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杭州电子科技大学通信工程学院,浙江 杭州 310018
[ "胡志蕊(1987- ),女,山东德州人,博士,杭州电子科技大学讲师,主要研究方向为协作通信、资源分配、信能同传技术" ]
[ "毕美华(1981- ),女,山东济宁人,博士,杭州电子科技大学副教授,主要研究方向为光与无线融合接入、新型光接入网系统的物理层安全、可重构的智能数据中心网络系统、IMDD/相干光传输系统中的新型均衡算法等" ]
[ "许方敏(1980- ),女,浙江瑞安人,博士,杭州电子科技大学讲师,主要研究方向为小区间干扰抑制、无线资源管理、MIMO建模及干扰删除等" ]
[ "何美霖(1986- ),女,湖南衡阳人,博士,杭州电子科技大学讲师,主要研究方向为 NOMA、多速率编码、多用户信息论和无线通信等" ]
[ "郑长亮(1980- ),男,河北唐山人,博士,杭州电子科技大学讲师,主要研究方向为无线通信、移动通信等" ]
网络出版日期:2022-06,
纸质出版日期:2022-06-25
移动端阅览
胡志蕊, 毕美华, 许方敏, 等. 基于APG合并及拓扑势优化的启发式用户关联策略[J]. 通信学报, 2022,43(6):98-107.
Zhirui HU, Meihua BI, Fangmin XU, et al. APG mergence and topological potential optimization based heuristic user association strategy[J]. Journal on communications, 2022, 43(6): 98-107.
胡志蕊, 毕美华, 许方敏, 等. 基于APG合并及拓扑势优化的启发式用户关联策略[J]. 通信学报, 2022,43(6):98-107. DOI: 10.11959/j.issn.1000-436x.2022121.
Zhirui HU, Meihua BI, Fangmin XU, et al. APG mergence and topological potential optimization based heuristic user association strategy[J]. Journal on communications, 2022, 43(6): 98-107. DOI: 10.11959/j.issn.1000-436x.2022121.
目的:去蜂窝网络通过接入点(AP)间协同服务网络内的用户,可突破传统蜂窝网络因密集小区间干扰造成的性能瓶颈,但需要大量的信息交互及信号处理,导致网络的可扩展性较差。为此,本文研究可提升去蜂窝网络可扩展性的用户关联策略。
方法:设计网络可扩展度作为可扩展性的衡量指标,以此为基础,利用优化理论研究提高网络可扩展度的用户关联策略。1)优化问题建模方面,首先,以节点间关联度是影响网络可扩展度的关键因素为突破点,构造表征节点间关联度的网络耦合度指标,以此建立起网络可扩展度与接入点簇(APG)间的数学关系,从而将提高网络可扩展度问题建模为最小化网络耦合度问题。然后,建立网络耦合度最小和用户速率最大的多目标优化问题,以此寻求网络可扩展度与网络服务质量的均衡。2)优化问题求解方面,为避免求解多目标优化问题的高计算复杂度,提出基于APG合并及拓扑势优化的启发式算法。所提算法通过APG合并的方式降低APG数目,并通过AP退出APG的方式降低AP所属APG的数目,从而降低网络耦合度,提高网络可扩展度。在APG合并方面,定义
<math xmlns="http://www.w3.org/1998/Math/MathML"> <mi>β</mi><mo>{</mo><mi mathvariant="script">I</mi><mo>
</mo><mi mathvariant="script">J</mi><mo>}</mo><mo>=</mo><mn>2</mn><mrow><mo>|</mo> <mrow> <mi mathvariant="script">I</mi><mo>∩</mo><mi mathvariant="script">J</mi></mrow> <mo>|</mo></mrow><mo>/</mo><mrow><mo>(</mo> <mrow> <mrow><mo>|</mo> <mi mathvariant="script">I</mi> <mo>|</mo></mrow><mo>+</mo><mrow><mo>|</mo> <mi mathvariant="script">J</mi> <mo>|</mo></mrow></mrow> <mo>)</mo></mrow></math>
表示集合
<math xmlns="http://www.w3.org/1998/Math/MathML"> <mi mathvariant="script">I</mi><mo>
