Fuzzy Logic and Accelerated Reinforcement Learning-Based User Association for Dense C-RANs
Cloud radio access network (C-RAN) is a potential mobile network architecture providing seamless connectivity to users with high data rates by integrating it with the small-cell technology of 5G mobile communication systems. In C-RAN, base station functionality is divided into a baseband unit (BBU)...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9521471/ |
| _version_ | 1818602980750917632 |
|---|---|
| author | Rehenuma Tasnim Rodoshi Taewoon Kim Wooyeol Choi |
| author_facet | Rehenuma Tasnim Rodoshi Taewoon Kim Wooyeol Choi |
| author_sort | Rehenuma Tasnim Rodoshi |
| collection | DOAJ |
| description | Cloud radio access network (C-RAN) is a potential mobile network architecture providing seamless connectivity to users with high data rates by integrating it with the small-cell technology of 5G mobile communication systems. In C-RAN, base station functionality is divided into a baseband unit (BBU) and remote radio head (RRH); the BBUs from multiple sites are centralized and virtualized using cloud computing and virtualization techniques. Frequent handovers occur in the network, which results in control message flooding and repeated service outages because of the dense deployment of short-range RRHs and user mobility. It is necessary to optimize handover control parameters before the handover and re-associate the user with an RRH to minimize unnecessary handovers in the network. Traditional handover schemes rely on signal strengths of RRHs, which cause a large number of unnecessary handovers when a user moves within the coverage of multiple-overlapped RRHs. This study investigates the handover in C-RAN by carefully optimizing the handover control parameter and selecting the target RRH for handover. Our main goal is associating the user with an RRH such that association after the handover remains possible for as long as possible while maintaining the quality of service (QoS) requirements of the users. We have used fuzzy logic to optimize the handover control parameter and a reinforcement learning-based algorithm to select the target RRH. A key component of the proposed RL-based scheme is using an acceleration technique for the faster convergence of the algorithm. Numerical results show that the proposed scheme can significantly reduce the number of handovers while ensuring QoS requirements. |
| first_indexed | 2024-12-16T13:15:54Z |
| format | Article |
| id | doaj.art-c9620a67bc3b48a88047fac7be28c277 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T13:15:54Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c9620a67bc3b48a88047fac7be28c2772022-12-21T22:30:29ZengIEEEIEEE Access2169-35362021-01-01911791011792410.1109/ACCESS.2021.31073259521471Fuzzy Logic and Accelerated Reinforcement Learning-Based User Association for Dense C-RANsRehenuma Tasnim Rodoshi0https://orcid.org/0000-0001-9662-6056Taewoon Kim1https://orcid.org/0000-0002-7811-5022Wooyeol Choi2https://orcid.org/0000-0002-7834-4044Department of Computer Engineering, Chosun University, Gwangju, Republic of KoreaSchool of Software, Hallym University, Chuncheon, Republic of KoreaDepartment of Computer Engineering, Chosun University, Gwangju, Republic of KoreaCloud radio access network (C-RAN) is a potential mobile network architecture providing seamless connectivity to users with high data rates by integrating it with the small-cell technology of 5G mobile communication systems. In C-RAN, base station functionality is divided into a baseband unit (BBU) and remote radio head (RRH); the BBUs from multiple sites are centralized and virtualized using cloud computing and virtualization techniques. Frequent handovers occur in the network, which results in control message flooding and repeated service outages because of the dense deployment of short-range RRHs and user mobility. It is necessary to optimize handover control parameters before the handover and re-associate the user with an RRH to minimize unnecessary handovers in the network. Traditional handover schemes rely on signal strengths of RRHs, which cause a large number of unnecessary handovers when a user moves within the coverage of multiple-overlapped RRHs. This study investigates the handover in C-RAN by carefully optimizing the handover control parameter and selecting the target RRH for handover. Our main goal is associating the user with an RRH such that association after the handover remains possible for as long as possible while maintaining the quality of service (QoS) requirements of the users. We have used fuzzy logic to optimize the handover control parameter and a reinforcement learning-based algorithm to select the target RRH. A key component of the proposed RL-based scheme is using an acceleration technique for the faster convergence of the algorithm. Numerical results show that the proposed scheme can significantly reduce the number of handovers while ensuring QoS requirements.https://ieeexplore.ieee.org/document/9521471/C-RANfuzzy logicreinforcement learninguser associationhandover |
| spellingShingle | Rehenuma Tasnim Rodoshi Taewoon Kim Wooyeol Choi Fuzzy Logic and Accelerated Reinforcement Learning-Based User Association for Dense C-RANs IEEE Access C-RAN fuzzy logic reinforcement learning user association handover |
| title | Fuzzy Logic and Accelerated Reinforcement Learning-Based User Association for Dense C-RANs |
| title_full | Fuzzy Logic and Accelerated Reinforcement Learning-Based User Association for Dense C-RANs |
| title_fullStr | Fuzzy Logic and Accelerated Reinforcement Learning-Based User Association for Dense C-RANs |
| title_full_unstemmed | Fuzzy Logic and Accelerated Reinforcement Learning-Based User Association for Dense C-RANs |
| title_short | Fuzzy Logic and Accelerated Reinforcement Learning-Based User Association for Dense C-RANs |
| title_sort | fuzzy logic and accelerated reinforcement learning based user association for dense c rans |
| topic | C-RAN fuzzy logic reinforcement learning user association handover |
| url | https://ieeexplore.ieee.org/document/9521471/ |
| work_keys_str_mv | AT rehenumatasnimrodoshi fuzzylogicandacceleratedreinforcementlearningbaseduserassociationfordensecrans AT taewoonkim fuzzylogicandacceleratedreinforcementlearningbaseduserassociationfordensecrans AT wooyeolchoi fuzzylogicandacceleratedreinforcementlearningbaseduserassociationfordensecrans |