893 research outputs found
Using Grouped Linear Prediction and Accelerated Reinforcement Learning for Online Content Caching
Proactive caching is an effective way to alleviate peak-hour traffic
congestion by prefetching popular contents at the wireless network edge. To
maximize the caching efficiency requires the knowledge of content popularity
profile, which however is often unavailable in advance. In this paper, we first
propose a new linear prediction model, named grouped linear model (GLM) to
estimate the future content requests based on historical data. Unlike many
existing works that assumed the static content popularity profile, our model
can adapt to the temporal variation of the content popularity in practical
systems due to the arrival of new contents and dynamics of user preference.
Based on the predicted content requests, we then propose a reinforcement
learning approach with model-free acceleration (RLMA) for online cache
replacement by taking into account both the cache hits and replacement cost.
This approach accelerates the learning process in non-stationary environment by
generating imaginary samples for Q-value updates. Numerical results based on
real-world traces show that the proposed prediction and learning based online
caching policy outperform all considered existing schemes.Comment: 6 pages, 4 figures, ICC 2018 worksho
A Feature-Based Bayesian Method for Content Popularity Prediction in Edge-Caching Networks
Edge-caching is recognized as an efficient technique for future wireless
cellular networks to improve network capacity and user-perceived quality of
experience. Due to the random content requests and the limited cache memory,
designing an efficient caching policy is a challenge. To enhance the
performance of caching systems, an accurate content request prediction
algorithm is essential. Here, we introduce a flexible model, a Poisson
regressor based on a Gaussian process, for the content request distribution in
stationary environments. Our proposed model can incorporate the content
features as side information for prediction enhancement. In order to learn the
model parameters, which yield the Poisson rates or alternatively content
popularities, we invoke the Bayesian approach which is very robust against
over-fitting.
However, the posterior distribution in the Bayes formula is analytically
intractable to compute. To tackle this issue, we apply a Monte Carlo Markov
Chain (MCMC) method to approximate the posterior distribution. Two types of
predictive distributions are formulated for the requests of existing contents
and for the requests of a newly-added content. Finally, simulation results are
provided to confirm the accuracy of the developed content popularity learning
approach.Comment: arXiv admin note: substantial text overlap with arXiv:1903.0306
A Bayesian Poisson-Gaussian Process Model for Popularity Learning in Edge-Caching Networks
Edge-caching is recognized as an efficient technique for future cellular
networks to improve network capacity and user-perceived quality of experience.
To enhance the performance of caching systems, designing an accurate content
request prediction algorithm plays an important role. In this paper, we develop
a flexible model, a Poisson regressor based on a Gaussian process, for the
content request distribution.
The first important advantage of the proposed model is that it encourages the
already existing or seen contents with similar features to be correlated in the
feature space and therefore it acts as a regularizer for the estimation.
Second, it allows to predict the popularities of newly-added or unseen contents
whose statistical data is not available in advance. In order to learn the model
parameters, which yield the Poisson arrival rates or alternatively the content
\textit{popularities}, we invoke the Bayesian approach which is robust against
over-fitting.
However, the resulting posterior distribution is analytically intractable to
compute. To tackle this, we apply a Markov Chain Monte Carlo (MCMC) method to
approximate this distribution which is also asymptotically exact. Nevertheless,
the MCMC is computationally demanding especially when the number of contents is
large. Thus, we employ the Variational Bayes (VB) method as an alternative low
complexity solution. More specifically, the VB method addresses the
approximation of the posterior distribution through an optimization problem.
Subsequently, we present a fast block-coordinate descent algorithm to solve
this optimization problem. Finally, extensive simulation results both on
synthetic and real-world datasets are provided to show the accuracy of our
prediction algorithm and the cache hit ratio (CHR) gain compared to existing
methods from the literature
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
Reinforcement learning for proactive content caching in wireless networks
Proactive content caching (PC) at the edge of wireless networks, that is, at the base stations (BSs) and/or user equipments (UEs), is a promising strategy to successfully handle the ever-growing mobile data traffic and to improve the quality-of-service for content delivery over wireless networks. However, factors such as limitations in storage capacity, time-variations in wireless channel conditions as well as in content demand profile pose challenges that need to be addressed in order to realise the benefits of PC
at the wireless edge.
This thesis aims to develop PC solutions that address these challenges. We consider PC directly at UEs equipped with finite capacity cache memories. This consideration is done within the framework of a dynamic system, where mobile users randomly request contents from a non-stationary content library; new contents are added to the library over time and each content may remain in the library for a random lifetime
within which it may be requested. Contents are delivered through wireless channels with time-varying quality, and any time contents are transmitted, a transmission cost associated with the number of bits downloaded and the channel quality of the receiving user(s) at that time is incurred by the system. We formulate each considered problem as a Markov decision process with the objective of minimising the long
term expected average cost on the system. We then use reinforcement learning (RL) to solve this highly challenging problem with a prohibitively large state and action spaces. In particular, we employ policy approximation techniques for compact representation of complex policy structures, and policy gradient RL methods to train the system. In a single-user problem setting that we consider, we show the optimality of a
threshold-based PC scheme that is adaptive to system dynamics. We use this result to characterise and design a multicast-aware PC scheme, based on deep RL framework, when we consider a multi-user problem setting. We perform extensive numerical simulations of the schemes we propose. Our results show not only significant improvements against the state-of-the-art reactive content delivery approaches, but also near-optimality of the proposed RL solutions based on comparisons with some lower bounds.Open Acces
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