Title
Double Coded Caching in Ultra Dense Networks: Caching and Multicast Scheduling via Deep Reinforcement Learning
Abstract
Proposed by Maddah-Ali and Niesen, a coded caching scheme has been verified to alleviate the load of networks efficiently. Recently, a new technique called placement delivery array (PDA) was proposed to characterize the coded caching scheme. In this paper, we consider a caching system in the scope of ultra dense networks (UDNs). Each base station (BS) has a finite cache and stores some contents. We propose an efficient coded content caching scheme called double coded caching to make the transmission robust to in-and-out wireless network quality. Then the dynamic caching and multicast scheduling are considered to jointly minimize the average delay and power of the content-centric wireless networks. This stochastic optimization problem can be formulated as a Markov decision process (MDP) with unknown transition probabilities and large state space. We propose a deep reinforcement learning approach to deal with the decision problem. Our algorithm uses a variational auto-encoder (VAE) neural network to approximate the state sufficiently, and uses a weighted double Q-learning scheme to reduce variance and overestimation of the Q function. Numerical results demonstrate that the proposed double coded caching scheme increases the probability of the successful transmission, and the caching and scheduling policy can effectively reduce the delay and the power consumption.
Year
DOI
Venue
2020
10.1109/TCOMM.2019.2955490
IEEE Transactions on Communications
Keywords
Field
DocType
Servers,Reinforcement learning,Handheld computers,Dynamic scheduling,Base stations,Wireless networks
Multicast scheduling,Computer science,Computer network,Electronic engineering,Ultra dense,Reinforcement learning
Journal
Volume
Issue
ISSN
68
2
0090-6778
Citations 
PageRank 
References 
2
0.36
0
Authors
6
Name
Order
Citations
PageRank
Zhengming Zhang1102.14
Hongyang Chen257043.33
Meng Hua31228.93
Chunguo Li44810.72
Yongming Huang51472146.50
Luxi Yang61180118.08