Downlink MIMO in LTE-advanced: SU-MIMO vs. MU-MIMO

Liu, Lingjia; Chen, Runhua; Geirhofer, S.; Sayana, Krishna; Shi, Zhihua; Zhou, Yongxing

doi:10.1109/mcom.2012.6146493

Cited by 248 publications

(113 citation statements)

References 3 publications

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“…Moreover, it is well known that choosing a proper antenna technique can lead to either meeting the increased traffic demand, or reducing the power consumption [3]. Looking forward to improve spectral efficiency, the long term evolution (LTE) cellular network 4G standard [4] employs multiple antenna (MIMO) technologies in order to mitigate the effects of fading, providing diversity gains, or to increase the network capacity, providing multiplexing gains through spatial multiplexing (SM) techniques. However, these techniques also lead to a greater energy consumption as a result of the multiple radio frequency (RF) chains, specially due to the power amplifier consumption that corresponds to 55-60% of the total consumption in a BS [5].…”

Section: Introductionmentioning

confidence: 99%

On the Area Energy Efficiency of Multiple Transmit Antenna Small Base Stations

Krauss

Brante

Souza

et al. 2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

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Abstract-We analyze the area energy efficiency (AEE) of spatial multiplexing (SM) and transmit antenna selection (TAS), considering a realistic power consumption model for small base stations (BSs), which includes the power consumed by the backhaul as well as different interference attenuation levels. Our results show an optimum number of BSs for each technique that maximizes the AEE. Moreover, we also show that TAS has a larger AEE than SM when the demand for system capacity is low, while SM becomes more energy efficient when the demanded capacity is larger. Additionally, when the capacity demand and the area to be covered are fixed, the number of BSs needed to be deployed is smaller for SM than for the other techniques. Finally, the system performance in terms of AEE is shown to be strongly dependent on the amount of interference, which in turn depends on the employed interference-mitigation scheme, and on the employed power consumption model.

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Section: Introductionmentioning

confidence: 99%

On the Area Energy Efficiency of Multiple Transmit Antenna Small Base Stations

Krauss

Brante

Souza

et al. 2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

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show abstract

“…5: the exhaustive search method (denoted as 'Exhaus alg.') and the random 4 In fact, the long term evolution -Advanced (LTE-A) standards can support UEs with up to four receive antennas [44], and some field trials have been demonstrated to verify the feasibility of using four antennas at the UEs [44]. .…”

Section: A Properties Of the User Selection Algorithmmentioning

confidence: 99%

Joint Fronthaul Link Selection and Transmit Precoding for Energy Efficiency Maximization of Multiuser MIMO-Aided Distributed Antenna Systems

Ren

Liu

Pan

et al. 2017

IEEE Trans. Commun.

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Abstract-We jointly select the fronthaul links and optimize the transmit precoding matrices for maximizing the energy efficiency of a multiuser multiple-input multiple-output aided distributed antenna system . The fronthaul link's power consumption is taken into consideration, which is assumed to be proportional to the number of active fronthaul links quantified by using indicator functions. Both the rate requirements and the power constraints of the remote access units are considered. Under realistic power constraints some of the users cannot be admitted. Hence, we formulate a two-stage optimization problem. In Stage I, a novel user selection method is proposed for determining the maximum number of admitted users. In Stage II, we deal with the energy efficiency optimization problem. First, the indicator function is approximated by a smooth concave logarithmic function. Then, a triple-layer iterative algorithm is proposed for solving the approximated energy efficiency optimization problem, which is proved to converge to the Karush-Kuhn-Tucker conditions of the smoothened energy efficiency optimization problem. To further reduce the complexity, a single-layer iterative algorithm is conceived, which guarantees convergence. Our simulation results show that the proposed user selection algorithm approaches the performance of the exhaustive search method. Finally, the proposed algorithms is capable of achieving an order of magnitude higher energy efficiency than its conventional counterpart operating without considering link selection.

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“…In (9) C n is the covariance matrix for the interference plus noise terms in (8). The conventional MMSE receiver assumes that interference is uncorrelated across the receiving antennas.…”

Section: Interference Rejection Combining Receivermentioning

confidence: 99%

Comp-Jt With Dynamic Cell Selection, Global Precoding Matrix And Irc Receiver For Lte-A

Ahmed¹,

Sourour²,

M.Elkamchouchi³

2015

IJWMN

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Coordinated multi-point transmission and reception (CoMP) is introduced in LTE- that jointly transmit the desired signal from the available ones (we assumed 3). The user also selects the closed loop precoding matrices for MIMO in a joint fashion to fit the independent MIMO channels from two base stations (eNBs). In addition, edge users are likely to be subject to severe Cochannel interference from eNBs outside the joint transmission set.To address co-channel interference from the base station(s) that are not included in CoMP joint transmission set, the user equipment employs Minimum Mean Squared Error receiver with Interference Rejection Combining (MMSE-IRC). We illustrate the effect of fading correlation between elements of the transmit and receive antennas. Also, the effect of the desired to interference eNB power ratio in case of medium correlation for 3 and 4 layers using MMSE-

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Downlink MIMO in LTE-advanced: SU-MIMO vs. MU-MIMO

Cited by 248 publications

References 3 publications

On the Area Energy Efficiency of Multiple Transmit Antenna Small Base Stations

On the Area Energy Efficiency of Multiple Transmit Antenna Small Base Stations

Joint Fronthaul Link Selection and Transmit Precoding for Energy Efficiency Maximization of Multiuser MIMO-Aided Distributed Antenna Systems

Comp-Jt With Dynamic Cell Selection, Global Precoding Matrix And Irc Receiver For Lte-A

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