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Cooperative cache in cognitive radio networks: A heterogeneous multi-agent learning approach

Cooperative cache in cognitive radio networks: A heterogeneous multi-agent learning approach
Cooperative cache in cognitive radio networks: A heterogeneous multi-agent learning approach
Deploying distributed cache in cognitive radio networks (CRNs), which spreads popular contents to the edge of network during the off-peak time through spectrum sharing, can reduce the deliver delay to users nearby without causing severe interference to the primary network. However, due to the un-predicable contents requirement as well as the band occupation of primary users, it is non-trivial to optimize the cache storage and contents fetching strategy of users dynamically. The paper proposes a heterogeneous multi-agent deep deterministic policy gradient (MADDPG) approach, which takes users and cache servers as two different types of agents to learn the cooperation and competition for mutual benefits. The numeral simulation demonstrates that comparing with the other single or homogeneous deep reinforcement learning (DRL) approaches, the proposed heterogeneous MADDPG can further reduce the delivery delay of users and enhance the cache efficiency of SBSs.
Cache storage, Cognitive Radio Networks, Cooperative Cache, Costs, Delays, Multi-Agent Deep Deterministic Policy Gradient, Optimization, Servers, System performance, Training
1089-7798
Gao, Ang
c50ad415-a1c3-4592-beee-b1ff254eb087
Liu, Hengtong
5d5daa91-6306-4417-9ace-a90e624e8483
Hu, Yansu
ce2f0b39-c4bd-4097-ae9c-46a04db9ef10
Liang, Wei
9576aa89-5e9a-489f-ae64-1f30628d3514
Ng, Soon Xin
e19a63b0-0f12-4591-ab5f-554820d5f78c
Gao, Ang
c50ad415-a1c3-4592-beee-b1ff254eb087
Liu, Hengtong
5d5daa91-6306-4417-9ace-a90e624e8483
Hu, Yansu
ce2f0b39-c4bd-4097-ae9c-46a04db9ef10
Liang, Wei
9576aa89-5e9a-489f-ae64-1f30628d3514
Ng, Soon Xin
e19a63b0-0f12-4591-ab5f-554820d5f78c

Gao, Ang, Liu, Hengtong, Hu, Yansu, Liang, Wei and Ng, Soon Xin (2022) Cooperative cache in cognitive radio networks: A heterogeneous multi-agent learning approach. IEEE Communications Letters. (doi:10.1109/LCOMM.2022.3151877).

Record type: Article

Abstract

Deploying distributed cache in cognitive radio networks (CRNs), which spreads popular contents to the edge of network during the off-peak time through spectrum sharing, can reduce the deliver delay to users nearby without causing severe interference to the primary network. However, due to the un-predicable contents requirement as well as the band occupation of primary users, it is non-trivial to optimize the cache storage and contents fetching strategy of users dynamically. The paper proposes a heterogeneous multi-agent deep deterministic policy gradient (MADDPG) approach, which takes users and cache servers as two different types of agents to learn the cooperation and competition for mutual benefits. The numeral simulation demonstrates that comparing with the other single or homogeneous deep reinforcement learning (DRL) approaches, the proposed heterogeneous MADDPG can further reduce the delivery delay of users and enhance the cache efficiency of SBSs.

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CoopertiveCacheGao - Accepted Manuscript
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More information

Accepted/In Press date: 22 January 2022
Published date: 16 February 2022
Keywords: Cache storage, Cognitive Radio Networks, Cooperative Cache, Costs, Delays, Multi-Agent Deep Deterministic Policy Gradient, Optimization, Servers, System performance, Training

Identifiers

Local EPrints ID: 456538
URI: http://eprints.soton.ac.uk/id/eprint/456538
ISSN: 1089-7798
PURE UUID: 51a75501-c218-438e-b952-e0d3cf99bb4c
ORCID for Soon Xin Ng: ORCID iD orcid.org/0000-0002-0930-7194

Catalogue record

Date deposited: 04 May 2022 17:13
Last modified: 05 May 2022 01:35

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Contributors

Author: Ang Gao
Author: Hengtong Liu
Author: Yansu Hu
Author: Wei Liang
Author: Soon Xin Ng ORCID iD

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