Title | ||
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Autonomous Throughput Improvement Scheme Using Machine Learning Algorithms For Heterogeneous Wireless Networks Aggregation |
Abstract | ||
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It is important to optimize aggregation schemes for heterogeneous wireless networks for maximizing communication throughput utilizing any available radio access networks. In the heterogeneous networks, differences of the quality of service (QoS), such as throughput, delay and packet loss rate, of the networks makes difficult to maximize the aggregation throughput. In this paper, we firstly analyze influences of such differences in QoS to the aggregation throughput, and show that it is possible to improve the throughput by adjusting the parameters of an aggregation system. Since manual parameter optimization is difficult and takes much time, we propose an autonomous parameter tuning scheme using a machine learning algorithm for the heterogeneous wireless network aggregation. We implement the proposed scheme on a heterogeneous cognitive radio network system. The results on our experimental network with network emulators show that the proposed scheme can improve the aggregation throughput better than the conventional schemes. We also evaluate the performance using public wireless network services, such as HSDPA, WiMAX and W-CDMA, and verify that the proposed scheme can improve the aggregation throughput by iterating the learning cycle even for the public wireless networks. Our experimental results show that the proposed scheme achieves twice better aggregation throughput than the conventional schemes. |
Year | DOI | Venue |
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2012 | 10.1587/transcom.E95.B.1143 | IEICE TRANSACTIONS ON COMMUNICATIONS |
Keywords | Field | DocType |
heterogeneous wireless networks, link aggregation, machine learning algorithm support vector machines, cognitive radio | Wireless network,Computer science,Quality of service,Computer network,Artificial intelligence,Maximum throughput scheduling,Throughput,Link aggregation,Distributed computing,Heterogeneous wireless network,Algorithm,Heterogeneous network,Machine learning,Cognitive radio | Journal |
Volume | Issue | ISSN |
E95B | 4 | 0916-8516 |
Citations | PageRank | References |
3 | 0.47 | 17 |
Authors | ||
7 |
Name | Order | Citations | PageRank |
---|---|---|---|
Yohsuke Kon | 1 | 3 | 0.47 |
Kazuki Hashiguchi | 2 | 3 | 0.47 |
Masato Ito | 3 | 3 | 0.47 |
Mikio Hasegawa | 4 | 290 | 43.75 |
Kentaro Ishizu | 5 | 223 | 48.06 |
Homare Murakami | 6 | 130 | 22.90 |
Hiroshi Harada | 7 | 476 | 77.46 |