm for transmission rate improvement and energy efficiency in MIMO-NOMA system based on a deep learning CCDNN framework

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