Joint optimization of user association and resource allocation in cognitive radio ultra-dense networks to improve genetic algorithm

doi:10.11772/j.issn.1001-9081.2021101777

Abstract

Abstract:

Aiming at the multi-dimensional resource allocation problem in the downlink heterogeneous cognitive radio Ultra-Dense Network （UDN）， an improved genetic algorithm was proposed to jointly optimize user association and resource allocation with the objective of maximizing the throughput of femtocell users. Firstly， preprocessing was performed before the algorithm running to initialize the user’s reachable base stations and available channels matrix. Secondly， symbol coding was used to encode the matching relationships between the user and the base stations as well as the user and the channels into a two-dimensional chromosome. Thirdly， dynamic choosing best for replication + roulette was used as the selection algorithm to speed up the convergence of the population. Finally， in order to avoid the algorithm from falling into the local optimum， the mutation operator of premature judgment was added in the mutation stage， so that the connection strategy of base station， user and channel was obtained with limited number of iterations. Experimental results show that when the numbers of base stations and channels are fixed， the proposed algorithm improves the total user throughput by 7.2% and improves the cognitive user throughput by 1.2% compared with the genetic algorithm of three-dimensional matching， and the computational complexity of the proposed algorithm is lower. The proposed algorithm reduces the search space of feasible solutions， and can effectively improve the total throughput of cognitive radio UDNs with lower complexity.

Key words: Ultra-Dense Network (UDN), Cognitive Radio (CR), heterogeneous network, genetic algorithm, joint optimization, user association, resource allocation

摘要：

针对下行的异构认知超密集异构网络（UDN）的多维资源配置问题，提出一种以毫微微小区用户最大吞吐量为目标的联合优化用户关联和资源分配的改进遗传算法。首先，在算法开始之前进行预处理，初始化用户可达基站和可用信道矩阵；其次，采用符号编码，将用户与基站以及用户与信道的匹配关系编码为一个二维的染色体；然后，将动态择优复制+轮盘赌作为选择算法，以加快种群的收敛；最后，为避免算法陷入局部最优，在变异阶段加入早熟判决的变异算子，从而在有限次迭代下求得基站、用户、信道的连接策略。实验结果表明，在基站与信道数量一定时，所提算法与三维匹配的遗传算法相比在用户总吞吐量方面提高了7.2%，在认知用户吞吐量方面提高了1.2%，且计算复杂度更低。所提算法缩小了可行解的搜索空间，能在较低复杂度下有效提高认知UDN的总吞吐量。

关键词: 超密集网络, 认知无线电, 异构网络, 遗传算法, 联合优化, 用户关联, 资源分配

CLC Number:

TN929.5

Junjie ZHANG, Runhe QIU. Joint optimization of user association and resource allocation in cognitive radio ultra-dense networks to improve genetic algorithm[J]. Journal of Computer Applications, 2022, 42(12): 3856-3862.

张俊杰, 仇润鹤. 认知超密集网络用户关联与资源分配联合优化遗传算法[J]. 《计算机应用》唯一官方网站, 2022, 42(12): 3856-3862.

Figures/Tables 12

Fig. 1 Heterogeneous cognitive radio ultra-dense network model

Fig. 2 Preprocessing

Fig. 3 Symbol coding

Fig. 4 Calculation of throughput in the same channel

Fig. 5 Mutation operators for avoiding premature

Fig. 6 Joint optimization algorithm framework

Tab. 1 Simulation parameters

参数	值
MBS覆盖半径Rm/m	100
FBS覆盖半径R/m	20
FBS最大发射功率 $P m a x M$ /dBm	35
FBS最大发射功率 $P m a x F$ /dBm	10
择优比例常数 $ρ$	0.8
用户总数N	［20∶20∶80］
热点用户占比 $a$	［0.1∶0.1∶0.9］
FBS总数K	［10∶4∶30］
信道个数C	［10∶2∶20］
染色体条数L_num	300
迭代上限frame	200
交叉概率Pc	0.6
载波间隔W/kHz	180
高斯白噪声 $N 0$ / $(d B m ⋅ H z - 1)$	-174
用户间距离阈值/m	1
FBS间距离阈值/m	15
FBS-MBS距离阈值/m	40

Tab. 1 Simulation parameters

参数	值
MBS覆盖半径Rm/m	100
FBS覆盖半径R/m	20
FBS最大发射功率 $P m a x M$ /dBm	35
FBS最大发射功率 $P m a x F$ /dBm	10
择优比例常数 $ρ$	0.8
用户总数N	［20∶20∶80］
热点用户占比 $a$	［0.1∶0.1∶0.9］
FBS总数K	［10∶4∶30］
信道个数C	［10∶2∶20］
染色体条数L_num	300
迭代上限frame	200
交叉概率Pc	0.6
载波间隔W/kHz	180
高斯白噪声 $N 0$ / $(d B m ⋅ H z - 1)$	-174
用户间距离阈值/m	1
FBS间距离阈值/m	15
FBS-MBS距离阈值/m	40

