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Deep-Reinforcement-Learning-Based Optimization for Cache-Enabled Opportunistic Interference Alignment Wireless Networks
- Source :
- IEEE Transactions on Vehicular Technology. 66:10433-10445
- Publication Year :
- 2017
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2017.
-
Abstract
- Both caching and interference alignment (IA) are promising techniques for next-generation wireless networks. Nevertheless, most of the existing works on cache-enabled IA wireless networks assume that the channel is invariant, which is unrealistic considering the time-varying nature of practical wireless environments. In this paper, we consider realistic time-varying channels. Specifically, the channel is formulated as a finite-state Markov channel (FSMC). The complexity of the system is very high when we consider realistic FSMC models. Therefore, in this paper, we propose a novel deep reinforcement learning approach, which is an advanced reinforcement learning algorithm that uses a deep $Q$ network to approximate the $Q$ value-action function. We use Google TensorFlow to implement deep reinforcement learning in this paper to obtain the optimal IA user selection policy in cache-enabled opportunistic IA wireless networks. Simulation results are presented to show that the performance of cache-enabled opportunistic IA networks in terms of the network's sum rate and energy efficiency can be significantly improved by using the proposed approach.
- Subjects :
- Engineering
Computer Networks and Communications
business.industry
Wireless network
Distributed computing
Aerospace Engineering
020302 automobile design & engineering
020206 networking & telecommunications
02 engineering and technology
Invariant (physics)
Interference (wave propagation)
0203 mechanical engineering
Automotive Engineering
0202 electrical engineering, electronic engineering, information engineering
Wireless
Reinforcement learning
Cache
Electrical and Electronic Engineering
business
Computer network
Efficient energy use
Communication channel
Subjects
Details
- ISSN :
- 19399359 and 00189545
- Volume :
- 66
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Vehicular Technology
- Accession number :
- edsair.doi...........44b76bd23aecabd95b83f207b3f31377