</mo><mi mathvariant="script">J</mi></math>
间的重叠率,并将重叠率超过一定门限值的APG进行合并;在AP退出APG方面,利用拓扑势函数建立网络耦合度和用户速率的关系,以此作为AP选择退出APG的性能指标。
结果:1)问题建模的合理性方面,如图2和图5所示,网络可扩展度η与网络耦合度κ的大小成反比,因此将提高网络可扩展度问题建模为最小化网络耦合度问题是合理的,通过降低网络耦合度来提高网络可扩展度是可行的;2)启发式算法的计算复杂度方面,所提算法的计算复杂度上限为
<math xmlns="http://www.w3.org/1998/Math/MathML"> <mi mathvariant="script">O</mi><mo stretchy="false">(</mo><mi>K</mi><mi>N</mi><msub> <mi>log</mi> <mn>2</mn> </msub> <mi>N</mi><mo>+</mo><msup> <mi>k</mi> <mn>2</mn> </msup> <mo>+</mo><mi>N</mi><mi>N</mi><msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>p</mtext> </msub> <mo stretchy="false">)</mo></math>
,而直接求解优化问题的计算复杂度为
<math xmlns="http://www.w3.org/1998/Math/MathML"><mi mathvariant="script">O</mi><mo stretchy="false">(</mo><msup> <mi>N</mi> <mrow> <msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>u</mtext> </msub> <mi>K</mi></mrow> </msup> <mo stretchy="false">)</mo></math>
;3)网络可扩展度的理论分析方面,以图3为例,假设AP2发生变动,图3(a)中受影响的AP有12个,网络可扩展度为η
2
=0.51,而图3(c)中受影响的AP有4个,网络可扩展度为η
2
=0.79;4)网络可扩展度的仿真验证方面,如图5所示,与传统策略相比,用户速率损失4.43%时,可扩展度可提高9.59%;与文献[10
]
的策略相比,用户速率损失4.99%时,可扩展度可提高22.15%;5)所提算法中重叠率门限β
0
和AP所关联AP数上限N
0
的影响方面,如图6所示,随着β
0
或N
0
的减小,h提高,用户总速率降低。随着
<math xmlns="http://www.w3.org/1998/Math/MathML"> <msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>p</mtext> </msub> </math>
的增大,β
0
的影响度增加,N
0
的影响度降低。以
<math xmlns="http://www.w3.org/1998/Math/MathML"> <msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>p</mtext> </msub> <mo>=</mo><mn>40</mn><mo>
</mo><mn>60</mn></math>
为例,β
0
=0.5和β
0
=0.9间h的差距由5.97%增大到14.17%,用户速率差距由47 bit/(s·Hz)增大到155 bit/(s·Hz);而N
0
=20和N
0
=60间η的差距由1.4%减小到0.4%,用户速率差距由76 bit/(s·Hz)减小到29 bit/(s·Hz)。
结论:所提用户关联策略以较小的用户速率损失为代价,提高了去蜂窝网络的可扩展度。所提策略中重叠率门限或AP所关联AP数上限越小,则网络可扩展度提升越多,速率损失越大。
Objective: In cell-free networks
access points (AP) collaborate to serve users. This coordination can break the performance bottleneck of traditional cellular network caused by inter-cell interference. However
it needs significant amounts information interaction and signal processing
which results in poor scalability. This paper studied the user association strategy that could improve the scalability of cell-free networks.