Fig. 7 Number of iterations varying with user number

Fig. 8 Total throughput varying with probability of mutation

Fig.9 Total throughput varying with the proportion of users in hotspots

Fig.10 FU throughput varying with the number of channels

Fig. 11 FU throughput varying with FBS distribution density

References 20

1	TENG Y L， LIU M T， YU F R， et al. Resource allocation for ultra-dense networks： a survey， some research issues and challenges［J］. IEEE Communications Surveys and Tutorials， 2019， 21（3）： 2134-2168. 10.1109/comst.2018.2867268
2	ARJOUNE Y， KAABOUCH N. A comprehensive survey on spectrum sensing in cognitive radio networks： recent advances， new challenges， and future research directions［J］. Sensors， 2019， 19（1）： No.126. 10.3390/s19010126
3	IVANOV A， TONCHEV K， POULKOV V， et al. Framework for implementation of cognitive radio based ultra-dense networks［C］// Proceedings of the 42nd International Conference on Telecommunications and Signal Processing. Piscataway： IEEE， 2019： 481-486. 10.1109/tsp.2019.8769067
4	El RHARRAS A， SABER M， CHEHRI A， et al. Optimization of spectrum utilization parameters in cognitive radio using genetic algorithm［J］. Procedia Computer Science， 2020， 176： 2466-2475. 10.1016/j.procs.2020.09.328
5	SINGH S P， KUMAR A， MISHRA S， et al. A novel analytical model for interference estimation in CR-enabled femtocells［J］. International Journal of Communication Systems， 2020， 33（4）： No.e4221. 10.1002/dac.4221
6	SHI L， YOO S J. Distributed fair resource allocation for cognitive femtocell networks［J］. Wireless Personal Communications， 2017， 93（4）： 883-902. 10.1007/s11277-016-3935-z
7	HUANG X W， ZHANG W J， YANG J M， et al. Two-tier trading strategy design for spectrum allocation in heterogeneous cognitive radio networks［J］. IET Communications， 2020， 14（16）： 2759-2768. 10.1049/iet-com.2019.1074
8	ADEDOYIN M， FALOWO O. QoS-aware radio resource allocation for ultra-dense heterogeneous networks［C］// Proceedings of the IEEE 28th Annual International Symposium on Personal， Indoor， and Mobile Radio Communications. Piscataway： IEEE， 2017： 1-7. 10.1109/pimrc.2017.8292177
9	TEEKARAMAN Y， MANOHARAN H， BASHA A R， et al. Hybrid optimization algorithms for resource allocation in heterogeneous cognitive radio networks ［J/OL］. Neural Processing Letters ［2021-08-06］.. 10.1007/s11063-020-10255-2
10	张俊杰，仇润鹤. 基于用户分簇的认知超密集网络资源分配［J/OL］.电讯技术. （2021-08-21）［2021-09-19］.. 10.3969/j.issn.1001-893x.2022.09.018
	ZHANG J J， QIU R H. Resource allocation for cognitive radio ultra-dense network based on user clustering［J/OL］. Telecommunication Engineering （2021-08-21）［2021-09-19］. . 10.3969/j.issn.1001-893x.2022.09.018
11	董晓庆. 异构无线网络密集部署场景下高效网络接入及频谱分配［J］. 计算机工程与应用， 2019， 55（4）：101-111.
	DONG X Q. Efficient network access and spectrum allocation in dense deployment of heterogeneous wireless networks［J］. Computer Engineering and Applications， 2019， 55（4）：101-111.
12	吴宣利，陈旭. 干扰门限与回程容量限制下UDN的能效与谱效联合优化算法［J］. 通信学报， 2019， 40（12）：86-97. 10.11959/j.issn.1000-436x.2019205
	WU X L， CHEN X. Joint energy efficiency and spectral efficiency optimization algorithm for UDN under the restriction of interference threshold and backhaul capacity［J］. Journal on Communications， 2019， 40（12）：86-97. 10.11959/j.issn.1000-436x.2019205
13	余钊贤，易辉跃，裴俊. 5G超密集异构网络带内无线回传资源分配方案［J］. 计算机工程， 2021， 47（3）：43-52.
	YU Z X， YI H Y， PEI J， Resource allocation scheme of in-band wireless backhaul in 5G ultra-dense heterogeneous network［J］. Computer Engineering， 2021， 47（3）：43-52.
14	RAHIM M， ALFAKEEH A S， HUSSAIN R， et al. Efficient channel allocation using matching theory for QoS provisioning in cognitive radio networks［J］. Sensors， 2020， 20（7）： No.1872. 10.3390/s20071872
15	GE X， LI X H， JIN H， et al. Joint user association and user scheduling for load balancing in heterogeneous networks［J］. IEEE Transactions on Wireless Communications， 2018， 17（5）： 3211-3225. 10.1109/twc.2018.2808488
16	龙恳，鲁江丽，李伟，等. 基于遗传算法求解三维匹配的资源分配问题［J］. 计算机工程与设计， 2021， 42（1）：24-30. 10.16208/j.issn1000-7024.2021.01.004
	LONG K， LU J L， LI W， et al. Improved genetic algorithm for solving resource allocation problem of 3D matching［J］. Computer Engineering and Design， 2021， 42（1）：24-30. 10.16208/j.issn1000-7024.2021.01.004
17	KONG H B， FLINT I， WANG P， et al. Modeling and analysis of wireless networks using Poisson hard-core process［C］// Proceedings of the 2017 IEEE International Conference on Communications. Piscataway： IEEE， 2017： 1-6. 10.1109/icc.2017.7997052
18	WANG X， KONG L H， KONG F X， et al. Millimeter wave communication： a comprehensive survey［J］. IEEE Communications Surveys and Tutorials， 2018， 20（3）： 1616-1653. 10.1109/comst.2018.2844322
19	KATOCH S， CHAUHAN S S， KUMAR V. A review on genetic algorithm： past， present， and future［J］. Multimedia Tools and Applications， 2021， 80（5）： 8091-8126. 10.1007/s11042-020-10139-6
20	孙彦赞，范卫蓉，张舜卿，等. 基于图着色的密集D2D网络资源分配算法［J］. 计算机工程， 2019， 45（2）：26-31.
	SUN Y Z， FAN W R， ZHANG S Q， et al. Resource allocation algorithm for dense D2D network based on graph coloring［J］. Computer Engineering， 2019， 45（2）：26-31.

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