2,651 research outputs found
Echo State Transfer Learning for Data Correlation Aware Resource Allocation in Wireless Virtual Reality
In this paper, the problem of data correlation-aware resource management is
studied for a network of wireless virtual reality (VR) users communicating over
cloud-based small cell networks (SCNs). In the studied model, small base
stations (SBSs) with limited computational resources act as VR control centers
that collect the tracking information from VR users over the cellular uplink
and send them to the VR users over the downlink. In such a setting, VR users
may send or request correlated or similar data (panoramic images and tracking
data). This potential spatial data correlation can be factored into the
resource allocation problem to reduce the traffic load in both uplink and
downlink. This VR resource allocation problem is formulated as a noncooperative
game that allows jointly optimizing the computational and spectrum resources,
while being cognizant of the data correlation. To solve this game, a transfer
learning algorithm based on the machine learning framework of echo state
networks (ESNs) is proposed. Unlike conventional reinforcement learning
algorithms that must be executed each time the environment changes, the
proposed algorithm can intelligently transfer information on the learned
utility, across time, to rapidly adapt to environmental dynamics due to factors
such as changes in the users' content or data correlation. Simulation results
show that the proposed algorithm achieves up to 16.7% and 18.2% gains in terms
of delay compared to the Q-learning with data correlation and Q-learning
without data correlation. The results also show that the proposed algorithm has
a faster convergence time than Q-learning and can guarantee low delays.Comment: This paper has been accepted by Asiloma
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
Self-Evolving Integrated Vertical Heterogeneous Networks
6G and beyond networks tend towards fully intelligent and adaptive design in
order to provide better operational agility in maintaining universal wireless
access and supporting a wide range of services and use cases while dealing with
network complexity efficiently. Such enhanced network agility will require
developing a self-evolving capability in designing both the network
architecture and resource management to intelligently utilize resources, reduce
operational costs, and achieve the coveted quality of service (QoS). To enable
this capability, the necessity of considering an integrated vertical
heterogeneous network (VHetNet) architecture appears to be inevitable due to
its high inherent agility. Moreover, employing an intelligent framework is
another crucial requirement for self-evolving networks to deal with real-time
network optimization problems. Hence, in this work, to provide a better insight
on network architecture design in support of self-evolving networks, we
highlight the merits of integrated VHetNet architecture while proposing an
intelligent framework for self-evolving integrated vertical heterogeneous
networks (SEI-VHetNets). The impact of the challenges associated with
SEI-VHetNet architecture, on network management is also studied considering a
generalized network model. Furthermore, the current literature on network
management of integrated VHetNets along with the recent advancements in
artificial intelligence (AI)/machine learning (ML) solutions are discussed.
Accordingly, the core challenges of integrating AI/ML in SEI-VHetNets are
identified. Finally, the potential future research directions for advancing the
autonomous and self-evolving capabilities of SEI-VHetNets are discussed.Comment: 25 pages, 5 figures, 2 table
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