D2D联合模式选择与资源分配的研究

doi:10.11871/jfdc.issn.2096-742X.2023.06.016

数据与计算发展前沿 ›› 2023, Vol. 5 ›› Issue (6): 173-184.

CSTR: 32002.14.jfdc.CN10-1649/TP.2023.06.016

doi: 10.11871/jfdc.issn.2096-742X.2023.06.016

• • 上一篇

D2D联合模式选择与资源分配的研究

郑奕佳^*(),杜永文,黄菊,张希权

兰州交通大学，电子与信息工程学院，甘肃兰州 730070

收稿日期:2022-07-29 出版日期:2023-12-20 发布日期:2023-12-25
通讯作者: *郑奕佳（E-mail: 2467239283@qq.com）
作者简介:郑奕佳，兰州交通大学电子与信息工程学院，硕士研究生，研究方向为无线通信网络。
本文主要负责实验调研及论文撰写。
ZHENG Yijia is a graduate student of the School of Electronics and Information Engineering, Lanzhou Jiaotong University. Her research direction is wireless communication networks.In this paper, she is mainly responsible for experimental research and paper writing.
E-mail: 2467239283@qq.com
基金资助:
国家自然科学基金(11461038);甘肃省科技支撑计划(144NKCA040)

Research on D2D Joint Mode Selection and Resource Allocation

ZHENG Yijia^*(),DU Yongwen,HUANG Ju,ZHANG Xiquan

School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China

Received:2022-07-29 Online:2023-12-20 Published:2023-12-25

摘要/Abstract

摘要：

【目的】 在通信资源相对匮乏的情况下，针对D2D直通技术复用蜂窝网络资源可能会带来干扰管理、通信模式选择和资源分配等众多技术问题。【方法】 本文研究选用模式选择、信道分配和功率控制相结合的方案，由于此方案分配问题是NP-hard问题，因此本文将优化问题划分为两个子问题，其一是为D2D选择合适的通信模式和为每个D2D用户分配合理的信道资源，其二是通过改进的灰狼优化算法为D2D用户和蜂窝用户进行功率优化，以提高整体系统吞吐量，同时保证干扰最小化。【结果】 综合上述不同场景下的实验结果表明，本文方案能够有效地降低干扰，提高系统吞吐量，改进的灰狼算法为本方案提升了大约4%的平均吞吐量。【局限】 灰狼优化算法处于不断地发展演变之中，自身也存在着一定的局限性。【结论】 在后续的工作中将继续探索并考虑网络状态和用户移动性，在保证系统性能的同时提高系统吞吐量。

关键词: 蜂窝网络, D2D, 模式选择, 资源分配, 灰狼算法

Abstract:

[Objective] In the case of relatively scarce communication resources, the D2D direct reuse technology of cellular network resources may cause numerous technical problems such as interference management, communication mode selection, and resource allocation. [Methods] This paper studies the scheme of the combination of mode selection, channel distribution, and power control. Because optimizing the combination scheme is an NP-hard problem, this paper divides the optimization problem into two sub-problems: one is to select an appropriate communication mode and allocate reasonable channel resources for each D2D user, and the other is to optimize the power for D2D users and honeycomb users by the improved gray wolf algorithm. The goal of this approach is to improve the overall system throughput and reduce the interference. [Results] The experimental results in different scenarios show that this approach can effectively reduce the interference and improve the system throughput. The improved gray wolf algorithm has increased the average throughput by about 4% for this solution. [Limitations] The Gray Wolf Optimization Algorithm is continuously evolving and introduces certain limitations. [Conclusions] The subsequent work will continue to explore and consider the network status and the user mobility, improving both performance and throughput of the system.

Key words: cellular network, D2D, mode selection, resource allocation, gray wolf algorithm

郑奕佳, 杜永文, 黄菊, 张希权. D2D联合模式选择与资源分配的研究[J]. 数据与计算发展前沿, 2023, 5(6): 173-184.

