认知异构蜂窝网络中改进蜉蝣算法的资源分配策略

doi:10.11959/j.issn.1000-436x.2022115

Abstract

Abstract:

Aiming at the optimization of uplink resource allocation in cognitive heterogeneous cellular networks, a resource allocation algorithm based on improved discrete mayfly algorithm was proposed.In the cognitive heterogeneous cellular network model, the power control strategy was introduced to control the interference suppression of transmitted power, and the improved discrete mayfly algorithm was used to optimize and solve the optimal distribution scheme based on the user’s quality of service (QoS) requirements and interference threshold constraints to maximize the energy efficiency (EE).In order to improve the convergence rate and search ability of the mayfly algorithm, the dynamic adaptive weights of incomplete Gamma and Beta distribution functions and the golden sine position updating strategy were introduced.The simulation results show that the closed-loop power control based on SINR can dynamically adjust the transmitting power of users and effectively restrain the interference between users.The GSWBMA has good optimization efficiency and convergence performance to solve the resource allocation problem, effectively improve the energy efficiency of the system and the transmission rate of users, and ensure the QoS requirements of users.

Key words: cognitive heterogeneous cellular network, resource allocation, mayfly algorithm, power control, quality of service

CLC Number:

TN929

Damin ZHANG, Yi WANG, Chengcheng ZOU, Peiwen ZHAO, Linna ZHANG. Resource allocation strategies for improved mayfly algorithm in cognitive heterogeneous cellular network[J]. Journal on Communications, 2022, 43(6): 156-167.

Figures/Tables 14

函数名	表达式	最优解
Bent Cigar	$f_{1} (X) = x_{1}^{2} + 10^{6} \sum_{i = 2}^{D} x_{i}^{2}$	0
High Conditioned Elliptic	$f_{2} (X) = {\sum_{i = 1}^{D} (10^{6})}^{\frac{i - 1}{D - 1}} x_{i}^{2}$	0
Rosebrock	$f_{3} (X) = \sum_{i = 1}^{D - 1} (100 (x_{i}^{2} - x_{i + 1})^{2} + (x_{i} - 1)^{2})$	0
Discus	$f_{4} (X) = 10^{6} x_{1}^{2} + \sum_{i = 2}^{D} x_{1}^{2}$	0
Schaffer F7	$f_{5} (X) = {[\frac{1}{D - 1} \sum_{i = 1}^{D - 1} (\sqrt{x_{i}} (\sin (50 s_{i}^{0.2}) + 1))]}^{2}$	0

函数	算法	最优值	平均值	标准差
	GSWBMA	0	0	0
f₁	MA	1.099×10^-16	1.271×10^-6	4.244×10^-6
	WMA	7.106×10^-172	8.850×10^-136	4.438×10^-135
	GSMA	0	0	0
	GSWBMA	0	0	0
f₂	MA	1.683×10^-13	5.372	2.941×10
	WMA	1.827×10^-153	6.008×10^-133	3.261×10^-132
	GSMA	0	0	0
	GSWBMA	3.715	$5 . 834$	3.741
f₃	MA	1.077×10	3.752×10	4.141×10
	WMA	6.668	6.718	$3 . 888 \times 10^{- 1}$
	GSMA	$3 . 596$	6.752	6.353
	GSWBMA	0	0	0
f₄	MA	4.034×10^-21	1.384×10^-12	7.567×10^-12
	WMA	1.862×10^-155	2.919×10^-128	1.596×10^-127
	GSMA	0	0	0
	GSWBMA	1.493×10^-21	$5 . 962 \times 1 0^{- 18}$	$1 . 023 \times 1 0^{- 17}$
f₅	MA	0	4.761×10^-10	1.606×10^-11
	WMA	2.095×10^-8	1.047×10^-5	2.431×10^-5
	GSMA	2.482×10^-14	3.061×10^-12	5.076×10^-10

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算法	参数
GSWBMA	同WMA、GSMA、BMA一致
WMA	λ=μ=0.1,b ₁=1,b₂=2
GSMA	a=π,b=-π,h=0.683
BMA	a₁=1.2,a₂=a₃=1.6,β=2,d=5,fl=1
BWOA	a_max=2,a_min=0
BGA	交叉概率 p_c=0.65,变异概率 p_m=0.01
BPSO	a ₁=1.5,a₂=1.5

参数	数值
小区半径/km	10
系统带宽/MHz	10
信道数量/个	35
认知蜂窝用户数/个	10
宏蜂窝用户数/个	15
MBS最大发射功率/dBm	46
CFBS最大发射功率/dBm	24
宏蜂窝用户干扰半径/m	600
认知蜂窝用户干扰半径/m	200
噪声功率/(dBm·Hz^-1)	-174
额定功率P₁/dBm	-78
最小传输速率需求R_min/(bit·s^-1)	10⁶
干扰阈值/dBm	-110

算法	公平性	网络最大效益
GSWBMA	56.335 3	5 541.85
BMA	50.638 6	5 320.12
BWOA	52.233 3	4 915.77
BPSO	47.309 7	4 492.25
BGA	48.958 5	4 344.55

Resource allocation strategies for improved mayfly algorithm in cognitive heterogeneous cellular network

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