Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
5G and Beyond 5G mobile networks use several high-frequency spectrum bands such as the millimeter-wave (mmWave) bands to alleviate the problem of bandwidth scarcity. However high-frequency bands do not cover larger distances. The coverage problem is addressed by using a heterogeneous network which c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/9/3210 |
| _version_ | 1797535243007164416 |
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| author | Yared Zerihun Bekele Young-June Choi |
| author_facet | Yared Zerihun Bekele Young-June Choi |
| author_sort | Yared Zerihun Bekele |
| collection | DOAJ |
| description | 5G and Beyond 5G mobile networks use several high-frequency spectrum bands such as the millimeter-wave (mmWave) bands to alleviate the problem of bandwidth scarcity. However high-frequency bands do not cover larger distances. The coverage problem is addressed by using a heterogeneous network which comprises numerous small and macrocells, defined by transmission and reception points (TRxPs). For such a network, random access is considered a challenging function in which users attempt to select an efficient TRxP by random access within a given time. Ideally, an efficient TRxP is less congested, minimizing delays in users’ random access. However, owing to the nature of random access, it is not feasible to deploy a centralized controller estimating the congestion level of each cell and deliver this information back to users during random access. To solve this problem, we establish an optimization problem and employ a reinforcement-learning-based scheme. The proposed scheme estimates congestion of TRxPs in service and selects the optimal access point. Mathematically, this approach is beneficial in approximating and minimizing a random access delay function. Through simulation, we demonstrate that our proposed deep learning-based algorithm improves performance on random access. Notably, the average access delay is improved by 58.89% from the original 3GPP algorithm, and the probability of successful access also improved. |
| first_indexed | 2024-03-10T11:41:46Z |
| format | Article |
| id | doaj.art-c8593d190b394b429a66fb952a7e2fd8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T11:41:46Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c8593d190b394b429a66fb952a7e2fd82023-11-21T18:28:04ZengMDPI AGSensors1424-82202021-05-01219321010.3390/s21093210Random Access Using Deep Reinforcement Learning in Dense Mobile NetworksYared Zerihun Bekele0Young-June Choi1Department of Artificial Intelligence, Ajou University, Suwon 16499, KoreaDepartment of Software and Computer Engineering, Ajou University, Suwon 16499, Korea5G and Beyond 5G mobile networks use several high-frequency spectrum bands such as the millimeter-wave (mmWave) bands to alleviate the problem of bandwidth scarcity. However high-frequency bands do not cover larger distances. The coverage problem is addressed by using a heterogeneous network which comprises numerous small and macrocells, defined by transmission and reception points (TRxPs). For such a network, random access is considered a challenging function in which users attempt to select an efficient TRxP by random access within a given time. Ideally, an efficient TRxP is less congested, minimizing delays in users’ random access. However, owing to the nature of random access, it is not feasible to deploy a centralized controller estimating the congestion level of each cell and deliver this information back to users during random access. To solve this problem, we establish an optimization problem and employ a reinforcement-learning-based scheme. The proposed scheme estimates congestion of TRxPs in service and selects the optimal access point. Mathematically, this approach is beneficial in approximating and minimizing a random access delay function. Through simulation, we demonstrate that our proposed deep learning-based algorithm improves performance on random access. Notably, the average access delay is improved by 58.89% from the original 3GPP algorithm, and the probability of successful access also improved.https://www.mdpi.com/1424-8220/21/9/3210machine learningoptimizationrandom access |
| spellingShingle | Yared Zerihun Bekele Young-June Choi Random Access Using Deep Reinforcement Learning in Dense Mobile Networks Sensors machine learning optimization random access |
| title | Random Access Using Deep Reinforcement Learning in Dense Mobile Networks |
| title_full | Random Access Using Deep Reinforcement Learning in Dense Mobile Networks |
| title_fullStr | Random Access Using Deep Reinforcement Learning in Dense Mobile Networks |
| title_full_unstemmed | Random Access Using Deep Reinforcement Learning in Dense Mobile Networks |
| title_short | Random Access Using Deep Reinforcement Learning in Dense Mobile Networks |
| title_sort | random access using deep reinforcement learning in dense mobile networks |
| topic | machine learning optimization random access |
| url | https://www.mdpi.com/1424-8220/21/9/3210 |
| work_keys_str_mv | AT yaredzerihunbekele randomaccessusingdeepreinforcementlearningindensemobilenetworks AT youngjunechoi randomaccessusingdeepreinforcementlearningindensemobilenetworks |