1,699 research outputs found
Edge Caching in Dense Heterogeneous Cellular Networks with Massive MIMO Aided Self-backhaul
This paper focuses on edge caching in dense heterogeneous cellular networks
(HetNets), in which small base stations (SBSs) with limited cache size store
the popular contents, and massive multiple-input multiple-output (MIMO) aided
macro base stations provide wireless self-backhaul when SBSs require the
non-cached contents. Our aim is to address the effects of cell load and hit
probability on the successful content delivery (SCD), and present the minimum
required base station density for avoiding the access overload in an arbitrary
small cell and backhaul overload in an arbitrary macrocell. The massive MIMO
backhaul achievable rate without downlink channel estimation is derived to
calculate the backhaul time, and the latency is also evaluated in such
networks. The analytical results confirm that hit probability needs to be
appropriately selected, in order to achieve SCD. The interplay between cache
size and SCD is explicitly quantified. It is theoretically demonstrated that
when non-cached contents are requested, the average delay of the non-cached
content delivery could be comparable to the cached content delivery with the
help of massive MIMO aided self-backhaul, if the average access rate of cached
content delivery is lower than that of self-backhauled content delivery.
Simulation results are presented to validate our analysis.Comment: Accepted to appear in IEEE Transactions on Wireless Communication
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
How Much Can D2D Communication Reduce Content Delivery Latency in Fog Networks with Edge Caching?
A Fog-Radio Access Network (F-RAN) is studied in which cache-enabled Edge
Nodes (ENs) with dedicated fronthaul connections to the cloud aim at delivering
contents to mobile users. Using an information-theoretic approach, this work
tackles the problem of quantifying the potential latency reduction that can be
obtained by enabling Device-to-Device (D2D) communication over out-of-band
broadcast links. Following prior work, the Normalized Delivery Time (NDT) --- a
metric that captures the high signal-to-noise ratio worst-case latency --- is
adopted as the performance criterion of interest. Joint edge caching, downlink
transmission, and D2D communication policies based on compress-and-forward are
proposed that are shown to be information-theoretically optimal to within a
constant multiplicative factor of two for all values of the problem parameters,
and to achieve the minimum NDT for a number of special cases. The analysis
provides insights on the role of D2D cooperation in improving the delivery
latency.Comment: Submitted to the IEEE Transactions on Communication
Cloud-Edge Non-Orthogonal Transmission for Fog Networks with Delayed CSI at the Cloud
In a Fog Radio Access Network (F-RAN), the cloud processor (CP) collects
channel state information (CSI) from the edge nodes (ENs) over fronthaul links.
As a result, the CSI at the cloud is generally affected by an error due to
outdating. In this work, the problem of content delivery based on fronthaul
transmission and edge caching is studied from an information-theoretic
perspective in the high signal-to-noise ratio (SNR) regime. For the set-up
under study, under the assumption of perfect CSI, prior work has shown the
(approximate or exact) optimality of a scheme in which the ENs transmit
information received from the cloud and cached contents over orthogonal
resources. In this work, it is demonstrated that a non-orthogonal transmission
scheme is able to substantially improve the latency performance in the presence
of imperfect CSI at the cloud.Comment: 5 pages, 4 figures, submitte
A New Look at Physical Layer Security, Caching, and Wireless Energy Harvesting for Heterogeneous Ultra-dense Networks
Heterogeneous ultra-dense networks enable ultra-high data rates and ultra-low
latency through the use of dense sub-6 GHz and millimeter wave (mmWave) small
cells with different antenna configurations. Existing work has widely studied
spectral and energy efficiency in such networks and shown that high spectral
and energy efficiency can be achieved. This article investigates the benefits
of heterogeneous ultra-dense network architecture from the perspectives of
three promising technologies, i.e., physical layer security, caching, and
wireless energy harvesting, and provides enthusiastic outlook towards
application of these technologies in heterogeneous ultra-dense networks. Based
on the rationale of each technology, opportunities and challenges are
identified to advance the research in this emerging network.Comment: Accepted to appear in IEEE Communications Magazin
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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