6,071 research outputs found
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
Energy-aware resource allocation in next generation wireless networks : application in large-scale MIMO Systems
In this thesis, we investigate the resource allocation problem for wireless networks that incorporate large-scale multiple-input multiple-output (MIMO) systems. These systems are considered as key technologies for future 5G wireless networks and are based on using few hundreds of antennas simultaneously to serve tens of users in the same time-frequency resource. The gains obtained by large-scale MIMO systems cannot be fully exploited without adequate resource allocation strategies. Hence, the aim of this thesis is to develop energy-aware resource allocation solutions for large-scale MIMO systems that take into consideration network power cost.
Firstly, this thesis investigates the downlink of a base station equipped with large-scale MIMO system while taking into account a non-negligible transmit circuit power consumption. This consumption involves that activating all RF chains does not always necessarily achieve the maximum sum-rate. Thus, we derive the optimal number of activated RF chains. In addition, efficient antenna selection, user scheduling and power allocation algorithms in term of instantaneous sum-rate are proposed and compared. Also, fairness is investigated by considering equal receive power among users.
Secondly, this thesis investigates a large-scale MIMO system that incorporates energy harvesting that is a promising key technology for greening future wireless networks since it reduces network operation costs and carbon footprints. Hence, we consider distributed large-scale MIMO systems made up of a set of remote radio heads (RRHs), each of which is powered by both an independent energy harvesting source and the grid. The grid energy source allows to compensate for the randomness and intermittency of the harvested energy. Optimal on-line and off-line energy management strategies are developed. In addition, on-line energy management algorithm based on energy prediction is devised. The feasibility problem is addressed by proposing an efficient link removal algorithm and for better energy efficiency, RRH on/off operation is investigated.
Thirdly, wireless backhauling was proposed as an alternative solution that enable low-cost connection between the small base stations and the macro base station in heterogeneous networks (HetNets). The coexistence of massive MIMO, HetNets and wireless backhauling is a promising research direction since massive MIMO is a suitable solution to enable wireless backhauling. Thus, we propose a new transmission technique that is able to efficiently manage the interference in heterogeneous networks with massive MIMO wireless backhaul. The optimal time splitting parameter and the allocated transmit power are derived. The proposed transmission technique is shown to be more efficient in terms of transmit power consumption than the conventional reverse time division duplex with bandwidth splitting.
In this thesis, we developed efficient resource allocation solutions related to system power for wireless networks that incorporate large-scale MIMO systems under different assumptions and network architectures. The results in this thesis can be expanded by investigating the research problems given at the end of the dissertation
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
Employing Antenna Selection to Improve Energy-Efficiency in Massive MIMO Systems
Massive MIMO systems promise high data rates by employing large number of
antennas, which also increases the power usage of the system as a consequence.
This creates an optimization problem which specifies how many antennas the
system should employ in order to operate with maximal energy efficiency. Our
main goal is to consider a base station with a fixed number of antennas, such
that the system can operate with a smaller subset of antennas according to the
number of active user terminals, which may vary over time. Thus, in this paper
we propose an antenna selection algorithm which selects the best antennas
according to the better channel conditions with respect to the users, aiming at
improving the overall energy efficiency. Then, due to the complexity of the
mathematical formulation, a tight approximation for the consumed power is
presented, using the Wishart theorem, and it is used to find a deterministic
formulation for the energy efficiency. Simulation results show that the
approximation is quite tight and that there is significant improvement in terms
of energy efficiency when antenna selection is employed.Comment: To appear in Transactions on Emerging Telecommunications
Technologies, 12 pages, 8 figures, 2 table
Multipair Full-Duplex Relaying with Massive Arrays and Linear Processing
We consider a multipair decode-and-forward relay channel, where multiple
sources transmit simultaneously their signals to multiple destinations with the
help of a full-duplex relay station. We assume that the relay station is
equipped with massive arrays, while all sources and destinations have a single
antenna. The relay station uses channel estimates obtained from received pilots
and zero-forcing (ZF) or maximum-ratio combining/maximum-ratio transmission
(MRC/MRT) to process the signals. To reduce significantly the loop interference
effect, we propose two techniques: i) using a massive receive antenna array; or
ii) using a massive transmit antenna array together with very low transmit
power at the relay station. We derive an exact achievable rate in closed-form
for MRC/MRT processing and an analytical approximation of the achievable rate
for ZF processing. This approximation is very tight, especially for large
number of relay station antennas. These closed-form expressions enable us to
determine the regions where the full-duplex mode outperforms the half-duplex
mode, as well as, to design an optimal power allocation scheme. This optimal
power allocation scheme aims to maximize the energy efficiency for a given sum
spectral efficiency and under peak power constraints at the relay station and
sources. Numerical results verify the effectiveness of the optimal power
allocation scheme. Furthermore, we show that, by doubling the number of
transmit/receive antennas at the relay station, the transmit power of each
source and of the relay station can be reduced by 1.5dB if the pilot power is
equal to the signal power, and by 3dB if the pilot power is kept fixed, while
maintaining a given quality-of-service
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