57,135 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-Efficient Low-Complexity Algorithm in 5G Massive MIMO Systems
Energy efficiency (EE) is a critical design when taking into account
circuit power consumption (CPC) in fifth-generation cellular networks. These
problems arise because of the increasing number of antennas in massive
multiple-input multiple-output (MIMO) systems, attributable to inter-cell
interference for channel state information. Apart from that, a higher number
of radio frequency (RF) chains at the base station and active users consume
more power due to the processing activities in digital-to-analogue converters
and power amplifiers. Therefore, antenna selection, user selection, optimal
transmission power, and pilot reuse power are important aspects in improving
energy efficiency in massive MIMO systems. This work aims to investigate
joint antenna selection, optimal transmit power and joint user selection based
on deriving the closed-form of the maximal EE, with complete knowledge
of large-scale fading with maximum ratio transmission. It also accounts for
channel estimation and eliminating pilot contamination as antennasM→∞.
This formulates the optimization problem of joint optimal antenna selection,
transmits power allocation and joint user selection to mitigate inter-cellinterference
in downlink multi-cell massiveMIMO systems under minimized
reuse of pilot sequences based on a novel iterative low-complexity algorithm
(LCA) for Newton’s methods and Lagrange multipliers. To analyze the precise
power consumption, a novel power consumption scheme is proposed for
each individual antenna, based on the transmit power amplifier and CPC.
Simulation results demonstrate that the maximal EE was achieved using the
iterative LCA based on reasonable maximum transmit power, in the case the
noise power is less than the received power pilot. The maximum EE was
achieved with the desired maximum transmit power threshold by minimizing pilot reuse, in the case the transmit power allocation ρd = 40 dBm, and the
optimal EE=71.232 Mb/j
A Novel Antenna Selection Scheme for Spatially Correlated Massive MIMO Uplinks with Imperfect Channel Estimation
We propose a new antenna selection scheme for a massive MIMO system with a
single user terminal and a base station with a large number of antennas. We
consider a practical scenario where there is a realistic correlation among the
antennas and imperfect channel estimation at the receiver side. The proposed
scheme exploits the sparsity of the channel matrix for the effective selection
of a limited number of antennas. To this end, we compute a sparse channel
matrix by minimising the mean squared error. This optimisation problem is then
solved by the well-known orthogonal matching pursuit algorithm. Widely used
models for spatial correlation among the antennas and channel estimation errors
are considered in this work. Simulation results demonstrate that when the
impacts of spatial correlation and imperfect channel estimation introduced, the
proposed scheme in the paper can significantly reduce complexity of the
receiver, without degrading the system performance compared to the maximum
ratio combining.Comment: in Proc. IEEE 81st Vehicular Technology Conference (VTC), May 2015, 6
pages, 5 figure
Sum-rate Maximizing in Downlink Massive MIMO Systems with Circuit Power Consumption
The downlink of a single cell base station (BS) equipped with large-scale
multiple-input multiple-output (MIMO) system is investigated in this paper. As
the number of antennas at the base station becomes large, the power consumed at
the RF chains cannot be anymore neglected. So, a circuit power consumption
model is introduced in this work. It involves that the maximal sum-rate is not
obtained when activating all the available RF chains. Hence, the aim of this
work is to find the optimal number of activated RF chains that maximizes the
sum-rate. Computing the optimal number of activated RF chains must be
accompanied by an adequate antenna selection strategy. First, we derive
analytically the optimal number of RF chains to be activated so that the
average sum-rate is maximized under received equal power. Then, we propose an
efficient greedy algorithm to select the sub-optimal set of RF chains to be
activated with regards to the system sum-rate. It allows finding the balance
between the power consumed at the RF chains and the transmitted power. The
performance of the proposed algorithm is compared with the optimal performance
given by brute force search (BFS) antenna selection. Simulations allow to
compare the performance given by greedy, optimal and random antenna selection
algorithms.Comment: IEEE International Conference on Wireless and Mobile Computing,
Networking and Communications (WiMob 2015
Reduced Switching Connectivity for Large Scale Antenna Selection
In this paper, we explore reduced-connectivity radio frequency (RF) switching
networks for reducing the analog hardware complexity and switching power losses
in antenna selection (AS) systems. In particular, we analyze different hardware
architectures for implementing the RF switching matrices required in AS designs
with a reduced number of RF chains. We explicitly show that fully-flexible
switching matrices, which facilitate the selection of any possible subset of
antennas and attain the maximum theoretical sum rates of AS, present numerous
drawbacks such as the introduction of significant insertion losses,
particularly pronounced in massive multiple-input multiple-output (MIMO)
systems. Since these disadvantages make fully-flexible switching suboptimal in
the energy efficiency sense, we further consider partially-connected switching
networks as an alternative switching architecture with reduced hardware
complexity, which we characterize in this work. In this context, we also
analyze the impact of reduced switching connectivity on the analog hardware and
digital signal processing of AS schemes that rely on channel power information.
Overall, the analytical and simulation results shown in this paper demonstrate
that partially-connected switching maximizes the energy efficiency of massive
MIMO systems for a reduced number of RF chains, while fully-flexible switching
offers sub-optimal energy efficiency benefits due to its significant switching
power losses.Comment: 14 pages, 11 figure
Ubiquitous Cell-Free Massive MIMO Communications
Since the first cellular networks were trialled in the 1970s, we have
witnessed an incredible wireless revolution. From 1G to 4G, the massive traffic
growth has been managed by a combination of wider bandwidths, refined radio
interfaces, and network densification, namely increasing the number of antennas
per site. Due its cost-efficiency, the latter has contributed the most. Massive
MIMO (multiple-input multiple-output) is a key 5G technology that uses massive
antenna arrays to provide a very high beamforming gain and spatially
multiplexing of users, and hence, increases the spectral and energy efficiency.
It constitutes a centralized solution to densify a network, and its performance
is limited by the inter-cell interference inherent in its cell-centric design.
Conversely, ubiquitous cell-free Massive MIMO refers to a distributed Massive
MIMO system implementing coherent user-centric transmission to overcome the
inter-cell interference limitation in cellular networks and provide additional
macro-diversity. These features, combined with the system scalability inherent
in the Massive MIMO design, distinguishes ubiquitous cell-free Massive MIMO
from prior coordinated distributed wireless systems. In this article, we
investigate the enormous potential of this promising technology while
addressing practical deployment issues to deal with the increased
back/front-hauling overhead deriving from the signal co-processing.Comment: Published in EURASIP Journal on Wireless Communications and
Networking on August 5, 201
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