2,217 research outputs found
Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks with Mobile Users
In this paper, the problem of proactive caching is studied for cloud radio
access networks (CRANs). In the studied model, the baseband units (BBUs) can
predict the content request distribution and mobility pattern of each user,
determine which content to cache at remote radio heads and BBUs. This problem
is formulated as an optimization problem which jointly incorporates backhaul
and fronthaul loads and content caching. To solve this problem, an algorithm
that combines the machine learning framework of echo state networks with
sublinear algorithms is proposed. Using echo state networks (ESNs), the BBUs
can predict each user's content request distribution and mobility pattern while
having only limited information on the network's and user's state. In order to
predict each user's periodic mobility pattern with minimal complexity, the
memory capacity of the corresponding ESN is derived for a periodic input. This
memory capacity is shown to be able to record the maximum amount of user
information for the proposed ESN model. Then, a sublinear algorithm is proposed
to determine which content to cache while using limited content request
distribution samples. Simulation results using real data from Youku and the
Beijing University of Posts and Telecommunications show that the proposed
approach yields significant gains, in terms of sum effective capacity, that
reach up to 27.8% and 30.7%, respectively, compared to random caching with
clustering and random caching without clustering algorithm.Comment: Accepted in the IEEE Transactions on Wireless Communication
The edge cloud: A holistic view of communication, computation and caching
The evolution of communication networks shows a clear shift of focus from
just improving the communications aspects to enabling new important services,
from Industry 4.0 to automated driving, virtual/augmented reality, Internet of
Things (IoT), and so on. This trend is evident in the roadmap planned for the
deployment of the fifth generation (5G) communication networks. This ambitious
goal requires a paradigm shift towards a vision that looks at communication,
computation and caching (3C) resources as three components of a single holistic
system. The further step is to bring these 3C resources closer to the mobile
user, at the edge of the network, to enable very low latency and high
reliability services. The scope of this chapter is to show that signal
processing techniques can play a key role in this new vision. In particular, we
motivate the joint optimization of 3C resources. Then we show how graph-based
representations can play a key role in building effective learning methods and
devising innovative resource allocation techniques.Comment: to appear in the book "Cooperative and Graph Signal Pocessing:
Principles and Applications", P. Djuric and C. Richard Eds., Academic Press,
Elsevier, 201
Fundamental Limits of Cloud and Cache-Aided Interference Management with Multi-Antenna Edge Nodes
In fog-aided cellular systems, content delivery latency can be minimized by
jointly optimizing edge caching and transmission strategies. In order to
account for the cache capacity limitations at the Edge Nodes (ENs),
transmission generally involves both fronthaul transfer from a cloud processor
with access to the content library to the ENs, as well as wireless delivery
from the ENs to the users. In this paper, the resulting problem is studied from
an information-theoretic viewpoint by making the following practically relevant
assumptions: 1) the ENs have multiple antennas; 2) only uncoded fractional
caching is allowed; 3) the fronthaul links are used to send fractions of
contents; and 4) the ENs are constrained to use one-shot linear precoding on
the wireless channel. Assuming offline proactive caching and focusing on a high
signal-to-noise ratio (SNR) latency metric, the optimal information-theoretic
performance is investigated under both serial and pipelined fronthaul-edge
transmission modes. The analysis characterizes the minimum high-SNR latency in
terms of Normalized Delivery Time (NDT) for worst-case users' demands. The
characterization is exact for a subset of system parameters, and is generally
optimal within a multiplicative factor of 3/2 for the serial case and of 2 for
the pipelined case. The results bring insights into the optimal interplay
between edge and cloud processing in fog-aided wireless networks as a function
of system resources, including the number of antennas at the ENs, the ENs'
cache capacity and the fronthaul capacity.Comment: 34 pages, 15 figures, submitte
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