3,448 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
Optimization of Massive Full-Dimensional MIMO for Positioning and Communication
Massive Full-Dimensional multiple-input multiple-output (FD-MIMO) base
stations (BSs) have the potential to bring multiplexing and coverage gains by
means of three-dimensional (3D) beamforming. Key technical challenges for their
deployment include the presence of limited-resolution front ends and the
acquisition of channel state information (CSI) at the BSs. This paper
investigates the use of FD-MIMO BSs to provide simultaneously high-rate data
communication and mobile 3D positioning in the downlink. The analysis
concentrates on the problem of beamforming design by accounting for imperfect
CSI acquisition via Time Division Duplex (TDD)-based training and for the
finite resolution of analog-to-digital converter (ADC) and digital-to-analog
converter (DAC) at the BSs. Both \textit{unstructured beamforming} and a
low-complexity \textit{Kronecker beamforming} solution are considered, where
for the latter the beamforming vectors are decomposed into separate azimuth and
elevation components. The proposed algorithmic solutions are based on Bussgang
theorem, rank-relaxation and successive convex approximation (SCA) methods.
Comprehensive numerical results demonstrate that the proposed schemes can
effectively cater to both data communication and positioning services,
providing only minor performance degradations as compared to the more
conventional cases in which either function is implemented. Moreover, the
proposed low-complexity Kronecker beamforming solutions are seen to guarantee a
limited performance loss in the presence of a large number of BS antennas.Comment: 30 pages, 6 figure
Distributed Massive MIMO in Cellular Networks: Impact of Imperfect Hardware and Number of Oscillators
Distributed massive multiple-input multiple-output (MIMO) combines the array
gain of coherent MIMO processing with the proximity gains of distributed
antenna setups. In this paper, we analyze how transceiver hardware impairments
affect the downlink with maximum ratio transmission. We derive closed-form
spectral efficiencies expressions and study their asymptotic behavior as the
number of the antennas increases. We prove a scaling law on the hardware
quality, which reveals that massive MIMO is resilient to additive distortions,
while multiplicative phase noise is a limiting factor. It is also better to
have separate oscillators at each antenna than one per BS.Comment: First published in the Proceedings of the 23rd European Signal
Processing Conference (EUSIPCO-2015) in 2015, published by EURASIP. 5 pages,
3, figure
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
Joint Design of Digital and Analog Processing for Downlink C-RAN with Large-Scale Antenna Arrays
In millimeter-wave communication systems with large-scale antenna arrays,
conventional digital beamforming may not be cost-effective. A promising
solution is the implementation of hybrid beamforming techniques, which consist
of low-dimensional digital beamforming followed by analog radio frequency (RF)
beamforming. This work studies the optimization of hybrid beamforming in the
context of a cloud radio access network (C-RAN) architecture. In a C-RAN
system, digital baseband signal processing functionalities are migrated from
remote radio heads (RRHs) to a baseband processing unit (BBU) in the "cloud" by
means of finite-capacity fronthaul links. Specifically, this work tackles the
problem of jointly optimizing digital beamforming and fronthaul quantization
strategies at the BBU, as well as RF beamforming at the RRHs, with the goal of
maximizing the weighted downlink sum-rate. Fronthaul capacity and per-RRH power
constraints are enforced along with constant modulus constraints on the RF
beamforming matrices. An iterative algorithm is proposed that is based on
successive convex approximation and on the relaxation of the constant modulus
constraint. The effectiveness of the proposed scheme is validated by numerical
simulation results
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