142 research outputs found
Energy-Efficient Antenna Selection and Power Allocation for Large-Scale Multiple Antenna Systems with Hybrid Energy Supply
The combination of energy harvesting and large-scale multiple antenna
technologies provides a promising solution for improving the energy efficiency
(EE) by exploiting renewable energy sources and reducing the transmission power
per user and per antenna. However, the introduction of energy harvesting
capabilities into large-scale multiple antenna systems poses many new
challenges for energy-efficient system design due to the intermittent
characteristics of renewable energy sources and limited battery capacity.
Furthermore, the total manufacture cost and the sum power of a large number of
radio frequency (RF) chains can not be ignored, and it would be impractical to
use all the antennas for transmission. In this paper, we propose an
energy-efficient antenna selection and power allocation algorithm to maximize
the EE subject to the constraint of user's quality of service (QoS). An
iterative offline optimization algorithm is proposed to solve the non-convex EE
optimization problem by exploiting the properties of nonlinear fractional
programming. The relationships among maximum EE, selected antenna number,
battery capacity, and EE-SE tradeoff are analyzed and verified through computer
simulations.Comment: IEEE Globecom 2014 Selected Areas in Communications Symposium-Green
Communications and Computing Trac
Energy-Delay Tradeoffs of Virtual Base Stations With a Computational-Resource-Aware Energy Consumption Model
The next generation (5G) cellular network faces the challenges of efficiency,
flexibility, and sustainability to support data traffic in the mobile Internet
era. To tackle these challenges, cloud-based cellular architectures have been
proposed where virtual base stations (VBSs) play a key role. VBSs bring further
energy savings but also demands a new energy consumption model as well as the
optimization of computational resources. This paper studies the energy-delay
tradeoffs of VBSs with delay tolerant traffic. We propose a
computational-resource-aware energy consumption model to capture the total
energy consumption of a VBS and reflect the dynamic allocation of computational
resources including the number of CPU cores and the CPU speed. Based on the
model, we analyze the energy-delay tradeoffs of a VBS considering BS sleeping
and state switching cost to minimize the weighted sum of power consumption and
average delay. We derive the explicit form of the optimal data transmission
rate and find the condition under which the energy optimal rate exists and is
unique. Opportunities to reduce the average delay and achieve energy savings
simultaneously are observed. We further propose an efficient algorithm to
jointly optimize the data rate and the number of CPU cores. Numerical results
validate our theoretical analyses and under a typical simulation setting we
find more than 60% energy savings can be achieved by VBSs compared with
conventional base stations under the EARTH model, which demonstrates the great
potential of VBSs in 5G cellular systems.Comment: 5 pages, 3 figures, accepted by ICCS'1
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