APG mergence and topological potential optimization based heuristic user association strategy
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 result...
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Editorial Department of Journal on Communications
2022-06-01
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| Series: | Tongxin xuebao |
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| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022121/ |
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| author | Zhirui HU Meihua BI Fangmin XU Meilin HE Changliang ZHENG |
| author_facet | Zhirui HU Meihua BI Fangmin XU Meilin HE Changliang ZHENG |
| author_sort | Zhirui HU |
| collection | DOAJ |
| description | 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,<inline-formula><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></inline-formula> was defined as the overlap rate between set <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"> <mi mathvariant="script">I</mi><mo>,</mo><mi mathvariant="script">J</mi></math></inline-formula>, 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 <inline-formula><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></inline-formula>,while that of directly solving the optimization problem is<inline-formula><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></inline-formula>.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<sub>2</sub>=0.51,while 4 APs in Fig.3(c)are affected and the network scalable degree is η<sub>2</sub>=0.79.4)Fig.5 shows the simulation results of network scalable degree.Compared with the traditional 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<sub>0</sub>of overlap rate and the upper limit number N<sub>0</sub>of AP associated, effect the performance.As shown in Fig.6,with β<sub>0</sub>or N<sub>0</sub>decreases,η increases and the total user rate decreases. With<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"> <msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>p</mtext> </msub> </math></inline-formula> increases,the effect of β<sub>0</sub>increases and that of N<sub>0</sub>decreases.Take <inline-formula><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></inline-formula>as an example.The η gap between β<sub>0</sub>=0.5 and β<sub>0</sub>=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<sub>0</sub> =20 and N<sub>0</sub> =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. |
| format | Article |
| id | doaj-art-e892c0b6b2c643748ca0372cee7fb62f |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2022-06-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-e892c0b6b2c643748ca0372cee7fb62f2025-01-14T07:23:41ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2022-06-01439810759836832APG mergence and topological potential optimization based heuristic user association strategyZhirui HUMeihua BIFangmin XUMeilin HEChangliang ZHENGObjective: 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,<inline-formula><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></inline-formula> was defined as the overlap rate between set <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"> <mi mathvariant="script">I</mi><mo>,</mo><mi mathvariant="script">J</mi></math></inline-formula>, 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 <inline-formula><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></inline-formula>,while that of directly solving the optimization problem is<inline-formula><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></inline-formula>.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<sub>2</sub>=0.51,while 4 APs in Fig.3(c)are affected and the network scalable degree is η<sub>2</sub>=0.79.4)Fig.5 shows the simulation results of network scalable degree.Compared with the traditional 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<sub>0</sub>of overlap rate and the upper limit number N<sub>0</sub>of AP associated, effect the performance.As shown in Fig.6,with β<sub>0</sub>or N<sub>0</sub>decreases,η increases and the total user rate decreases. With<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"> <msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>p</mtext> </msub> </math></inline-formula> increases,the effect of β<sub>0</sub>increases and that of N<sub>0</sub>decreases.Take <inline-formula><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></inline-formula>as an example.The η gap between β<sub>0</sub>=0.5 and β<sub>0</sub>=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<sub>0</sub> =20 and N<sub>0</sub> =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.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022121/cell-free networkuser-centric networkscalabilityuser association strategy |
| spellingShingle | Zhirui HU Meihua BI Fangmin XU Meilin HE Changliang ZHENG APG mergence and topological potential optimization based heuristic user association strategy Tongxin xuebao cell-free network user-centric network scalability user association strategy |
| title | APG mergence and topological potential optimization based heuristic user association strategy |
| title_full | APG mergence and topological potential optimization based heuristic user association strategy |
| title_fullStr | APG mergence and topological potential optimization based heuristic user association strategy |
| title_full_unstemmed | APG mergence and topological potential optimization based heuristic user association strategy |
| title_short | APG mergence and topological potential optimization based heuristic user association strategy |
| title_sort | apg mergence and topological potential optimization based heuristic user association strategy |
| topic | cell-free network user-centric network scalability user association strategy |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022121/ |
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