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These complex contradicting objectives are embedded in our reinforcement learning (RL) model that is developed and implemented as shown in this paper. The objective function is defined as the net revenue gained by PUs from renting some of their spectrum. RL is used to extract the optimal control policy that maximizes the PUs\u2019 profit continuously over time. The extracted policy is used by PUs to manage renting the spectrum to SUs and it helps PUs to adapt to the changing network conditions. Performance evaluation of the proposed spectrum trading approach shows that it is able to find the optimal size and price of spectrum for each primary user under different conditions. Moreover, the approach constitutes a framework for studying, synthesizing and optimizing other schemes. Another contribution is proposing a new distributed algorithm to manage spectrum sharing among PUs. In our scheme, PUs exchange channels dynamically based on the availability of neighbor\u2019s idle channels. In our cooperative scheme, the objective of spectrum sharing is to maximize the total revenue and utilize spectrum efficiently. Compared to the poverty\u2010line heuristic that does not consider the availability of unused spectrum, our scheme has the advantage of utilizing spectrum efficiently.<\/jats:p>","DOI":"10.1155\/2012\/562615","type":"journal-article","created":{"date-parts":[[2012,8,22]],"date-time":"2012-08-22T21:10:16Z","timestamp":1345669816000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Optimizing Spectrum Trading in Cognitive Mesh Network Using Machine Learning"],"prefix":"10.1155","volume":"2012","author":[{"given":"Ayoub","family":"Alsarhan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anjali","family":"Agarwal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2012,8,22]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2006.05.001"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-68832-9"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2004.12.001"},{"volume-title":"Reinforcement Learning: An Introduction","year":"1998","author":"Sutton R. 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