Transmit Antenna Subset Selection for Downlink MIMO Systems in Multicell Environments

Kang, Jee Woong; Je, Hui Won; Park, Chang Soon; Lee, Kwang Bok

doi:10.1109/twc.2010.07.090442

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…According to the upper bound of network energy efficiency in (18), NEE upper is only related to the large scale CSI between BS and its serving users, the number of transmit antennas, and transmit power. Using (18) to implement antenna selection can effectively reduce the signaling overhead caused by the complete CSI sharing among different BSs. (9) into the maximization problem of the upper bound NEE upper of network energy efficiency.…”

Section: Transmit and Receive Antenna Selection Scheme Based On Maximmentioning

confidence: 99%

“…However, these schemes mentioned previously are based on single BS scenario without considering the influence of interference from neighbor BSs. Reference [18] uses statistical information of neighbor BSs on transmit antenna set selection to avoid vast signaling exchanges among neighbor BSs. In [19], a coordinated beamforming method is given for downlink multicell MIMO network.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy Efficiency Maximization through Cooperative Transmit and Receive Antenna Selection for Multicell MU-MIMO System

Dong

Zhang

Wang

et al. 2013

International Journal of Antennas and Propagation

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The capacity of Multiple Input Multiple Output (MIMO) system is highly related to the number of active antennas. But as the active antenna number increases, the MIMO system will consume more energy. To maximize the energy efficiency of MIMO system, we propose an antenna selection scheme which can maximize the energy efficiency of BS cluster. In the scheme, ergodic energy efficiency is derived according to large scale channel state information (CSI). Based on this ergodic energy efficiency, we introduce a cost function varied with the number of antennas, in which the effect to the energy efficiency of both the serving BS and the neighbor BS is considered. With this function, we can transform the whole system optimization problem to a sectional optimization problem and obtain a suboptimal antenna set using a heuristic algorithm. Simulation results verify that the proposed approach performs better than the comparison schemes in terms of network energy efficiency and achieves 98% network energy efficiency of the centralized antenna selection scheme. Besides, since the proposed scheme does not need the complete CSI of the neighbor BS, it can effectively reduce the signaling overhead.

show abstract

Section: Transmit and Receive Antenna Selection Scheme Based On Maximmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy Efficiency Maximization through Cooperative Transmit and Receive Antenna Selection for Multicell MU-MIMO System

Dong

Zhang

Wang

et al. 2013

International Journal of Antennas and Propagation

View full text Add to dashboard Cite

show abstract

“…However, the price to pay for large-scale MIMO is increased radio frequency (RF) hardware complexities. Antenna selection is an elegant solution to such problems [14] (see [15][16][17][18][19][20] and the references therein). In particular, a fast MIMO antenna subset based on the QR decomposition of channel matrix is proposed in [21].…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity Transmit Antenna Selection and Beamforming for Large-Scale MIMO Communications

Qian

Wang

Shao

2014

International Journal of Antennas and Propagation

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Transmit antenna selection plays an important role in large-scale multiple-input multiple-output (MIMO) communications, but optimal large-scale MIMO antenna selection is a technical challenge. Exhaustive search is often employed in antenna selection, but it cannot be efficiently implemented in large-scale MIMO communication systems due to its prohibitive high computation complexity. This paper proposes a low-complexity interactive multiple-parameter optimization method for joint transmit antenna selection and beamforming in large-scale MIMO communication systems. The objective is to jointly maximize the channel outrage capacity and signal-to-noise (SNR) performance and minimize the mean square error in transmit antenna selection and minimum variance distortionless response (MVDR) beamforming without exhaustive search. The effectiveness of all the proposed methods is verified by extensive simulation results. It is shown that the required antenna selection processing time of the proposed method does not increase along with the increase of selected antennas, but the computation complexity of conventional exhaustive search method will significantly increase when large-scale antennas are employed in the system. This is particularly useful in antenna selection for large-scale MIMO communication systems.

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Low-complexity transceiver design and antenna subset selection for cooperative half- and full-duplex relaying systems

Zhang

2014

2014 IEEE Global Communications Conference

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Transmit Antenna Subset Selection for Downlink MIMO Systems in Multicell Environments

Cited by 5 publications

References 10 publications

Energy Efficiency Maximization through Cooperative Transmit and Receive Antenna Selection for Multicell MU-MIMO System

Energy Efficiency Maximization through Cooperative Transmit and Receive Antenna Selection for Multicell MU-MIMO System

Low-Complexity Transmit Antenna Selection and Beamforming for Large-Scale MIMO Communications

Low-complexity transceiver design and antenna subset selection for cooperative half- and full-duplex relaying systems

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