2,164 research outputs found
Wireless Communications in the Era of Big Data
The rapidly growing wave of wireless data service is pushing against the
boundary of our communication network's processing power. The pervasive and
exponentially increasing data traffic present imminent challenges to all the
aspects of the wireless system design, such as spectrum efficiency, computing
capabilities and fronthaul/backhaul link capacity. In this article, we discuss
the challenges and opportunities in the design of scalable wireless systems to
embrace such a "bigdata" era. On one hand, we review the state-of-the-art
networking architectures and signal processing techniques adaptable for
managing the bigdata traffic in wireless networks. On the other hand, instead
of viewing mobile bigdata as a unwanted burden, we introduce methods to
capitalize from the vast data traffic, for building a bigdata-aware wireless
network with better wireless service quality and new mobile applications. We
highlight several promising future research directions for wireless
communications in the mobile bigdata era.Comment: This article is accepted and to appear in IEEE Communications
Magazin
A Survey of Deep Learning for Data Caching in Edge Network
The concept of edge caching provision in emerging 5G and beyond mobile
networks is a promising method to deal both with the traffic congestion problem
in the core network as well as reducing latency to access popular content. In
that respect end user demand for popular content can be satisfied by
proactively caching it at the network edge, i.e, at close proximity to the
users. In addition to model based caching schemes learning-based edge caching
optimizations has recently attracted significant attention and the aim
hereafter is to capture these recent advances for both model based and data
driven techniques in the area of proactive caching. This paper summarizes the
utilization of deep learning for data caching in edge network. We first outline
the typical research topics in content caching and formulate a taxonomy based
on network hierarchical structure. Then, a number of key types of deep learning
algorithms are presented, ranging from supervised learning to unsupervised
learning as well as reinforcement learning. Furthermore, a comparison of
state-of-the-art literature is provided from the aspects of caching topics and
deep learning methods. Finally, we discuss research challenges and future
directions of applying deep learning for cachin
A Deep Reinforcement Learning-Based Framework for Content Caching
Content caching at the edge nodes is a promising technique to reduce the data
traffic in next-generation wireless networks. Inspired by the success of Deep
Reinforcement Learning (DRL) in solving complicated control problems, this work
presents a DRL-based framework with Wolpertinger architecture for content
caching at the base station. The proposed framework is aimed at maximizing the
long-term cache hit rate, and it requires no knowledge of the content
popularity distribution. To evaluate the proposed framework, we compare the
performance with other caching algorithms, including Least Recently Used (LRU),
Least Frequently Used (LFU), and First-In First-Out (FIFO) caching strategies.
Meanwhile, since the Wolpertinger architecture can effectively limit the action
space size, we also compare the performance with Deep Q-Network to identify the
impact of dropping a portion of the actions. Our results show that the proposed
framework can achieve improved short-term cache hit rate and improved and
stable long-term cache hit rate in comparison with LRU, LFU, and FIFO schemes.
Additionally, the performance is shown to be competitive in comparison to Deep
Q-learning, while the proposed framework can provide significant savings in
runtime.Comment: 6 pages, 3 figure
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