29,901 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
Update or Wait: How to Keep Your Data Fresh
In this work, we study how to optimally manage the freshness of information
updates sent from a source node to a destination via a channel. A proper metric
for data freshness at the destination is the age-of-information, or simply age,
which is defined as how old the freshest received update is since the moment
that this update was generated at the source node (e.g., a sensor). A
reasonable update policy is the zero-wait policy, i.e., the source node submits
a fresh update once the previous update is delivered and the channel becomes
free, which achieves the maximum throughput and the minimum delay.
Surprisingly, this zero-wait policy does not always minimize the age. This
counter-intuitive phenomenon motivates us to study how to optimally control
information updates to keep the data fresh and to understand when the zero-wait
policy is optimal. We introduce a general age penalty function to characterize
the level of dissatisfaction on data staleness and formulate the average age
penalty minimization problem as a constrained semi-Markov decision problem
(SMDP) with an uncountable state space. We develop efficient algorithms to find
the optimal update policy among all causal policies, and establish sufficient
and necessary conditions for the optimality of the zero-wait policy. Our
investigation shows that the zero-wait policy is far from the optimum if (i)
the age penalty function grows quickly with respect to the age, (ii) the packet
transmission times over the channel are positively correlated over time, or
(iii) the packet transmission times are highly random (e.g., following a
heavy-tail distribution)
An Upper Bound to Zero-Delay Rate Distortion via Kalman Filtering for Vector Gaussian Sources
We deal with zero-delay source coding of a vector Gaussian autoregressive
(AR) source subject to an average mean squared error (MSE) fidelity criterion.
Toward this end, we consider the nonanticipative rate distortion function
(NRDF) which is a lower bound to the causal and zero-delay rate distortion
function (RDF). We use the realization scheme with feedback proposed in [1] to
model the corresponding optimal "test-channel" of the NRDF, when considering
vector Gaussian AR(1) sources subject to an average MSE distortion. We give
conditions on the vector Gaussian AR(1) source to ensure asymptotic
stationarity of the realization scheme (bounded performance). Then, we encode
the vector innovations due to Kalman filtering via lattice quantization with
subtractive dither and memoryless entropy coding. This coding scheme provides a
tight upper bound to the zero-delay Gaussian RDF. We extend this result to
vector Gaussian AR sources of any finite order. Further, we show that for
infinite dimensional vector Gaussian AR sources of any finite order, the NRDF
coincides with the zero-delay RDF. Our theoretical framework is corroborated
with a simulation example.Comment: 7 pages, 6 figures, accepted for publication in IEEE Information
Theory Workshop (ITW
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