ZHENG Yijia, DU Yongwen, HUANG Ju, ZHANG Xiquan. Research on D2D Joint Mode Selection and Resource Allocation[J]. Frontiers of Data and Computing, 2023, 5(6): 173-184, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2023.06.016.

图/表 11

图1

算法1

模式选择算法"

Algorithm 1: Mode selection algorithm

计算每个D2D用户在蜂窝模式和专用模式的信道容量
创建一个向量集合T,其中元素为每个D2D用户在蜂窝模式下和专用模式下信道容量对比后的最大值.
初始化上行信道计数器

n 1

=0,下行信道计数器

n 2

=0.
While

n 1

N u

and

n 2

N D

do
select k*= argmaxTk
If

T k *

T k * 1

then

n 2

set

n 1

n 1

+1,

n 2

=n2+1,

x k * 1

=1.
else
if

N u

-n1>

N D

-n2 then
set n1=n1+1 and

x k * 2

=1.
else
set n₂=n₂+1 and

x k * 2

=1.
end if
end if
Set

T k * 1

T k * 2

=0_,并且更新T
end while
For i=1 to max{

N u

-n1,

N D

-n2}
select k*=argmax

T k 2

,set

x k * 2

=1
end for

算法1

算法2

信道分配算法"

Algorithm 2: The Channel Allocation Algorithm
初始化M个CU和N个DU的用户参数信息利用HK算法得到矩阵（35）中每个DU的最佳信道安排. for all k=1:k- $N D$ - $N u$ do set $x π k, ω π k 3$ =1 end for for all k=1:k- $N D$ +1:k do set $x π k 2$ =1 end for

算法2

图2

图3

算法3

基于CGWO的功率控制算法"

Algorithm 3： Power control algorithm

输入：种群数量，搜索维度，迭代次数，D2D用户
输出: 吞吐量
初始化种群数量，计算满足约束条件（15）和（16）的个体位置；
根据（20），（21），（22）计算适应度值；
找出三个最大适应度值；
While（t<t_max）;
通过改进的非线性收敛因子以及种群更新策略更新

X α

、

X β

、

X δ

：
t=t+1
end while
最优个体为DUE最优功率分配结果，得到最佳发射功率矩阵；
输出系统中所有DUE的最大吞吐量

算法3

表4

仿真参数"

参数	参数值
蜂窝半径（R）	0.5KM
蜂窝用户数	10-20
带宽	180KHZ
最大CU传输功率	25dBm
最大D2D传输功率	25dBm
上行信道数	20
下行信道数	20
SING阈值, $? m i n$	10dm
D2D用户数	活跃的CU个数的10%，20%...100%
噪声功率	-174dBm/HZ
快速衰落	瑞丽衰落

表4

表5

基准测试函数"

Function	Function Expression	Range	Fmin
F7	$f 7 (x) = 0.1 {s i n 2 (3 π x 1) + ∑ i = 1 n (x i - 1) 2 [1 + s i n 2 (3 π x i + 1)] + (x n - 1) 2 [1 + s i n 2 (2 π x n)]} + ∑ i = 1 n u (x i, 5,100,4)$	[-1.28,1.28]	0
F10	$f 10 (x) = - 20 e x p (- 0.2 1 n ∑ i = 1 n c o s (2 π x i)) + 20 + e$	[-32,32]	0
F14	$f 14 (x) = 1500 + ∑ j = 1 25 1 j + ∑ i = 1 2 (x i - x i j) 6 - 1$	[-65,65]	1
F15	$f 15 (x) = ∑ i = 1 11 [a i - x 1 (b i 2 + b i x 2) b i 2 + b i x 3 + x 4] 2$	[-5,5]	0.00030

表5

图4

图5

图6

参考文献 18

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D2D联合模式选择与资源分配的研究

Research on D2D Joint Mode Selection and Resource Allocation

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