عرض بسيط للتسجيلة

المؤلفWang, Dan
المؤلفZhang, Wei
المؤلفSong, Bin
المؤلفDu, Xiaojiang
المؤلفGuizani, Mohsen
تاريخ الإتاحة2020-08-18T08:34:45Z
تاريخ النشر2019
اسم المنشورIEEE Transactions on Cognitive Communications and Networking
المصدرScopus
الرقم المعياري الدولي للكتاب23327731
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/TCCN.2019.2950242
معرّف المصادر الموحدhttp://hdl.handle.net/10576/15669
الملخصThe ever-increasing urban population and the corresponding material demands have brought unprecedented burdens to cities. To guarantee better QoS for citizens, smart cities leverage emerging technologies such as the Cognitive Radio Internet of Things (CR-IoT). However, resource allocation is a great challenge for CR-IoT, mainly because of the extremely numerous devices and users. Generally, the auction theory and game theory are applied to overcome the challenge. In this paper, we propose a multi-agent reinforcement learning (MARL) algorithm to learn the optimal resource allocation strategy in the oligopoly market model. Firstly, we model a multi-agent scenario with the primary users (PUs) as sellers and secondary users (SUs) as buyers. Then, we propose the Q-probabilistic multi-agent learning (QPML) and apply it to allocate resources in the market. In the multi-agent learning process, the PUs and SUs learn strategies to maximize their benefits and improve spectrum utilization. The performance of QPML is compared with Learning Automation (LA) through simulations. The experimental results show that our approach outperforms other approaches and performs well. IEEE
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعCR-IoT
market model.
MARL
resource allocation
العنوانMarket-Based Model in CR-IoT: A QProbabilistic Multi-agent Reinforcement Learning Approach
النوعArticle


الملفات في هذه التسجيلة

الملفاتالحجمالصيغةالعرض

لا توجد ملفات لها صلة بهذه التسجيلة.

هذه التسجيلة تظهر في المجموعات التالية

عرض بسيط للتسجيلة