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المؤلفSaad H.
المؤلفMohamed A.
المؤلفElbatt T.
تاريخ الإتاحة2022-04-21T08:58:34Z
تاريخ النشر2013
اسم المنشورIEEE Vehicular Technology Conference
المصدرScopus
المعرّفhttp://dx.doi.org/10.1109/VTCFall.2013.6692027
معرّف المصادر الموحدhttp://hdl.handle.net/10576/30159
الملخصIn this paper, we address the problem of distributed interference management of cognitive femtocells that share the same frequency range with macrocells using distributed multiagent Q-learning. We formulate and solve three problems representing three different Q-learning algorithms: namely, centralized, femto-based distributed and subcarrier-based distributed power control using Q-learning (CPC-Q, FBDPC-Q and SBDPCQ). CPC-Q, although not of practical interest, characterizes the global optimum. Each of FBDPC-Q and SBDPC-Q works in two different learning paradigms: Independent (IL) and Cooperative (CL). The former is considered the simplest form for applying Q-learning in multi-agent scenarios, where all the femtocells learn independently. The latter is the proposed scheme in which femtocells share partial information during the learning process in order to strike a balance between practical relevance and performance. In terms of performance, the simulation results showed that the CL paradigm outperforms the IL paradigm and achieves an aggregate femtocells capacity that is very close to the optimal one. For the practical relevance issue, we evaluate the robustness and scalability of SBDPC-Q, in real time, by deploying new femtocells in the system during the learning process, where we showed that SBDPC-Q in the CL paradigm is scalable to large number of femtocells and more robust to the network dynamics compared to the IL paradigm. Copyright 2013 by the Institute of Electrical and Electronic Engineers, Inc.
راعي المشروعQatar National Research Fund
اللغةen
الناشرIEEE
الموضوعDistributed interference managements
Distributed power control
Femtocell Networks
Learning paradigms
Multi-agent Q-learning
Partial information
Q-learning algorithms
Q-learning approach
Learning algorithms
Learning systems
Multi agent systems
Femtocell
العنوانA cooperative Q-learning approach for online power allocation in femtocell networks
النوعConference Paper
dc.accessType Abstract Only


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