Adaptive Bandwidth Allocation for Massive MIMO Systems Based on Multiple Services
Aiming at the characteristics of resource periodicity in massive MIMO systems and bandwidth allocation without comprehensive consideration of user service QoS and channel state information, resulting in poor user satisfaction and low bandwidth utilization, this paper proposes an adaptive bandwidth a...
| 發表在: | Applied Sciences |
|---|---|
| Main Authors: | , , , , |
| 格式: | Article |
| 語言: | 英语 |
| 出版: |
MDPI AG
2023-08-01
|
| 主題: | |
| 在線閱讀: | https://www.mdpi.com/2076-3417/13/17/9861 |
| _version_ | 1849895035315683328 |
|---|---|
| author | Qingli Liu Rui Li Yangyang Li Peiling Wang Jiaxu Sun |
| author_facet | Qingli Liu Rui Li Yangyang Li Peiling Wang Jiaxu Sun |
| author_sort | Qingli Liu |
| collection | DOAJ |
| container_title | Applied Sciences |
| description | Aiming at the characteristics of resource periodicity in massive MIMO systems and bandwidth allocation without comprehensive consideration of user service QoS and channel state information, resulting in poor user satisfaction and low bandwidth utilization, this paper proposes an adaptive bandwidth allocation method based on user services. This method comprehensively considers factors, such as user service QoS, channel state information, and resource periodicity, to adaptively allocate bandwidth for users using different services. Firstly, based on the service priority, the user priority is dynamically adjusted according to the current channel state information and the continuous periodicity of the allocation, and the user is scheduled.; Secondly, the dynamic priority is combined with the minimum guaranteed time slot to establish the objective function of adaptive bandwidth allocation. Finally, chaos theory, Levy flight, and reverse learning are integrated to improve the bald eagle optimization algorithm. The improved bald eagle algorithm is used to solve the problem, and the optimal solution to bandwidth allocation is obtained. The simulation shows that compared with the traditional bandwidth allocation method based on user service quality perception, the bandwidth allocation algorithm based on the minimum rate requirement, and the ant colony-based allocation algorithm, the bandwidth allocation method proposed in this paper improves the system utility value, bandwidth utilization rate, throughput, and user satisfaction by 23.70%, 4.22%, 6.55%, and 4.28%, respectively, and better meets the business needs of users. |
| format | Article |
| id | doaj-art-caaac1b515bf46c89d6edd6adb77458b |
| institution | Directory of Open Access Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| spelling | doaj-art-caaac1b515bf46c89d6edd6adb77458b2025-08-20T01:02:10ZengMDPI AGApplied Sciences2076-34172023-08-011317986110.3390/app13179861Adaptive Bandwidth Allocation for Massive MIMO Systems Based on Multiple ServicesQingli Liu0Rui Li1Yangyang Li2Peiling Wang3Jiaxu Sun4Communication and Network Laboratory, Dalian University, Dalian 116622, ChinaCommunication and Network Laboratory, Dalian University, Dalian 116622, ChinaCommunication and Network Laboratory, Dalian University, Dalian 116622, ChinaCommunication and Network Laboratory, Dalian University, Dalian 116622, ChinaCommunication and Network Laboratory, Dalian University, Dalian 116622, ChinaAiming at the characteristics of resource periodicity in massive MIMO systems and bandwidth allocation without comprehensive consideration of user service QoS and channel state information, resulting in poor user satisfaction and low bandwidth utilization, this paper proposes an adaptive bandwidth allocation method based on user services. This method comprehensively considers factors, such as user service QoS, channel state information, and resource periodicity, to adaptively allocate bandwidth for users using different services. Firstly, based on the service priority, the user priority is dynamically adjusted according to the current channel state information and the continuous periodicity of the allocation, and the user is scheduled.; Secondly, the dynamic priority is combined with the minimum guaranteed time slot to establish the objective function of adaptive bandwidth allocation. Finally, chaos theory, Levy flight, and reverse learning are integrated to improve the bald eagle optimization algorithm. The improved bald eagle algorithm is used to solve the problem, and the optimal solution to bandwidth allocation is obtained. The simulation shows that compared with the traditional bandwidth allocation method based on user service quality perception, the bandwidth allocation algorithm based on the minimum rate requirement, and the ant colony-based allocation algorithm, the bandwidth allocation method proposed in this paper improves the system utility value, bandwidth utilization rate, throughput, and user satisfaction by 23.70%, 4.22%, 6.55%, and 4.28%, respectively, and better meets the business needs of users.https://www.mdpi.com/2076-3417/13/17/9861massive MIMO systembandwidth allocationuser trafficchannel statedynamic priority |
| spellingShingle | Qingli Liu Rui Li Yangyang Li Peiling Wang Jiaxu Sun Adaptive Bandwidth Allocation for Massive MIMO Systems Based on Multiple Services massive MIMO system bandwidth allocation user traffic channel state dynamic priority |
| title | Adaptive Bandwidth Allocation for Massive MIMO Systems Based on Multiple Services |
| title_full | Adaptive Bandwidth Allocation for Massive MIMO Systems Based on Multiple Services |
| title_fullStr | Adaptive Bandwidth Allocation for Massive MIMO Systems Based on Multiple Services |
| title_full_unstemmed | Adaptive Bandwidth Allocation for Massive MIMO Systems Based on Multiple Services |
| title_short | Adaptive Bandwidth Allocation for Massive MIMO Systems Based on Multiple Services |
| title_sort | adaptive bandwidth allocation for massive mimo systems based on multiple services |
| topic | massive MIMO system bandwidth allocation user traffic channel state dynamic priority |
| url | https://www.mdpi.com/2076-3417/13/17/9861 |
| work_keys_str_mv | AT qingliliu adaptivebandwidthallocationformassivemimosystemsbasedonmultipleservices AT ruili adaptivebandwidthallocationformassivemimosystemsbasedonmultipleservices AT yangyangli adaptivebandwidthallocationformassivemimosystemsbasedonmultipleservices AT peilingwang adaptivebandwidthallocationformassivemimosystemsbasedonmultipleservices AT jiaxusun adaptivebandwidthallocationformassivemimosystemsbasedonmultipleservices |
