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

通信学报 ›› 2022, Vol. 43 ›› Issue (6): 156-167.doi: 10.11959/j.issn.1000-436x.2022115

认知异构蜂窝网络中改进蜉蝣算法的资源分配策略

张达敏¹, 王义¹, 邹诚诚¹, 赵沛雯¹, 张琳娜²

¹ 贵州大学大数据与信息工程学院，贵州贵阳 550025
² 贵州大学机械工程学院，贵州贵阳 550025

修回日期:2022-05-09 出版日期:2022-06-01 发布日期:2022-06-01
作者简介:张达敏（1967- ），男，贵州贵阳人，博士，贵州大学教授，主要研究方向为计算机软件、认知无线电、优化计算等
王义（1997- ），男，苗族，贵州遵义人，贵州大学硕士生，主要研究方向为认知无线电与异构蜂窝网络融合、优化计算、路由选择等
邹诚诚（1998- ），女，布依族，贵州兴义人，贵州大学硕士生，主要研究方向为异构网络、优化计算、资源分配
赵沛雯（1997- ），女，贵州贵阳人，贵州大学硕士生，主要研究方向为认知无线电、异构无线网络、智能优化算法等
张琳娜（1997- ），女，贵州贵阳人，贵州大学讲师，主要研究方向为产品缺陷检测、深度学习等
基金资助:
国家自然科学基金资助项目(62062021);国家自然科学基金资助项目(61872034);贵州省科学技术基金资助项目([2020]1Y254);贵州省自然科学基金资助项目([2019]1064)

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

Damin ZHANG¹, Yi WANG¹, Chengcheng ZOU¹, Peiwen ZHAO¹, Linna ZHANG²

¹ School of Big Data ＆Information Engineering, Guizhou University, Guiyang 550025, China
² School of Mechanical Engineering, Guizhou University, Guiyang 550025, China

Revised:2022-05-09 Online:2022-06-01 Published:2022-06-01
Supported by:
The National Natural Science Foundation of China(62062021);The National Natural Science Foundation of China(61872034);The Science and Technology Foundation of Guizhou Province([2020]1Y254);The Natural Science Foundation of Guizhou Province([2019]1064)

摘要/Abstract

摘要：

针对认知异构蜂窝网络中上行链路资源分配的优化问题，提出认知异构蜂窝网络中改进离散蜉蝣算法的资源分配算法。认知异构蜂窝网络模型中，考虑用户层间干扰和带外干扰引入功率控制策略控制发射功率来干扰抑制，基于用户服务质量（QoS）需求和干扰阈值约束，最大化能量效率为优化目标，利用改进离散蜉蝣算法优化求解得出最优分配方案。引入不完全Gamma和Beta分布函数的动态自适应权重、黄金正弦位置更新策略，提升蜉蝣算法的收敛速度和搜索能力。仿真实验表明，基于接收SINR的闭环功率控制动态调整用户端的发射功率，能有效抑制用户间的干扰，GSWBMA 求解资源分配问题具有良好的寻优效率和收敛性能，有效提升系统能量效率和用户传输速率，保证用户QoS需求。

关键词: 认知异构蜂窝网络, 资源分配, 蜉蝣算法, 功率控制, 服务质量

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

中图分类号:

TN929

张达敏, 王义, 邹诚诚, 赵沛雯, 张琳娜. 认知异构蜂窝网络中改进蜉蝣算法的资源分配策略[J]. 通信学报, 2022, 43(6): 156-167.

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.

图/表 14

图1

图2

表1

表2

6个CEC测试函数基本信息"

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

表2

表3

CEC测试函数的有效性对比"

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

表3

图3

表4

图4

图5

表5

图6

图7

图8

图9

参考文献 27

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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

认知异构蜂窝网络中改进蜉蝣算法的资源分配策略

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

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