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 $N_{0}$ / $(d B m \cdot H z^{- 1})$	-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 $N_{0}$ / $(d B m \cdot H z^{- 1})$	-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

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