تقرير
Deep Q-Learning Based Resource Allocation in Interference Systems With Outage Constraint
| العنوان: | Deep Q-Learning Based Resource Allocation in Interference Systems With Outage Constraint |
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| المؤلفون: | Alam, Saniul, Islam, Sadia, Khandaker, Muhammad R. A., Khan, Risala T., Tariq, Faisal, Toding, Apriana |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Networking and Internet Architecture |
| الوصف: | This correspondence considers the resource allocation problem in wireless interference channel (IC) under link outage constraints. Since the optimization problem is non-convex in nature, existing approaches to find the optimal power allocation are computationally intensive and thus practically infeasible. Recently, deep reinforcement learning has shown promising outcome in solving non-convex optimization problems with reduced complexity. In this correspondence, we utilize a deep Q-learning (DQL) approach which interacts with the wireless environment and learns the optimal power allocation of a wireless IC while maximizing overall sum-rate of the system and maintaining reliability requirement of each link. We have used two separate deep Q-networks to remove the inherent instability in learning process. Simulation results demonstrate that the proposed DQL approach outperforms existing geometric programming based solution. Comment: Submitted to IEEE TVT |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2203.02791 |
| رقم الأكسشن: | edsarx.2203.02791 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |