1,058 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
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
Resource allocation for transmit hybrid beamforming in decoupled millimeter wave multiuser-MIMO downlink
This paper presents a study on joint radio resource allocation and hybrid precoding in multicarrier massive multiple-input multiple-output communications for 5G cellular networks. In this paper, we present the resource allocation algorithm to maximize the proportional fairness (PF) spectral efficiency under the per subchannel power and the beamforming rank constraints. Two heuristic algorithms are designed. The proportional fairness hybrid beamforming algorithm provides the transmit precoder with a proportional fair spectral efficiency among users for the desired number of radio-frequency (RF) chains. Then, we transform the number of RF chains or rank constrained optimization problem into convex semidefinite programming (SDP) problem, which can be solved by standard techniques. Inspired by the formulated convex SDP problem, a low-complexity, two-step, PF-relaxed optimization algorithm has been provided for the formulated convex optimization problem. Simulation results show that the proposed suboptimal solution to the relaxed optimization problem is near-optimal for the signal-to-noise ratio SNR <= 10 dB and has a performance gap not greater than 2.33 b/s/Hz within the SNR range 0-25 dB. It also outperforms the maximum throughput and PF-based hybrid beamforming schemes for sum spectral efficiency, individual spectral efficiency, and fairness index
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
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