300 research outputs found
On the Interplay Between Edge Caching and HARQ in Fog-RAN
In a Fog Radio Access Network (Fog-RAN), edge caching is combined with
cloud-aided transmission in order to compensate for the limited hit probability
of the caches at the base stations (BSs). Unlike the typical wired scenarios
studied in the networking literature in which entire files are typically
cached, recent research has suggested that fractional caching at the BSs of a
wireless system can be beneficial. This paper investigates the benefits of
fractional caching in a scenario with a cloud processor connected via a
wireless fronthaul link to a BS, which serves a number of mobile users on a
wireless downlink channel using orthogonal spectral resources. The fronthaul
and downlink channels occupy orthogonal frequency bands. The end-to-end
delivery latency for given requests of the users depends on the HARQ processes
run on the two links to counteract fading-induced outages. An analytical
framework based on theory of Markov chains with rewards is provided that
enables the optimization of fractional edge caching at the BSs. Numerical
results demonstrate meaningful advantages for fractional caching due to the
interplay between caching and HARQ transmission. The gains are observed in the
typical case in which the performance is limited by the wireless downlink
channel and the file popularity distribution is not too skewed
Online Reinforcement Learning of X-Haul Content Delivery Mode in Fog Radio Access Networks
We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in
the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The
system aims at delivering contents on demand with minimal average latency from
a time-varying library of popular contents. Information about uncached
requested files can be transferred from the cloud to the eRRHs by following
either backhaul or fronthaul modes. The backhaul mode transfers fractions of
the requested files, while the fronthaul mode transmits quantized baseband
samples as in Cloud-RAN (C-RAN). The backhaul mode allows the caches of the
eRRHs to be updated, which may lower future delivery latencies. In contrast,
the fronthaul mode enables cooperative C-RAN transmissions that may reduce the
current delivery latency. Taking into account the trade-off between current and
future delivery performance, this paper proposes an adaptive selection method
between the two delivery modes to minimize the long-term delivery latency.
Assuming an unknown and time-varying popularity model, the method is based on
model-free Reinforcement Learning (RL). Numerical results confirm the
effectiveness of the proposed RL scheme.Comment: 5 pages, 2 figure
A review on green caching strategies for next generation communication networks
© 2020 IEEE. In recent years, the ever-increasing demand for networking resources and energy, fueled by the unprecedented upsurge in Internet traffic, has been a cause for concern for many service providers. Content caching, which serves user requests locally, is deemed to be an enabling technology in addressing the challenges offered by the phenomenal growth in Internet traffic. Conventionally, content caching is considered as a viable solution to alleviate the backhaul pressure. However, recently, many studies have reported energy cost reductions contributed by content caching in cache-equipped networks. The hypothesis is that caching shortens content delivery distance and eventually achieves significant reduction in transmission energy consumption. This has motivated us to conduct this study and in this article, a comprehensive survey of the state-of-the-art green caching techniques is provided. This review paper extensively discusses contributions of the existing studies on green caching. In addition, the study explores different cache-equipped network types, solution methods, and application scenarios. We categorically present that the optimal selection of the caching nodes, smart resource management, popular content selection, and renewable energy integration can substantially improve energy efficiency of the cache-equipped systems. In addition, based on the comprehensive analysis, we also highlight some potential research ideas relevant to green content caching
Online edge caching and wireless delivery in fog-aided networks
Multimedia content is the significant fraction of transferred data over the wireless medium in the modern cellular and wireless communication networks. To improve the quality of experience perceived by users, one promising solution is to push the most popular contents as close as to users, also known as the edge of network. Storing content at the edge nodes (ENs) or base stations (BSs) is called caching . In Fog Radio Access Network (F-RAN), each EN is equipped with a cache as well as a fronthaul connection to the content server. Among the new design problems raised by the outlined scenarios, two key issues are addressed in this dissertation: 1) How to utilize cache and fronthaul resources while taking into account the wireless channel impairments; 2) How to incorporate the time-variability of popular set in the performance evaluation of F-RAN. These aspects are investigated by using information-theoretic models, obtaining fundamental insights that have been corroborated by various illustrative examples. To address point 1), two scenarios are investigated. First, a single-cell scenario with two transmitters is considered. A fog-aided small-cell BS as one of the transmitters and a cloud-aided macro-cell BS as the second transmitter collaborate with each other to send the requested content over a partially connected wireless channel. The intended and interference channels are modeled by erasure channels. Assuming a static set of popular contents, offline caching maps the library of files to cached contents stored at small-cell BS such that the cache capacity requirement is met. The delivery time per bit (DTB) is adopted as a measure of the coding latency, that is, the duration of the transmission block, required for reliable delivery. It is proved that optimal DTB is a linear decreasing function of cache capacity as well as inversely proportional with capacity of fronthaul link. In the second scenario, the same single-cell model is used with the only caveat that the set of popular files is time-varying. In this case, online caching maps the library of files to cached contents at small-cell BS. Thanks to availability of popular set at macro-BS, the DTB is finite and has upper and lower bounds which are functions of system resources i.e., cache and fronthaul link capacities. As for point 2), the model is comprised of an arbitrary number of ENs and users connected through an interference-limited wireless channel at high-SNR regime. All equally important ENs are benefited from cache capacity as well as fronthaul connection to the content server. The time-variability of popular set necessitates online caching to enable ENs keep track of changes in the popular set. The analysis is centered on the characterization of the long-term Normalized Delivery Time (NDT), which captures the temporal dependence of the coding latencies accrued across multiple time slots in the high-SNR regime. Online edge caching and delivery schemes based on reactive and proactive caching principles are investigated for both serial and pipelined transmission modes across fronthaul and edge segments. The outcome of analytical results provides a controversial view of contemporary research on the edge caching. It is proved that with a time-varying set of popular files, the capacity of fronthaul link between ENs and content server set a fundamental limit on the system performance. This is due to the fact that the original information source is content server and the only way to retrieve information is via fronthaul links. While edge caching can provide some gains in term of reduced latency, the gain diminishes as a result of the fact that the cached content is prone to be outdated with time-varying popularity
Online Reinforcement Learning of X-Haul Content Delivery Mode in Fog Radio Access Networks
We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in
the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The
system aims at delivering contents on demand with minimal average latency from
a time-varying library of popular contents. Information about uncached
requested files can be transferred from the cloud to the eRRHs by following
either backhaul or fronthaul modes. The backhaul mode transfers fractions of
the requested files, while the fronthaul mode transmits quantized baseband
samples as in Cloud-RAN (C-RAN). The backhaul mode allows the caches of the
eRRHs to be updated, which may lower future delivery latencies. In contrast,
the fronthaul mode enables cooperative C-RAN transmissions that may reduce the
current delivery latency. Taking into account the trade-off between current and
future delivery performance, this paper proposes an adaptive selection method
between the two delivery modes to minimize the long-term delivery latency.
Assuming an unknown and time-varying popularity model, the method is based on
model-free Reinforcement Learning (RL). Numerical results confirm the
effectiveness of the proposed RL scheme.Comment: 12 pages, 2 figure
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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