Methods:The network scalable degree was designed as a measure of scalability
and then a user association strategy to improve network scalable degree was studied by using optimization theory. 1) For modelling the optimization problem
firstly
the network coupling degree
representing the degree of association among nodes
was constructed to establish the mathematical relationship between the network scalable degree and AP group (APG).Thus
the problem of improving the network scalable degree was modeled as the problem of minimizing the network coupling degree.Then
a multi-objective optimization problem of minimum network coupling degree and maximum user rate was established to find the balance between network scalable degree and network service quality. 2) For solving the optimization problem
to avoid the high computational complexity
a heuristic user association strategy based on APG mergence and topological potential optimization was proposed.With the proposed algorithm
the number of APG could be reduced by APG mergence
and the number of APG that AP belongs to could be reduced by AP exiting APG. Thus
it can reduce the network coupling degree and improve the network scalable degree. For APG mergence
<math xmlns="http://www.w3.org/1998/Math/MathML"> <mi mathvariant="script">O</mi><mo stretchy="false">(</mo><mi>K</mi><mi>N</mi><msub> <mi>log</mi> <mn>2</mn> </msub> <mi>N</mi><mo>+</mo><msup> <mi>k</mi> <mn>2</mn> </msup> <mo>+</mo><mi>N</mi><mi>N</mi><msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>p</mtext> </msub> <mo stretchy="false">)</mo></math>
was defined as the overlap rate between set
<math xmlns="http://www.w3.org/1998/Math/MathML"> <mi mathvariant="script">I</mi><mo>
</mo><mi mathvariant="script">J</mi></math>
and the APG whose overlap rate exceeds a certain threshold value would be merged. In terms of AP exiting APG
the relationship between network coupling degree and user rate was established by topological potential function
which was used as the performance index of AP exiting APG.
Results:1)For the rationality of problem modeling
Fig.2 and Fig.5 show that the network scalable degree is inversely proportional to the network coupling degree. Therefore
it is reasonable to model the problem of improving network scalable degree as minimizing network coupling degree
and it is feasible to improve network scalable degree by reducing network coupling degree.2)The upper limit of computational complexity of the proposed algorithm is
<math xmlns="http://www.w3.org/1998/Math/MathML"> <mi mathvariant="script">O</mi><mo stretchy="false">(</mo><mi>K</mi><mi>N</mi><msub> <mi>log</mi> <mn>2</mn> </msub> <mi>N</mi><mo>+</mo><msup> <mi>k</mi> <mn>2</mn> </msup> <mo>+</mo><mi>N</mi><mi>N</mi><msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>p</mtext> </msub> <mo stretchy="false">)</mo></math>
while that of directly solving the optimization problem is
<math xmlns="http://www.w3.org/1998/Math/MathML"><mi mathvariant="script">O</mi><mo stretchy="false">(</mo><msup> <mi>N</mi> <mrow> <msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>u</mtext> </msub> <mi>K</mi></mrow> </msup> <mo stretchy="false">)</mo></math>
.3)For theoretical analysis of the network scalable degree
take Fig.3 as an example.If AP2 changes
12 APs in Fig. 3(a)are affected and the network scalable degree is η
2
=0.51
while 4 APs in Fig.3(c)are affected and the network scalable degree is η
2
=0.79.4)Fig.5 shows the simulation results of network scalable degree.Compared with the traditi
onal strategy
the network scalable degree is improved by 9.59% with 4.43% user rate loss.Compared with the strategy in[10
]
the network scalable degree is improved by 22.15% with 4.99% user rate loss. 5) The algorithm parameters
the threshold β
0
of overlap rate and the upper limit number N
0
of AP associated
effect the performance.As shown in Fig.6
with β
0
or N
0
decreases
η increases and the total user rate decreases. With
<math xmlns="http://www.w3.org/1998/Math/MathML"> <msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>p</mtext> </msub> </math>
increases
the effect of β
0
increases and that of N
0
decreases.Take
<math xmlns="http://www.w3.org/1998/Math/MathML"> <msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>p</mtext> </msub> <mo>=</mo><mn>40</mn><mo>
</mo><mn>60</mn></math>
as an example.The η gap between β
0
=0.5 and β
0
=0.9 increases from 5.97% to 14.17%
and the user rate gap increases from 47 bit/(s·Hz) to 155 bit/(s·Hz). The η gap between N
0
=20 and N
0
=60 decreases from 1.4% to 0.4%
and the user rate gap decreases from 76 bit/(s·Hz)to 29 bit/(s·Hz).
Conclusions: The proposed user association strategy can improve the network scalable degree of cell-free networks at the cost of less rate loss. The smaller the overlap rate threshold or the upper limit of APs associated with an AP
the more the network scalable degree increases and the greater the rate loss.
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