Sparse bandit learning based location management for space-ground integrated networks
Sparse bandit learning based location management for space-ground integrated networks
The Space-Ground Integrated Network (SGIN) concept constitutes a promising solution for providing seamless global coverage. However, the mobility of satellites and wireless terminals imposes unprecedented challenges on the location management in SGIN. We tackle this challenge by conceiving a split identifier (ID) and Network Address (NA) based design for providing natural mobility support, and characterize the ID-NA mapping allocation problem by exploiting the storage capacity of both Geostationary Earth Orbit Satellites (GEOSs) and Low Earth Orbiting Satellites (LEOSs) to form a spatially distributed binding resolution system and optimize the caching reward in each LEOS. By considering the large quantity of ID-NA mapping and the sparsity of popular mapping having positive mean caching rewards, we formulate the mapping allocation problem as a sparse Multi-Armed Bandit (MAB) learning procedure, where the mappings are treated as the arms and the LEOSs act as the players. A distributed learning algorithm, namely the Sparse Upper confidence bound based Learning aided Caching algorithm (SULC), is proposed for estimating the mean caching rewards of mappings and selecting the optimal mappings for caching. Moreover, we derive a sub-linear upper bound of the cumulative learning regret to prove the learning efficiency of the proposed SULC. Extensive simulations have been conducted to show that the proposed SULC can quickly identify the popular mappings and provide near-optimal content hit rates. In contrast with the existing solutions, SULC has higher caching rewards and can significantly reduce the cumulative regret after a short period of learning.
Bandits theory, Delays, Handover, Heuristic algorithms, IP networks, Location management, Mapping allocation, Protocols, Reinforcement learning, Resource management, Satellites
10314-10329
He, Huasen
42ffe3a1-f91b-4ec9-b313-8ad2151f3572
Qin, Changkun
7e16c2b7-2693-487c-be20-6f91366c25d4
Chen, Shuangwu
2ae342de-f10c-4f5e-925e-c44b683f7872
Jiang, Xiaofeng
c870dcfa-db0b-4b45-aea5-ccefce44616b
Yang, Jian
9ed96f76-42d8-4bca-9c72-e7c7145a5d29
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
1 August 2023
He, Huasen
42ffe3a1-f91b-4ec9-b313-8ad2151f3572
Qin, Changkun
7e16c2b7-2693-487c-be20-6f91366c25d4
Chen, Shuangwu
2ae342de-f10c-4f5e-925e-c44b683f7872
Jiang, Xiaofeng
c870dcfa-db0b-4b45-aea5-ccefce44616b
Yang, Jian
9ed96f76-42d8-4bca-9c72-e7c7145a5d29
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
He, Huasen, Qin, Changkun, Chen, Shuangwu, Jiang, Xiaofeng, Yang, Jian and Hanzo, Lajos
(2023)
Sparse bandit learning based location management for space-ground integrated networks.
IEEE Transactions on Vehicular Technology, 72 (8), .
(doi:10.1109/TVT.2023.3258140).
Abstract
The Space-Ground Integrated Network (SGIN) concept constitutes a promising solution for providing seamless global coverage. However, the mobility of satellites and wireless terminals imposes unprecedented challenges on the location management in SGIN. We tackle this challenge by conceiving a split identifier (ID) and Network Address (NA) based design for providing natural mobility support, and characterize the ID-NA mapping allocation problem by exploiting the storage capacity of both Geostationary Earth Orbit Satellites (GEOSs) and Low Earth Orbiting Satellites (LEOSs) to form a spatially distributed binding resolution system and optimize the caching reward in each LEOS. By considering the large quantity of ID-NA mapping and the sparsity of popular mapping having positive mean caching rewards, we formulate the mapping allocation problem as a sparse Multi-Armed Bandit (MAB) learning procedure, where the mappings are treated as the arms and the LEOSs act as the players. A distributed learning algorithm, namely the Sparse Upper confidence bound based Learning aided Caching algorithm (SULC), is proposed for estimating the mean caching rewards of mappings and selecting the optimal mappings for caching. Moreover, we derive a sub-linear upper bound of the cumulative learning regret to prove the learning efficiency of the proposed SULC. Extensive simulations have been conducted to show that the proposed SULC can quickly identify the popular mappings and provide near-optimal content hit rates. In contrast with the existing solutions, SULC has higher caching rewards and can significantly reduce the cumulative regret after a short period of learning.
Text
location_managment_sparse_tvt_final
- Accepted Manuscript
More information
Accepted/In Press date: 13 March 2023
e-pub ahead of print date: 17 March 2023
Published date: 1 August 2023
Additional Information:
Funding Information:
This work was supported in part by the National Key R&D Program of China underGrant 2022YFB3902800, in part by theNationalNatural Science Foundations of China under Grants 62173315, 62101525, 62201543 and 62021001, in part by the Youth Innovation Promotion Association of the Chinese Academy of Sciences under Grant 2020450, in part by the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant XDC07020200, in part by the Fundamental Research Funds for the Central Universities, and in part by the China Environment for Network Innovations. The work of Lajos Hanzo was supported in part by Engineering and Physical Sciences Research Council under Projects EP/W016605/1 and EP/X01228X/1 and in part by European Research Council's Advanced Fellow Grant QuantCom under Grant 789028.
Publisher Copyright:
© 1967-2012 IEEE.
Keywords:
Bandits theory, Delays, Handover, Heuristic algorithms, IP networks, Location management, Mapping allocation, Protocols, Reinforcement learning, Resource management, Satellites
Identifiers
Local EPrints ID: 477213
URI: http://eprints.soton.ac.uk/id/eprint/477213
ISSN: 0018-9545
PURE UUID: 2efdca68-2edc-475e-8067-e7ebf9720e91
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Date deposited: 01 Jun 2023 16:44
Last modified: 18 Mar 2024 02:36
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Contributors
Author:
Huasen He
Author:
Changkun Qin
Author:
Shuangwu Chen
Author:
Xiaofeng Jiang
Author:
Jian Yang
Author:
Lajos Hanzo
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